Reviewerly/exHarmony huggingface dataset:
A Hybrid Method for Dealing With DVL Faults of SINS/DVL Integrated Navigation System

Aiming at the situation that the underwater integrated navigation system is easy to be disturbed and the output is unavailable during the task of underwater vehicle, a RVBAKF/ LSTM hybrid method is proposed in this paper. Considering the actual underwater working environment of autonomous underwater vehicles (AUV), the measured value of Doppler velocity log (DVL) is prone to outliers or even interruption, so it is very important to improve the fault tolerance of integrated navigation system. For the processing of outliers, a robust adaptive filter based on VB theory is proposed. Its advantage is to realize the adaptation of time-varying noise and reduce the interference of outliers to the integrated navigation system. Firstly, selecting the SINS output related to AUV velocity, such as velocity, attitude, angular velocity and specific force as the training input when the DVL output is normal. When DVL output is interrupted, the long short-term memory (LSTM) model is used to provide pseudo measurement values for integrated navigation system. The effectiveness of the method is verified by the on-board experimental data. When the DVL output is interrupted, the method can effectively improve the accuracy of the integrated navigation system. The method in this paper is compared with four different hybrid methods, and the results are obviously better than the four methods.
The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems

Recommendations are meant to increase sales or ad revenue, as these are the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimizes for lucrative preferences and thus co-produces the preferences they mine. This relates to the well-known problems of feedback loops, filter bubbles, and echo chambers. In this article, I discuss the implications of the fact that computing systems necessarily work with proxies when inferring recommendations and raise a number of questions about whether recommender systems actually do what they are claimed to do, while also analysing the often-perverse economic incentive structures that have a major impact on relevant design decisions. Finally, I will explain how the choice architectures for data controllers and providers of AI systems as foreseen in the EU's General Data Protection Regulation (GDPR), the proposed EU Digital Services Act (DSA) and the proposed EU AI Act will help to break through various vicious circles, by constraining how people may be targeted (GDPR, DSA) and by requiring documented evidence of the robustness, resilience, reliability, and the responsible design and deployment of high-risk recommender systems (AI Act).
Trajectory Planning for UAVs Based on Interfered Fluid Dynamical System and Bézier Curves

In this paper, a 3D trajectory planner for Unmanned Aerial Vehicles (UAVs) based on Interfered Fluid Dynamical System (IFDS) and Bézier curves is introduced. The proposed strategy joints the potentialities of IFDS with the use of Bézier curves to obtain optimized trajectories with continuous curvature. While IFDS computes an initial trajectory to safely avoid fixed and dynamic obstacles, Bézier curve optimization generates a trajectory satisfying kinematic constraints. This combination is computationally efficient for online applications with limited hardware and a smoothed path is obtained, for safe and flyable trajectories. Simulations are performed for a fixed-wing UAV in a complex and dynamic environment.
Are blockchain-based digital transformation and ecosystem-based business models mutually reinforcing? The principal-agent conflict perspective

This paper explores the implications of digitalization and business model innovation for the principal-agent conflict. Continuous digital transformation has recently become a feature sine qua non for companies. It is also stimulating business model innovation resulting in the growing adoption of ecosystem-based models. These trends may have significant implications for the principal-agent relationship, essential for understanding value creation by a firm. In order to analyze the impact of digital transformation, we use blockchain technology as a proxy. To measure the potential impact on the principal-agent conflict, we study management and shareholder-sponsored proposals at annual meetings. The level of shareholder involvement in governance is measured based on the number of shareholder-sponsored proposals received. In addition, we measure shareholder support for management-sponsored proposals. A sample of 2481 NYSE, Nasdaq and AMEX-traded firms for the period 2015–2019 is used. First of all, we show that digitalization per se has a mitigating impact on the agency conflict. Shareholders become more active, albeit not more hostile towards management. Secondly, we have identified the strongest impact in such sectors as information technology, communications, finance, and healthcare. These are the most significantly impacted by ecosystem-based business model innovation. We conclude that digitalization and ecosystem-based business models are mutually reinforcing in mitigating the principal-agent conflict.
An improved bilevel optimization approach for image super-resolution based on a fractional diffusion tensor

