Reviewerly/exHarmony huggingface dataset:
A Video-Based Method With Strong-Robustness for Vehicle Detection and Classification Based on Static Appearance Features and Motion Features

Vehicle detection and classification plays an important role in intelligent transportation system.Compared with traditional detectors, the detection and classification based on traffic surveillance video shows a huge advantage in its flexibility and continuity.However, to get wide applicability and strong robustness, most current methods focus on improving the accuracy of detectors by adjusting network parameters constantly, or increasing the size of training sets, which challenges the collection and labeling of data, the performance of computers, the scope of application and so on.Moreover, the unique continuity characteristic of the video, which can be used to describe the motion features of vehicle, is often ignored.Take these facts into account, this paper proposed a video-based vehicle detection and classification method, which is based on static appearance features and motion features both.Four detectors of different performance were trained with small training sets, and the designed algorithms for the remove, selection and reorganization of detected objects contribute to obtaining the optimal results of detection and classification.The experiment results show that the proposed method is able to detect and classify vehicles with more than 0.95 accuracy dealing with different road environments.
Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit

Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.
Coherent $H^{\infty}$ Control for Linear Quantum Systems With Uncertainties in the Interaction Hamiltonian

This work conducts robust H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> analysis for a class of quantum systems subject to perturbations in the interaction Hamiltonian. A necessary and sufficient condition for the robustly strict bounded real property of this type of uncertain quantum system is proposed. This paper focuses on the study of coherent robust H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> controller design for quantum systems with uncertainties in the interaction Hamiltonian. The desired controller is connected with the uncertain quantum system through direct and indirect couplings. A necessary and sufficient condition is provided to build a connection between the robust H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> control problem and the scaled H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> control problem. A numerical procedure is provided to obtain coefficients of a coherent controller. An example is presented to illustrate the controller design method.
Unsupervised learning framework for interest point detection and description via properties optimization

This paper presents an unsupervised interest point detection and description method named Properties Optimization Point (POP), which provides a unified objective to optimize different properties of interest point. First, the proposed objective formulates the interest point set as a latent variable, which is flexible to integrate different properties. With the latent variable, the probability formulations are designed for four traditional properties (sparsity, repeatability, invariability, discriminability). Second, a novel property termed as informativeness indicating the information complexity of a local area is designed to determine the areas containing high information, from which interest points are encouraged to be extracted. Third, an efficient approximate Expectation Maximization is proposed to optimize the non-differentiable objective which integrates the above five properties. Finally, POP is instantiated with fully convolutional networks. Experimental results demonstrate that POP outperforms state-of-the-art methods on a number of image matching benchmarks containing both planar and non-planar scenes.
RETRACTED: Film and television culture dissemination based on ZYNQ embedded digital image processing

The difference is between the various countries and countries huge culture provides an extended material and heaps of inspiration for a spread of creative creations, has become with the presence of various regions of the planet's culture. At constant time, that square measure inspired painting, music, movies, and conjointly inheritance and disseminated several works of art like a Television (TV) based on proposed Artificial Neural Network (ANN). The interaction with the art of native culture and film and Tv, film and tv art, completely different geographical analysis organization from the purpose of reading is to push understanding and cultural Communication, digital image and culture Zynq via the Communication of making the active role the plays return currently natural landscape, a special depiction of the characters within the region, localized application of the language and the special skills inheritance, film and tv, of native culture and film and tv Arts expect to grasp each the digital image of the two-way interaction between is unconcealed. The most important reason for the technical, innovative development of film and TV arts mislead the thought. The latter of inheritance and cultural modeling, whereas providing endless material and inspiration for making film and TV in each time and house, the previous relies on science and technology. Regional variations are because of the diversification of film and TV Arts. Voice, image, S, and at a similar time the mixture of the actual graphics, pleasures audiovisual, the sector of film and tv, shows a distinct region, race, and alternative native parts to the audience using Field Programmable Gate Array (FPGA) tool. The next section provides a close description and samples of the various regions of the digital and cultural dissemination and television series of the Zynq image of the film.
High-Speed RLWE-Oriented Polynomial Multiplier Utilizing Karatsuba Algorithm

