Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Speed Estimation along with Vehicle Detection Counting

Vehicle counting, tracking, and speed estimation play an important role in ensuring better traffic management. It also helps in reducing road accidents. Therefore, the research work in this paper presents the most accurate way to detect a vehicle's speed, track it, and estimate its traffic count. In this work, the concept of dual axis and triple axis reference lines is used to estimate the speed of the vehicle. Moreover, the proposed method increases the accuracy of the estimation speed by 94.4%. Further, a line of interest counting algorithm is employed in this system to detect the count of traffic vehicles and improve the traffic management system. In addition, the background subtraction method is used to track the vehicles. Finally, the comparative analysis of the proposed method is carried out with similar methods in this paper.
Spatial and temporal constrained ranked retrieval over videos

Recent advances in Computer Vision (CV) algorithms have improved accuracy and efficiency, making video annotations possible with high accuracy. In this paper, we utilize the annotated data provided by such algorithms and construct graph representations to capture both object labels and spatial-temporal relationships of objects in videos. We define the problem of Spatial and Temporal Constrained Ranked Retrieval (STAR Retrieval) over videos. Based on the graph representation, we propose a two-phase approach, consisting of the ingestion phase, where we construct and materialize the Graph Index (GI), and the query phase, where we compute the top ranked windows (video clips) according to the window matching score efficiently. We propose two algorithms to perform Spatial Matching (SMA) and Temporal Matching (TM) separately with an early-stopping mechanism. Our experiments demonstrate the effectiveness of the proposed methods, achieving orders of magnitude speedups on queries with high selectivity.
An Image Pixel Interval Power (IPIP) Method Using Deep Learning Classification Models

The implementation of deep learning (DL) in various fields is becoming common. In addition, demand for higher accuracy models is increasing continuously at the same rates as the growth of other fields of science. Using all DL tools in the development of computer vision (CV) is a fundamental aspect of its future development. Considering all such tools, we conducted research on the effect of data representation on the final classification accuracy and proposed image pixels' double representation (IPDR) and image pixels' multiple representations (IPMR) for skipping certain pixels in the images in a dataset. Because the image pixel values range from 0 to 255, we proposed including all knowledge from different intervals of pixels. With IPDR, we trained the model using a dataset and obtained the prediction probabilities for the classification task. Next, we created two different datasets from an existing dataset. The first dataset took only image pixels of higher than 127, with all other image pixels in the dataset changed to zeros. The second dataset took only image pixels equal to or lower than 127. These two created datasets were trained on the same model architecture and their prediction accuracies for classification were ensembled with the prediction accuracies of the main model. With the IPMR method, we applied the same method as previously described, although instead of two intervals, from 0 to 127, and 127 to 255, we used, multiple intervals of 50 (i.e., [0:50], (50:100], (100:150], (150:200], and (200:255]) for the Cifar10 dataset. The number of intervals depends on the dataset, and applying our method, we achieved 89.46%, 98.90%, and 73, 38% accuracies on the Fashion MNIST, MNIST, and Cifar10 datasets, respectively, whereas their original classification accuracies under classic training were 89.27%, 98.65%, and 71.29%, respectively. As the advantage of using this method, it can be applied to any classification task and gives only extra knowledge on the trained data. As another simplicity of this method, it can be used with other DL ensemble models simultaneously.
Revealing Occlusions with 4D Neural Fields

For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence. We introduce a framework for learning to estimate 4D visual representations from monocular RGB-D video, which is able to persist objects, even once they become obstructed by occlusions. Unlike traditional video representations, we encode point clouds into a continuous representation, which permits the model to attend across the spatiotemporal context to resolve occlusions. On two large video datasets that we release along with this paper, our experiments show that the representation is able to successfully reveal occlusions for several tasks, without any architectural changes. Visualizations show that the attention mechanism automatically learns to follow occluded objects. Since our approach can be trained end-to-end and is easily adaptable, we believe it will be useful for handling occlusions in many video understanding tasks. Data, code, and models are available at occ1usions. cs. co1umbia. edu.
Invertible Neural Networks for Graph Prediction

