Reviewerly/exHarmony huggingface dataset:
Robust Multi-Sensor Navigation in GNSS Degraded Environments

The success of an Unmanned Aircraft Vehicle for effective operation beyond visual line of sight is linked to the system’s ability to maintain precise positioning during stationary and maneuvering flight under all encountered conditions, such as sensor measurement degradation or unavailability. Therefore, unmanned operations demand a resilient, accurate, and integral navigation system. Sensor fusion increases data redundancy by combining measurements from complementary sensors, such as Global Navigation Satellite Systems GNSS, Visual Inertial Navigation Systems VINS, and Light Detection and Ranging Systems LIDAR. This research aims to propose an enhanced adaptative sensor switching mechanism within a multi-sensor navigation system for improved robustness in GNSS-degraded environments accounting for accuracy through the interaction between different sensor models by means of their probability of failure. The switching is driven by a real-time process that dynamically adapts to sensor loss and degradation environments based solely on each sensor’s adaptative probability of failure and their interaction. This approach is developed based on previous research and is improved to account for the dependency of heuristic determination for sensor uncertainty models. The simulation is achieved with the help of the UAV Toolbox from MATLAB and the positioning signals modeled through ground truth manipulation with existing error models to test and evaluate the proposed methodology. The results presented show an improvement by mitigating the heuristic dependency and Markov chain sensitivity reported in previous research.
Locomotion of multiple vehicles in multi-lane sky corridors: Some experimental results

Safe locomotion of a UAV sharing airspace with others is a very challenging problem. This problem can be solved by serving each UAV sharing airspace with a geofenced lane encapsulated in a mobility corridor. This paper presents a hybrid control strategy for the safe locomotion of UAVs in such corridors. One component of the strategy finds its application in keeping the UAV within the lane geofence, and the other controls the UAV toward its destination. Applications of the proposed controller have been attempted with the experiments done in two stages: a motion capture scenario with indoor localization without wind and field experiments with wind effects. The experimental results included in this paper validate its application in a real-world scenario.
Selection of Tuning Parameters of the Unscented Kalman Filter using Analytical Truth Statistics

Nonlinear state estimation is an important aspect of aerospace sensing and navigation. Due to the inherent nonlinearity of flight mechanics, nonlinear filtering techniques such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are commonly implemented in aerospace applications. One of the issues surrounding the UKF is how to select the various scaling parameters in the filter. While some works discuss these parameters, research is limited in terms of guidance on how to properly select these parameters. This work utilizes truth statistics for nonlinear transformations to investigate the effect of the UKF scaling parameters on mean and covariance estimation for various common nonlinear functions.
On-demand Landmark Activation to aid Navigation for Advanced Air Mobility

Navigation requirements for Uncrewed Aerial Systems (UAS) utilizing the Advanced Air Mobility (AAM) framework requires complete or partial autonomy that currently relies primarily on Global Positioning System (GPS) signals. However, with AAM services extending to areas like urban canyons that shows considerable degradation in the quality and accuracy of GPS data, alternate means of navigation are being explored. This paper proposes one such method by injecting synthetic known features as landmarks into the environment, that can be activated \textit{on demand} by the vehicles. Accurate navigation requires precise localization (knowledge of the UAS' current location with respect to a common reference frame or map) which can be achieved by measuring range to these landmarks with the help of onboard sensors. Practical considerations like cost of operation, communication cost, bandwidth to name a few prevents placing these landmarks abundantly in the environment. Hence, this paper explores the option of On-Demand Landmark Activation using localization uncertainty as a constraint in the system. The frequency of measurements received from landmarks and/or from neighboring vehicles in a cooperative setting is used as a metric in developing a data-driven approach for modeling uncertainty prediction. MATLAB is used to generate the training and test datasets for single and cooperative UAS cases which shows that Gaussian Process Regression is a viable technique for generating a closed-loop uncertainty model. An Unreal Engine (UE) based generalized high-fidelity simulator in AirSim has also been developed for a cooperative navigation scenario as part of this paper to provide datasets for robust testing in different environments.
Fairness Matters