Variational regularization techniques are widely used to improve the quality of the super-resolved image. However, the success of these methods depends on some sensitive parameters which have to be manually tuned. Recently, an improved strategy to avoid this illness is to learn these parameters from some available data. In this work we propose an improved super-resolution method with bilevel optimization technique to learn the spatially dependent parameter λ that controls the diffusivity of the fractional tensor of the Partial Differential Equation (PDE) to remove both Gaussian and speckle noises. Also, we elaborate a mathematical well-posedness of the optimal control problem and we introduce a projected quasi-Newton algorithm to compute the HR image X and the associated parameter λ. The numerical experiments cope with our theoretical part and show a significant improvement of the restored HR image compared to some state of art super-resolution.
Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition

With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user’s ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user’s privacy. To achieve a high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system preforms action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.
Scam Recognition in Visa/Credit Card Using Genetic Algorithm

In recent years, individuals have been concerned about the extensive and pervasive use of data mining for fraud detection in credit card models based on data mining. Because our challenge resembles a classification problem, traditional data mining strategies are inapplicable. As a result, metaheuristic methodologies such as genetic algorithms are used to create an alternate strategy. The genetic algorithm is utilized in this examination to offer a charge card misrepresentation discovery framework. Genetic algorithm is a sort of calculation that intends to improve at taking care of issues over the long haul. These are algorithms that evolve over time. When a card is cloned, stolen, or lost and seized, it signifies that fraudster have done something odd with it, and it is generally utilized until the obtainable bound is exhausted. As a result, somewhat than focusing on quantity of properly categorized communications, strategy that reduces overall allowed limit on fraud-prone cards takes precedence. Using a genetic algorithm, it seeks to reduce false alarms. So, fraud detection is the process of identifying false alerts in data and reporting the outcomes over time.
An automated face mask detection using machine learning techniques

The finish of 2019 saw the event of corona virus. It has continued to cause havoc for many people and organizations well into 2022. As the globe recovers from the epidemic and prepares to resume normal operations, a wave of pressure is felt by all individuals, especially those that will continue taking face to face. Studies have demonstrated that conveying a cover significantly decreases the shot at irresistible specialist transmission still as gives a method of assurance. To relieve this drawback, this undertaking and innovation hold the key here for goal this issue. It presents a Deep Learning based generally framework that will observe occasions at any place facial coverings aren't utilized as expected. The face mask helps us from effecting various diseases to us. We all know the importance of face masks in these recent times due to this pandemic situation. The rapid decrease of covid cases leads to the ignorance of face masks due to that many of the people are affected again and the second wave of virus spread among people. People started taking precautions and again cases decreased, then now also people started ignoring the face masks. There are a lot of basic gears that will be needed to fight the Corona infection. Face Mask is one of the most fundamental. Initially, everyone was not compelled to wear a facial covering, but as time has gone, researchers and doctors have suggested that everyone does so. In the general public presently, which can be utilized for different applications like at the air terminal, medical clinics, workplaces, schools, and so on. This framework can be critical at air terminals to identify voyagers whether or not they are wearing a mask. We are creating a system that will detect masks. Due to the ignorance of masks and taking precautions, again the third wave is going to start. Now in recent times, a new variant of the covid virus came out called “Omicron”. This system can detect whether someone is wearing a mask or not, and it may be deployed anyplace, such as a CCTV or security system. The recent research helps us to understand how the working of previous face mask detection systems. We see there is a lot of defects, which we are going to solve in our face mask detection system.
Improving differential evolution using a best discarded vector selection strategy