Graph prediction problems prevail in data analysis and machine learning. The inverse prediction problem, namely to infer input data from given output labels, is of emerging interest in various applications. In this work, we develop invertible graph neural network (iGNN), a deep generative model to tackle the inverse prediction problem on graphs by casting it as a conditional generative task. The proposed model consists of an invertible sub-network that maps one-to-one from data to an intermediate encoded feature, which allows forward prediction by a linear classification sub-network as well as efficient generation from output labels via a parametric mixture model. The invertibility of the encoding sub-network is ensured by a Wasserstein-2 regularization which allows free-form layers in the residual blocks. The model is scalable to large graphs by a factorized parametric mixture model of the encoded feature and is computationally scalable by using GNN layers. The existence of invertible flow mapping is backed by theories of optimal transport and diffusion process, and we prove the expressiveness of graph convolution layers to approximate the theoretical flows of graph data. The proposed iGNN model is experimentally examined on synthetic data, including the example on large graphs, and the empirical advantage is also demonstrated on real-application datasets of solar ramping event data and traffic flow anomaly detection.
Multisensor-Based Continuous Authentication of Smartphone Users With Two-Stage Feature Extraction

The one-time authentication mechanism in traditional authentication methods cannot continuously authenticate smartphone users’ identities throughout the session. Continuous authentication based on the behavioral biometrics recorded by the built-in sensors can solve this issue. However, the existing methods based on multisensor have poor ability to extract valuable features that can represent smartphone users’ behavioral patterns. This article proposes a novel method combining the manual construction and the deep metric learning method to perform two-stage feature extraction, respectively. We transform the time-series raw data from three sensors (accelerometer, gyroscope, and magnetometer) into 69 statistical features in the first stage. Furthermore, unlike the existing serial feature fusion methods, we innovatively fuse the constructed statistical features from three sensors into a three-channel matrix. Then, the fused features matrix with a three-channel is fed to the deep metric learning model for the second stage of feature extraction. We use the elliptic envelope algorithm to classify the user as a legitimate user or an impostor. Finally, we evaluate the performance of the proposed method on two public data sets. Experimental results show that our method can achieve an average accuracy of 99.71% and an average equal error rate (EER) of 0.56% on the hand movement, movement, orientation, and grasp data set, and an average accuracy of 99.59% and an average EER of 0.61% on the BrainRun data set.
Orientation-First Strategy With Angle Attention Module for Rotated Object Detection in Remote Sensing Images

Recently, object detection in remote sensing images (RSIs) have received extensive attention and made significant progress. Nonetheless, the arbitrary orientations of objects in RSIs make their detection a challenging task. Most of the existing detection methods are difficult to extract the orientation features of objects due to the lack of directionality of conventional convolutions. In addition, the boundary discontinuity in angle regression affects the detection of object orientations. In response to these problems, this paper proposes an orientation-first refinement detector (OFRDet), which is based on a strategy that enables the detector to detect the angle of an object ahead of others and presets oriented anchors. In OFRDet, we propose an angle encoding regression module (AERM) and an angle channel attention module (ACAM). AERM transforms angle detection into multi-parameter regression, which eliminates boundary discontinuities. ACAM uses convolution kernels with different angles to extract directional features purposefully according to the preset oriented anchors. After these two modules, more accurate bounding boxes are generated and sent to the refined stage to obtain the final detection results. We evaluate our method and demonstrate the effectiveness of it by conducting experiments on two challenging and credible datasets, DOTA, HRSC2016. OFRDet achieves competitive results 79.56%, 96.29% mAP on the two datasets, respectively.
Multi-Client Boolean File Retrieval With Adaptable Authorization Switching for Secure Cloud Search Services

Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs: (i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30 <inline-formula><tex-math notation="LaTeX">$\sim 200\times$</tex-math></inline-formula> smaller public parameter and secret key size, with the assumed least frequent keyword in a query ( <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> -term) as 21. Moreover, it runs 10 <inline-formula><tex-math notation="LaTeX">$\sim 20\times$</tex-math></inline-formula> faster for file encryption and <inline-formula><tex-math notation="LaTeX">$&gt;20\times$</tex-math></inline-formula> faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> -term=1 (resp. =21), as compared to classic dynamic ABKS system.
Research on Inverse Kinematics of Manipulator Based on Neural Network