The proof-of-work (PoW) based blockchains are more secure nowadays since profit-oriented miners contribute more computing powers in exchange for fair revenues. This virtuous circle only works under an incentive-compatible consensus, which is found to be fragile under selfish mining attacks. Specifically, selfish miners can conceal and reveal blocks strategically to earn unfairly higher revenue compared to honest behaviors. Previous countermeasures either require incompatible modifications or fail to consider the asynchronous network and multiple honest nodes setting in reality. In this paper, we introduce the unfairness measurement based on the KL-divergence from the computing power distribution to the revenue distribution of miners. To improve fairness with the existence of selfish miners, we propose a novel block promotion strategy namely Tit-for-Tat (TFT), for honest miners. In particular, based on a miner's local observation of forks, we design the suspicious probability measurement of other nodes. Rather than promoting a fresh block instantly, miners withhold it for different time periods according to others' suspicious probability before delivery. Meanwhile, to minimize the attacker's unfair revenue, we formulate the delay vector (DV) problem for honest miners to determine the optimal withholding time. We prove that DV problem is nonconvex, and thus propose two approximation algorithms that yield ϵ-suboptimal solutions. In addition, we extend TFT strategy to support dynamic networks. Extensive experiments validate the efficiency and effectiveness of our strategy and algorithms to reduce unfairness by 54.62% within bounded withholding time.
No Repetition

Stochastic sample-based estimators are among the most fundamental and universally applied tools in statistics. Such estimators are particularly important when processing huge amounts of data, where we need to be able to answer a wide range of statistical queries reliably, yet cannot afford to store the data in its full length. In many applications we need the sampling to be coordinated which is typically attained using hashing. In previous work, a common strategy to obtain reliable sample-based estimators that work within certain error bounds with high probability has been to design one that works with constant probability, and then boost the probability by taking the median over r independent repetitions. Aamand et al. (STOC'20) recently proposed a fast and practical hashing scheme with strong concentration bounds , Tabulation-1Permutation, the first of its kind. In this paper, we demonstrate that using such a hash family for the sampling, we achieve the same high probability bounds without any need for repetitions. Using the same space, this saves a factor r in time, and simplifies the overall algorithms. We validate our approach experimentally on both real and synthetic data. We compare Tabulation-1Permutation with other hash functions such as strongly universal hash functions and various other hash functions such as MurmurHash3 and BLAKE3, both with and without resorting to repetitions. We see that if we want reliability in terms of small error probabilities, then Tabulation-1Permutation is significantly faster.
Semi-supervised 3D U-Net Learning Based on Meta Pseudo Labels

Deep learning models have demonstrated promising performance for segmenting medical images and are significantly dependent on a huge amount of well-annotated data. However, it is difficult to get a large amount of data, particularly in clinical practices. Likewise, high-performance deep learning models have an enormous model size, restricting their use in actual applications. In order to reduce the burden of both expensive annotations and computational expenses, we designed the semi-supervised knowledge-based method on top of 3D U-Net and Meta Pseudo Labels. We train the teacher network with labelled data to generate the pseudo labels. And then we train the student network on the pseudo labels, and give the training feedback to the teacher network. The student network on FLARE2022 grand challenge Dataset achieved 81.19 % of DSC and 85.20% of NSD. As for the network inference speed, it needs 50.59 s for a single case.
Unlabeled Abdominal Multi-organ Image Segmentation Based on Semi-supervised Adversarial Training Strategy

The unlabeled images are helpful to generalize segmentation models. To make full use of the unlabeled images, we develop a generator-discriminator training pipeline based on the EfficientSegNet, which has achieved the best performance and efficiency in previous FLARE 2021 challenge. For the generator, a coarse-to-fine strategy is used to produce segmentations of abdominal organs. Then the labeled image and the ground truth are applied to optimize the generator. The discriminator receives the original unlabeled image or the relevant noised image, together with their generated segmentation results to determine which segmentation is better for the unlabeled image. After the adversarial training, the generator is used to segment the unlabeled images. On the FLARE 2022 final testing set of 200 cases, our method achieved an average dice similarity coefficient (DSC) of 0.8497 and a normalized surface dice (NSD) of 0.8915. In the inference stage, the average inference time is 11.67 s per case, and the average GPU (MB) and CPU (%) consumption per case are 311 and 225.6, respectively. The source code is freely available at https://github.com/Yuanke-Pan/Adversarial-Efficient SegNet .
Semi-supervised Augmented 3D-CNN for FLARE22 Challenge