• Discarded trial vectors were used to improve differential evolution performance. • A best discarded vector selection strategy was proposed. • The best discarded vector selection strategy can be applied to various differential evolution algorithms. In this study, a selection strategy based on discarded trial vectors was constructed to improve the performance of the differential evolution (DE) algorithm. The proposed strategy is called the best discarded vector selection (BDVS) strategy. The uniqueness of BDVS is that the best discarded trial vector for an individual and the best vector for the individual are used to improve the individual. First, an external archive is constructed to record the best discarded trial vectors for each individual. During the survivor selection operation, if a trial vector is worse than the target vector and the target vector is in a stagnant state, then the target vector is replaced with the best vector of the target vector or a vector chosen at random from the external archive. BDVS is a general framework that can easily be integrated into various DE algorithms. In our experiments, BDVS was applied to eight DE algorithms, which were then compared to their original algorithms. BDVS was verified using the 2013 IEEE Congress on Evolutionary Computation (CEC 2013), CEC 2014, and CEC 2017 benchmark test sets and 88 test functions were considered. The experimental results demonstrate that BDVS can significantly improve DE performance.
Extreme Spin Squeezing via Optimized One-Axis Twisting and Rotations

We propose a scheme for the generation of optimal squeezed states for Ramsey interferometry. The scheme consists of an alternating series of one-axis twisting pulses and rotations, both of which are straightforward to implement experimentally. The resulting states show a metrological gain proportional to the Heisenberg limit. We demonstrate that the Heisenberg scaling is maintained even when placing constraints on the amplitude of the pulses implementing the one-axis twisting and when taking into account realistic losses due to photon scattering.
A hybrid machine learning model for timely prediction of breast cancer

Breast cancer is one of the leading causes of untimely deaths among women in various countries across the world. This can be attributed to many factors including late detection which often increase its severity. Thus, detecting the disease early would help mitigate its mortality rate and other risks associated with it. This study developed a hybrid machine learning model for timely prediction of breast cancer to help combat the disease. The dataset from Kaggle was adopted to predict the breast tumor growth and sizes using random tree classification, logistic regression, XBoost tree and multilayer perceptron on the dataset. The implementation of these machine learning algorithms and visualization of the results was done using Python. The results achieved a high accuracy (99.65%) on training and testing datasets which is far better than traditional means. The predictive model has good potential to enhance early detection and diagnosis of breast cancer and improvement of treatment outcome. It could also assist patients to timely deal with their condition or life patterns to support their recovery or survival.
Adapting Software Architectures to Machine Learning Challenges

Unique developmental and operational characteristics of machine learning (ML) components as well as their inherent uncertainty demand robust engineering principles are used to ensure their quality. We aim to determine how software systems can be (re-) architected to enable robust integration of ML components. Towards this goal, we conducted a mixed-methods empirical study consisting of (i) a systematic literature review to identify the challenges and their solutions in software architecture for ML, (ii) semi-structured interviews with practitioners to qualitatively complement the initial findings and (iii) a survey to quantitatively validate the challenges and their solutions. We compiled and validated twenty challenges and solutions for (re-) architecting systems with ML components. Our results indicate, for example, that traditional software architecture challenges (e.g., component coupling) also play an important role when using ML components; along with new ML specific challenges (e.g., the need for continuous retraining). Moreover, the results indicate that ML heightened decision drivers, such as privacy, play a marginal role compared to traditional decision drivers, such as scalability. Using the survey we were able to establish a link between architectural solutions and software quality attributes, which enabled us to provide twenty architectural tactics used to satisfy individual quality requirements of systems with ML components. Altogether, the results of the study can be interpreted as an empirical framework that supports the process of (re-) architecting software systems with ML components.
Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference

In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive filtering framework. We utilize the variational Bayes inference method to solve the association problem and to approximate the intractable true posterior. The performance of the proposed solution is demonstrated in simulations and real-data experiments. The results show that our method outperforms the state-of-the-art methods in terms of accuracy with lower computational complexity.
Interpretable machine learning

research-article Open Access Share on Interpretable machine learning: moving from mythos to diagnostics Authors: Valerie Chen Carnegie Mellon University, Pittsburgh, PA Carnegie Mellon University, Pittsburgh, PAView Profile , Jeffrey Li University of Washington, Seattle, WA University of Washington, Seattle, WAView Profile , Joon Sik Kim Carnegie Mellon University Carnegie Mellon UniversityView Profile , Gregory Plumb Carnegie Mellon University, Pittsburgh, PA Carnegie Mellon University, Pittsburgh, PAView Profile , Ameet Talwalkar Carnegie Mellon University, Pittsburgh, PA Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 8August 2022 pp 43–50https://doi.org/10.1145/3546036Published:21 July 2022Publication History 0citation8,320DownloadsMetricsTotal Citations0Total Downloads8,320Last 12 Months8,320Last 6 weeks544 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Factor Model for Online Education during the COVID-19 Pandemic Using the IoT