Lattice-based cryptography (LBC) is one of the promising post-quantum candidates which offers good security and performance. The most time consuming operations in LBC is the polynomial multiplication, which can be performed through widely explored algorithms like schoolbook polynomial multiplication algorithm (SPMA) and Number Theoretic Transform (NTT). However, Karatsuba algorithm with better complexity compared to SPMA, is not widely studied for FPGA implementation of LBC. In this brief, we proposed an optimized SPMA-Karatsuba (SK) architecture with novel technique to implement the negacyclic convolution. The proposed architecture is more than 2.09× faster in expense of 96.06% additional hardware resources compared to the state-of-the-art SPMA architecture. This shows that the combination of SPMA and Karatsuba algorithm can produce hardware architecture with higher speed yet maintain balanced area-time efficiency compared to SPMA-only architecture. This is especially useful for developing IoT edge nodes or gateway devices that require high speed but able to tolerate some additional hardware area.
Comparison of mutton freshness grade discrimination based on hyperspectral imaging, near infrared spectroscopy and their fusion information

Abstract The discrimination models of mutton freshness grades based on hyperspectral imaging (HSI, 400–1,000 nm), near infrared spectroscopy (NIRS, 900–2,500 nm) and their fusion information were established and compared in this study. Mutton freshness was divided into three grades by the comprehensive evaluation criterion. Then the characteristic variables were screened from preprocessed full‐band variables, and discriminant models were established and compared based on the full‐band variables, the characteristic variables and fusion information. The results showed that both full‐band models of HSI and NIRS showed good performance, and the accuracies of all datasets were higher than 92.6%. The model accuracy decreased gradually with the decrease of the variables number when modeling with characteristic variables. When modeling with fusion information, the extreme learning machine models had better performance, and the accuracies of all datasets were over 92.7%. Comprehensively considering the accuracy, the cost, the efficiency of data acquisition and analysis, the fusion of HSI and NIRS had no significant effect compared to the single sensor with full‐band variables, and NIRS was possibly more economical and practical than HSI. This study provides reference and theoretical basis for selection of sensors in future studies and development of simplified, low‐cost detection devices for mutton freshness grade. Practical Applications Based on the full‐band variables, the characteristic variables, and fusion information of HSI (400–1,000 nm) and NIRS (900–2,500 nm), the discrimination models of mutton freshness grades were established and compared. The characteristic variables were screened by the methods of CARS, GA, SPA and their synergic methods. We could come to a conclusion that both full‐band models of HSI and NIRS showed good results. Comprehensively considering the accuracy, the cost, the efficiency of data acquisition and analysis, the fusion of HSI and NIRS had no significant effect compared to the single sensor based on the full‐band variables, and NIRS was possibly more economical and practical than HSI. This study provides reference and theoretical basis for selection of sensors in future studies and development of simplified, low‐cost detection devices for mutton freshness grade.
CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence

Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images. Recently, researchers design various learning-based algorithms to undertake the co-segmentation task. The main difficulty in this task is how to effectively transfer information between images to make conditional predictions. In this paper, we present CycleSegNet, a novel framework for the co-segmentation task. Our network design has two key components: a region correspondence module which is the basic operation for exchanging information between local image regions, and a cycle refinement module, which utilizes ConvLSTMs to progressively update image representations and exchange information in a cycle and iterative manner. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on four popular benchmark datasets -- PASCAL VOC dataset, MSRC dataset, Internet dataset, and iCoseg dataset, by 2.6%, 7.7%, 2.2%, and 2.9%, respectively.
Study of Prevention of Mode Collapse in Generative Adversarial Network (GAN)