Abdominal organ segmentation has been used in many important clinical applications, however, cases with accurate labels require huge manual labour and financial resources. As a potential alternative, semi-supervised learning can explore useful information from unlabeled cases, with only few labeled cases involved. Therefore, we propose our baseline model using augmented 3D-UNet and adopt semi-supervised method–Mean Teacher, to make quantitative evaluation on the FLARE2022 validation cases. Our method achieves average dice similarity coefficient (DSC) of 62.16 $$\%$$ , Normalized Surface Distance (NSD) of 62.27 $$\%$$ , running time of 9.58 s, and AUC of GPU and CPU is only 7424 and 199 respectively, which surpasses almost all other teams on resource consumption, demonstrating the effectiveness of our methods.
ONe Index for All Kernels (ONIAK)

In this work, we formulate and solve a new type of approximate nearest neighbor search (ANNS) problems called ANNS after linear transformation (ALT). In ANNS-ALT, we search for the vector (in a dataset) that, after being linearly transformed by a user-specified query matrix, is closest to a query vector. It is a very general mother problem in the sense that a wide range of baby ANNS problems that have important applications in databases and machine learning can be reduced to and solved as ANNS-ALT, or its dual that we call ANNS-ALTD. We propose a novel and computationally efficient solution, called ONe Index for All Kernels (ONIAK), to ANNS-ALT and all its baby problems when the data dimension d is not too large (say d ≤ 200). In ONIAK, a universal index is built, once and for all, for answering all future ANNS-ALT queries that can have distinct query matrices. We show by experiments that, when d is not too large, ONIAK has better query performance than linear scan on the mother problem (of ANNS-ALT), and has query performances comparable to those of the state-of-the-art solutions on the baby problems. However, the algorithmic technique behind this universal index approach suffers from a so-called dimension blowup problem that can make the indexing time prohibitively long for a large dataset. We propose a novel algorithmic technique, called fast GOE quadratic form (FGoeQF), that completely solves the (prohibitively long indexing time) fallout of the dimension blowup problem. We also propose a Johnson-Lindenstrauss transform (JLT) based ANNS-ALT (and ANNS-ALTD) solution that significantly outperforms any competitor when d is large.
Research Data Management

This chapter explains the main aspects and the importance of being familiar with research data management (RDM), stressing the centrality of the research data lifecycle and the importance of data sharing, whereby the ideas of the FAIR principles are characterized as a main driver of reuse. The importance of data management plans is emphasized as well. RDM processes such as adding metadata, citing, retrieving, and curating datasets are portrayed. The need for cooperating between disciplinary researchers and different data professionals, such as data librarians and data curators, is highlighted. Moreover, it is underlined that a number of educational programs to data science also contain data management.
Content and Context-Aware Recommender Systems for Business

E-commerce activities among prominent retailing firms in modern times is inconceivable without the ubiquitous presence of recommender systems. This article brings forth the more advanced topics like content based and context-aware methods. Content-based methods use the actions and ratings of the users to match the user to new items based on past ratings. The objective here is to create user profiles and subsequently subject the profiles to classification algorithms. Knowledge-based systems are for more customized products with little history of usage and therefore little past data to help in recommendations. Such systems rely on either case-based recommendations or on a set of relevant constraints to identify appropriate recommendations. And finally, ensemble recommender systems help in combining the prediction power from multiple data sources. Finally, the author presents a discussion on the evaluation methods for recommender systems. The article is aimed towards both academic and managerial audiences.
Machine Learning for Decision Support in the ICU

In this article, the authors provide an overview of the potential and challenges of machine learning for healthcare decision support. They first discuss the healthcare decision support ecosystems, including (1) beneficiaries, (2) health data, and (3) models. They then introduce the three main challenges of the healthcare decision support systems: data complexity, decision criticality, and model explainability. From there, they use unplanned intensive care unit readmission predictions in tackling the three main challenges of machine learning-based healthcare decision support systems. They investigate the data complexity issue by adopting dimension reduction techniques on patients' medical records to integrate patients' chart events, demographics, and the ICD-9 code. To address the decision criticality issue, they perform an in-depth deep learning performance analysis, and they analyze each feature's contribution to the predictive model. To unpack the model explainability issue, they illustrate the importance of each input feature and its combinations in the predictive model.
Challenges and Opportunities in Knowledge Representation and Reasoning