The inverse kinematics solution of manipulator is an important part of robot dynamics, trajectory planning and trajectory tracking control. This paper presents an inverse kinematics solution method of manipulator in which the RBF network is used. This method overcomes many defects of traditional inverse kinematics solution methods, such as low precision and slow convergence speed, and provides a new solution for the inverse kinematics of manipulator. Experimental results show that the RBF network has compact topological structure, separation and learning of structural parameters, fast convergence speed and global approximation ability, which fundamentally solves the local optimization problem of BP network. The scheme designed in this paper is simple and practical, and the trained parallel neural network has fast convergence speed and high precision, which can meet the requirements of high-speed inverse kinematics in the real-time control of manipulator.
Mortality Prediction of Lung Cancer from CT Images Using Deep Learning Techniques

The leading cause for the death in humans is cancer. One of the most common cancers is lung cancer and deadly cancers, causing significant harm to the human body. Cancer must be detected early in order to be cured. Many lives can be spared if lung cancer is detected early. To achieve better results, a Computerised System deep-learning can be implemented. The main purpose of this research is to develop models employing a variety of Deeping Learning models. Classifier and to forecast the mortality prediction and to identify which the (NSCLC) Non-Small-Cell-lung-Cancer diseases are affected severe and this disease can be treated accordingly. To determine which of the two models is the best. In this research, the Resnet18, Resnet50, Resnet101 are used as architectural model to predict the lung cancer disease Several performance metrices were used to analyse these models, and the result of those performances metrices show that the Convolutional neural network classifier is the best model than support vector machine.
Recommendations in E-Commerce Systems Based on Deep Matrix Factorization

E-commerce systems (including online shopping, entertainment, etc.) play an increasingly important role and have become popular in digital life. These systems have also become one of the cores, and vital issues for many businesses, especially from the recent COVID-19 pandemic, the importance of online e-commerce systems are very necessary. Techniques in recommendation systems are widely used to support users in finding suitable products/items in online systems. This work proposes using deep matrix factorization for recommendation in online e-commerce systems. We provide a detailed architecture of a deep matrix factorization as well as make a comparison with the standard matrix factorization model. Experimental results on ten published data sets show that the deep matrix factorization model can work well for recommendations in online e-commerce systems.
Student-t Mixture GLMB Filter with Heavy-tailed Noises

The generalized labeled multi-Bernoulli (GLMB) filter acts as a prospective solution in multi-target tracking (MTT) applications. Nevertheless, considering the heavy-tailed process together with measurement noises, the emerged noise outliers can seriously deteriorate the tracking performance exhibited by the GLMB filter. In order to solve this challenging issue, the study develops a Student-t mixture GLMB (STM-GLMB) filter, which employs multivariate St models for adapting the heavy-tailed noises (HTNs), deriving the closed-form implementation regarding the GLMB filter for propagating the parameters of the STM models considering the multi-target St distributions. The filter becomes tractable relying on the introduction of approximations. According to simulation results, the STM-GLMB multi-target tracking algorithm is valid and stable in heavy-tailed process and measurement noises.
Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases

As machine learning (ML) systems become increasingly widespread, it is necessary to audit these systems for biases prior to their de-ployment. Recent research has developed algorithms for effectively identifying intersectional bias in the form of interpretable, underper-forming subsets (or slices) of the data. However, these solutions and their insights are limited without a tool for visually understanding and interacting with the results of these algorithms. We propose Visual Auditor, an interactive visualization tool for auditing and summarizing model biases. Visual Auditor assists model validation by providing an interpretable overview of intersectional bias (bias that is present when examining populations defined by multiple features), details about relationships between problematic data slices, and a comparison between underperforming and overper-forming data slices in a model. Our open-source tool runs directly in both computational notebooks and web browsers, making model auditing accessible and easily integrated into current ML development workflows. An observational user study in collaboration with domain experts at Fiddler AI highlights that our tool can help ML practitioners identify and understand model biases.
Using the SOCIO Chatbot for UML Modeling: A Second Family of Experiments on Usability in Academic Settings

After improving the SOCIO chatbot prototype model, we wanted to know how/if its usability has changed. An evidence-based empirical evaluation of the usability of SOCIO V1 (updated version) requires an extensive verification of the experimental results. A family of experiments is a method of verification whereby we can check if the experimental results are reproducible. Through comparison with the updated control tool Creately, we aimed to gain a better understanding of the usability of the collaborative modeling chatbot and how it could be improved based on experimental evidence of changes in terms of efficiency, effectiveness, satisfaction, and quality. A total of 87 students from three countries were recruited. We conducted a family of three experiments to compare the usability of SOCIO V1 and updated Creately in academic settings. Students appeared to be more satisfied with SOCIO V1, and SOCIO V1 scored better on completeness. There were no significant differences between the two tools regarding efficiency and quality. This study provides evidence on how to employ a family of experiments to improve chatbot usability and enrich knowledge on chatbot usability experimentation.
Optimization of Sensor-Placement on Vehicles using Quantum-Classical Hybrid Methods