With the advancement of Deep Neural Network and its increased applications, requirement of data has increased exponentially. To fulfil this requirement Deep generative models specifically Generative Adversarial Networks (GANs) have emerged as a very powerful tool. However, tuning GAN parameters are extremely difficult due to its instability and it is very prone to miss modes while training, which is termed as Mode Collapse. Mode collapse leads generators to generate the images of a particular mode while ignoring the other mode classes. In the present research, we propose a novel method to deal with Mode Collapse by using multiple generator architecture. Initially, We have shown the comparison of different GAN architectures which deals with the Mode collapse problem. We use Inception score (IS) as a evaluation metric to evaluate the performance of GAN. We started analysing GAN on a simple dataset (MNIST) using DCGAN architecture. To produce better results, the present work describes the implementation of two other different approaches. We have experimented on Wasserstein GAN (WGAN) which improves GANs training by adopting a different metric which is Wasserstein distance for calculating the distance between two probability distributions. Subsequently we have proposed the approach of multiple generator GAN architecture which uses multiple generators to provide a better solution to the missing modes problem. We evaluate our approach on several datasets (MNIST, CIFAR-10, SVHN, CelebA Face dataset) with encouraging results compared to the other existing architectures.
Holistic Filter Pruning for Efficient Deep Neural Networks

Deep neural networks (DNNs) are usually over-parameterized to increase the likelihood of getting adequate initial weights by random initialization. Consequently, trained DNNs have many redundancies which can be pruned from the model to reduce complexity and improve the ability to generalize. Structural sparsity, as achieved by filter pruning, directly reduces the tensor sizes of weights and activations and is thus particularly effective for reducing complexity. We propose Holistic Filter Pruning (HFP), a novel approach for common DNN training that is easy to implement and enables to specify accurate pruning rates for the number of both parameters and multiplications. After each forward pass, the current model size is calculated and compared to the desired target size. By gradient descent, a global solution can be found that allocates the pruning budget over the individual layers such that the desired target size is fulfilled. In various experiments, we give insights into the training and achieve state-of-the-art performance on CIFAR-10 and ImageNet.
Ensemble Effort Estimation using dynamic selection

The Software Effort Estimation (SEE) process has been approached in different ways in the literature, including models built from Machine Learning (ML). The combination of these models (Ensemble) is an important research topic in ML, and has lead to improvements in accuracy compared to individual models. This paper proposes heterogeneous and dynamic ensemble selection (DES) models, composed by a set of regressors dynamically selected by classifiers to estimate software development effort. In the training phase, a pool of regression algorithms is trained using training data and a validation data set to validate the models. Next, some classifiers are trained to identify the best regression model from the pool for each training instance. In the test phase each trained classifier is used to dynamically select a regressor model from the pool for predicting the effort for each test instance. The final prediction is given by the combination of the predictions of the regressors selected by the classifiers. An experimental analysis considering a relevant set of software effort estimation problems is reported. The experiments demonstrate that the proposed method outperforms individual regressors and some state of the art models of the literature.
Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction

Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.
Regularly updated deterministic policy gradient algorithm

Deep Deterministic Policy Gradient (DDPG) algorithm is one of the most well-known reinforcement learning methods. However, this method is inefficient and unstable in practical applications. On the other hand, the bias and variance of the Q estimation in the target function are sometimes difficult to control. This paper proposes a Regularly Updated Deterministic (RUD) policy gradient algorithm for these problems. This paper theoretically proves that the learning procedure with RUD can make better use of new data in replay buffer than the traditional procedure. In addition, the low variance of the Q value in RUD is more suitable for the current Clipped Double Q-learning strategy. This paper has designed a comparison experiment against previous methods, an ablation experiment with the original DDPG, and other analytical experiments in Mujoco environments. The experimental results demonstrate the effectiveness and superiority of RUD.
Adaptive Convolution for Semantic Role Labeling