Knowledge representation is of immense importance in the field of artificial intelligence and natural language processing. The representation of knowledge goes hand in hand with automated reasoning as one of the key goals of representing knowledge effectively is being able to reason about it. Researchers of knowledge representation and reasoning have built techniques and methods that are the main source of development in computer science and have made tremendous progress in a wide variety of real-life applications, ranging from natural language processing to robotics and software engineering. Further research is required in order to allow a more active role in guiding the reasoning process through the knowledge representation framework. This article has discussed knowledge representation and reasoning and analyzed the major challenges and new opportunities where novel knowledge representation and reasoning research have had a major impact.
Validating Machine Vision Competency Against Human Vision

This work elucidates the human intelligence performance and machine intelligence in geographical region-wise face classification incorporating a sample of 120 human identifiers and computational models like convolutional neural network and colour local binary pattern. A novel Indian colour face database is created consisting of 2010 distinctive face images of east and south regions. On human side, an automated human intelligence system is established to evaluate the visual capabilities of human. On machine side, the authors trained two CovNets, one comprising more layers trained with 1800 normal face database images and another one trained with 1000 contoured images of face obtained by canny edge detection approximation method, to estimate the human intelligence response that found face shape the more discriminative feature among other face features. Experimental results showed the human classification proficiency (96%) stood superior to the machine algorithms even in challenging aspects.
Comparison of Multi Layer Perceptron, Random Forest &amp; Logistic Regression on Students Performance Test

The test is one thing that can be taken to measure a person's ability to understand a material or a competency. In general, there is a final test taken by students at the school level, before reaching the final test, usually students will take a series of preparatory tests. In reality, of course, not all students can take the test preparation well. therefore of course the school has data related to test preparation. From this test preparation data, a classification technique can be used to classify the data of students who have completed the preparatory test and students who have not completed the preparatory test, so that schools can prepare the best strategy. To assist in classifying data, data classification techniques are needed, in this study the Multi-Layer Perceptron, Random Forest and Simple Logistics algorithms were used. These three methods produce different accuracy when used for the data classification process. For testing the data, scenarios are used using cross-validation. The results of this test scenario show that the Logistic Regression method is superior to the Random Forest and Multi-Layer Perceptron methods with an accuracy of 73.9%. The best Root Mean Square Error results are in the Multi-Layer Perceptron method with the smallest value of 0.363.
Evaluation of Software Quality Assurance Silampari Smart City Of Lubuklinggau Based On ISO/IEC 25010:2011 Analysis Model

Silampari Smart City is a mobile application developed by Government of Lubuklinggau City to interact with the public. The application must meet the elements of good eligibility so that people feel comfortable and safe in using tha application. This research will evaluate this mobile application using ISO/IEC 25010:2011 which becomes the basis for evaluating using the Product Quality and Quality-in-Use dimensions. The characteristics used have been adapted for Mobile Applications based on Kuria's research. The results of the study show that the characteristics that became the priority of the stakeholders were Security, based on the Analytical Hierarchy Process (AHP) method. Based on characteristic testing, it shows that the application has fulfilled the elements of each characteristic. Based on the user's perspective, both stakeholders and general users show that the application is very feasible in accordance with the characteristics of ISO/IEC 25010:2011.
Analysis and Design of Supply Chain Management System at CV Putra Nusantara Mandiri

One of the critical systems needed in retail companies is the inventory system. This system is related to inventory management, transaction recording, reporting, distribution and can be used for sales forecasting. CV Putra Nusantara Mandiri is one of the retail companies that has not implemented an inventory system in their business processes, and this is often causes several problems, both financial and non-financially, which should be prevented by implementing an inventory system. To fix the problems, it is necessary to change the system from manual to computerized, which is integrated through a supply chain management approach to help maximize the company's profitability. This research aims to design an inventory system for warehouse and logistics units at CV Putra Nusantara Mandiri. The research was conducted using a descriptive qualitative methodology and further developed using the data collection methods of literature study, field study, and interviews. In addition to the design method using the waterfall method, which refers to the development of the Pressman. The results of this study are in the form of an inventory system design that can later be used to facilitate the supply chain process with suppliers and consumers, as well as a User Interface design for the back-office web.
Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well.