The paper presents a quantum method to optimize the placement of sensors on the surface of a vehicle. The problem, as posted in the BMW Quantum Computing Challenge 2021, is to arrive at the optimal positions and configurations (type and orientation) of the sensors on the vehicle surface that maximizes coverage of the Region of Interest (RoI), while minimizing the total cost of the selected sensors. The dataset contains approximately 100,000 points in the RoI, with defined measures of criticality (ranging between 0 to 1), distributed over a volume of approximately 40,000 cubic metres around the vehicle. The types of sensors, their coverage parameters and costs are inputs to the problem.The optimization problem is decomposed and solved individually for each side of the vehicle, and the results for the entire vehicle are collated and presented in the paper. The approach considers a quadratic approximation to the maximum set-coverage formulation with the advantage of obtaining the optimal number of sensors as an output from the model itself. The resultant quadratic program is solved using quantum methods, as well as a standard, state-of-the-art Integer Quadratic Program (IQP) solver, respectively. The quadratic approximation consumes qubits that are linear in the number of total configurations and significantly reduces the number of decision variables required in an Ising formulation of the problem.The quantum and classical methods are found to perform on par with each other. A detailed comparison of the results is presented in the paper. Given the combinatorial nature of the optimization problem, the classical approach may become intractable while solving the problem in an integrated manner (instead of solving individually for each side). Under such scenarios, there is a possibility to expect benefits by adopting the proposed strategies with the availability of large-sized quantum computers in the near future.
Secure Distributed Estimation Over Multitask Networks Against Multiple Attacks

In recent years, distributed multitask estimation on networks, where different sensor nodes estimate different parameters, has received increasing attention. This article mainly focuses on the challenge of distributed security estimation in the context of multiple attacks on multitask networks. First, we propose a distributed multitask secure estimation algorithm based on the local outlier factor and intertask correlation in a multitask network. Additionally, a novel distributed time-varying fusion strategy that equalizes the weight assigned to each node depending on the data density is proposed to mitigate further the effect of multiple attacks on the algorithm's performance. Finally, the performance of the proposed algorithm is evaluated in terms of mean and mean square error, as well as simulated experiments against multiple attacks. Theoretical and numerical studies indicate that the proposed approach can efficiently repel multiple attacks.
Classification of Lung Cancer by Using Machine Learning Algorithms

Due to the structure of cancer cells, where the majority of the cells are overlapped with each other, early diagnosis of lung cancer is a difficult challenge, so the cause of lung cancer remains unknown and prevention is difficult, early discovery of lung cancer is the only approach to cure it. The classification of lung cancer is a crucial process, based on signs that appear on patients; cancers could easily be predicted and treated. This paper uses KNN, SVM, Naïve Bayes and narrow neural network (NNN) classifiers. KNN showed 85.87% accuracy. SVM, Naïve Bayes and NNN showed 92.6%, 90.3% and 90% accuracy respectively. Results came after the fact that among the four classifiers SVM was the most accurate and much better to classify our data.
User-Centric Explainability in Healthcare: A Knowledge-Level Perspective of Informed Machine Learning

Explaining increasingly complex machine learning will remain crucial to cope with risks, regulations, responsibilities, and human support in healthcare. However, extant explainable systems mostly provide explanations that mismatch clinical users’ conceptions and fail their expectations to leverage validated and clinically relevant information. A key to more user-centric and satisfying explanations can be seen in combining data-driven and knowledge-based systems, i.e., to utilize prior knowledge jointly with the patterns learned from data. We conduct a structured review of knowledge-informed machine learning in healthcare. In this article, we build on a framework to characterize user knowledge and prior knowledge embodied in explanations. Specifically, we explicate the types and contexts of knowledge to examine the fit between knowledge-informed approaches and users. Our results highlight that knowledge-informed machine learning is a promising paradigm to enrich former data-driven systems, yielding explanations that can increase formal understanding, convey useful medical knowledge, and are more intuitive. Although complying with medical conception, it still needs to be investigated whether knowledge-informed explanations increase medical user acceptance and trust in clinical machine learning-based information systems.