Semantic role labeling (SRL) aims at elaborating the meaning of a sentence by forming a predicate-argument structure. Recent researches depicted that the effective use of syntax can improve SRL performance. However, syntax is a complicated linguistic clue and is hard to be effectively applied in a downstream task like SRL. This work effectively encodes syntax using adaptive convolution which endows strong flexibility to existing convolutional networks. The existing CNNs may help in encoding a complicated structure like syntax for SRL, but it still has shortcomings. Contrary to traditional convolutional networks that use same filters for different inputs, adaptive convolution uses adaptively generated filters conditioned on syntactically-informed inputs. We achieve this with the integration of a filter generation network which generates the input specific filters. This helps the model to focus on important syntactic features present inside the input, thus enlarging the gap between syntax-aware and syntax-agnostic SRL systems. We further study a hashing technique to compress the size of the filter generation network for SRL in terms of trainable parameters. Experiments on CoNLL-2009 dataset confirm that the proposed model substantially outperforms most previous SRL systems for both English and Chinese languages.
Speech signal processing on graphs: The graph frequency analysis and an improved graph Wiener filtering method

In the paper, we investigate a graph representation of speech signals and graph speech enhancement technology. Specifically, we first propose a new graph k-shift operator Ck to map speech signals into the graph domain and construct a novel graph Fourier basis by using its singular eigenvectors for speech graph signals (SGSs). On this basis, we propose an improved graph Wiener filtering method based on the minimum mean square error (MMSE) criterion to suppress the noise interference in noisy speech. Comparing with the traditional methods in DSP and the existed graph Wiener filtering methods by applying graph shift operators in GSP, our numerical simulation results show that the performance of the proposed method outperforms that of these methods in terms of both average SSNR and mean PESQ score. Moreover, the computational complexity of the proposed method is much lower than that of the existed graph Wiener filtering methods and a little higher than that of classical methods in DSP.
HighRes-MVSNet: A Fast Multi-View Stereo Network for Dense 3D Reconstruction From High-Resolution Images

We propose an end-to-end deep learning architecture for 3D reconstruction from high-resolution images. While many approaches focus on improving reconstruction quality alone, we primarily focus on decreasing memory requirements in order to exploit the abundant information provided by modern high-resolution cameras. Towards this end, we present HighRes-MVSNet, a convolutional neural network with a pyramid encoder-decoder structure searching for depth correspondences incrementally over a coarse-to-fine hierarchy. The first stage of our network encodes the image features to a much smaller resolution in order to significantly reduce the memory requirements. Additionally, we limit the depth search range in every hierarchy level to the vicinity of the previous prediction. In this manner, we are able to produce highly accurate 3D models while only using a fraction of the GPU memory and runtime of previous methods. Although our method is aimed at much higher resolution images, we are still able to produce state-of-the-art results on the Tanks and Temples benchmark and achieve outstanding scores on the DTU benchmark.
Class-agnostic Object Detection

Object detection models perform well at localizing and classifying objects that they are shown during training. However, due to the difficulty and cost associated with creating and annotating detection datasets, trained models detect a limited number of object types with unknown objects treated as background content. This hinders the adoption of conventional detectors in real-world applications like large-scale object matching, visual grounding, visual relation prediction, obstacle detection (where it is more important to determine the presence and location of objects than to find specific types), etc. We propose class-agnostic object detection as a new problem that focuses on detecting objects irrespective of their object-classes. Specifically, the goal is to predict bounding boxes for all objects in an image but not their object-classes. The predicted boxes can then be consumed by another system to perform application-specific classification, retrieval, etc. We propose training and eval uation protocols for benchmarking class-agnostic detectors to advance future research in this domain. Finally, we propose (1) baseline methods and (2) a new adversarial learning framework for class-agnostic detection that forces the model to exclude class-specific information from features used for predictions. Experimental results show that adversarial learning improves class-agnostic detection efficacy.