Coronavirus disease (COVID-19) has spread quickly around the globe. COVID-19 has affected the education sector due to partial or complete lockdowns that were implemented throughout the world between 2019 and 2022. This pandemic severely affected the education sectors in developing countries such as Pakistan. All the educational institutions in Pakistan turned to online education. However, the education sector lacked the teaching experts, digital experts, the Internet of Things (IoT), and resources needed for online education. The shift from traditional to online education has created many challenges for developing countries during a pandemic such as COVID-19, for example, access to the IoT. This paper aims to introduce the factor model (F model), which will provide guidelines for the government and universities for minimizing the deficiencies related to online education. The F-model will identify all the factors that affect the performance and guide the user about their importance. This will allow the user to resolve that issue and improve the performance of their department or institution. Thus, the F model will benefit the education sector by mitigating the challenges related to online education. The F model is not only confined to online education but can be operated in the fields of science and industry for data extraction and the calculation of results. First, the data is collected physically and online through a student survey related to the challenges of online education during a pandemic. The data extraction and the calculation of the results are carried out using the F model. The results of the survey are alarming and the government has a lot of work to do to improve online education using the IoT. According to the F model, the government should take serious action to improve the performance of students, teachers, and all education sectors not only during the COVID-19 pandemic but also for possible future pandemics.
An SAR Ship Object Detection Algorithm Based on Feature Information Efficient Representation Network

In the synthetic aperture radar (SAR) ship image, the target size is small and dense, the background is complex and changeable, the ship target is difficult to distinguish from the surrounding background, and there are many ship-like targets in the image. This makes it difficult for deep-learning-based target detection algorithms to obtain effective feature information, resulting in missed and false detection. The effective expression of the feature information of the target to be detected is the key to the target detection algorithm. How to improve the clear expression of image feature information in the network has always been a difficult point. Aiming at the above problems, this paper proposes a new target detection algorithm, the feature information efficient representation network (FIERNet). The algorithm can extract better feature details, enhance network feature fusion and information expression, and improve model detection capabilities. First, the convolution transformer feature extraction (CTFE) module is proposed, and a convolution transformer feature extraction network (CTFENet) is built with this module as a feature extraction block. The network enables the model to obtain more accurate and comprehensive feature information, weakens the interference of invalid information, and improves the overall performance of the network. Second, a new effective feature information fusion (EFIF) module is proposed to enhance the transfer and fusion of the main information of feature maps. Finally, a new frame-decoding formula is proposed to further improve the coincidence between the predicted frame and the target frame and obtain more accurate picture information. Experiments show that the method achieves 94.14% and 92.01% mean precision (mAP) on SSDD and SAR-ship datasets, and it works well on large-scale SAR ship images. In addition, FIERNet greatly reduces the occurrence of missed detection and false detection in SAR ship detection. Compared to other state-of-the-art object detection algorithms, FIERNet outperforms them on various performance metrics on SAR images.
Temporal super-resolution of echocardiography using a novel high-precision non-polynomial interpolation

Higher frame rates are very useful for improving medical diagnosis in fast-moving parts of the heart, especially in the valves. To this end, we propose a non-polynomial interpolation method for increasing the frame rate in echocardiography. Besides describing the proposed method, we present two additional contributions: (1) we obtain a closed-form solution, which is continuous and infinitely differentiable; (2) we provide an error analysis of the method. The resulting error bound indicates that the interpolation method is reliable. Finally, to show the efficiency of our proposal in temporal super-resolution, i.e., the increase in frame rate, we apply it to three types of datasets, including a 1D signal, a simulated dataset, and B-mode echocardiography images. Our experimental results show that the Mean Squared Error of the proposed method is reduced from 0.6 to 0.3, while having the same computational complexity compared to cubic B-spline. The quantitative results also indicate that, even with lower selection rates, we can reach a high performance reconstruction while the image quality is not degraded significantly.