Reviewerly/exHarmony huggingface dataset:
Facial feature tracking method using a hybrid model of the Kalman filter and the sliding innovation filter

The purpose of this paper is to aid in detecting synthesized video (specifically created through the use of DeepFake) by exploring facial-feature tracking methods. Analyzing individual facial features, should allow for more successful detection of DeepFake videos according to H. Nguyen et al.’s research [22] and A. A. Maksutov’s list of commonly use techniques to identify fabricated media [17]. To detect these facial features in images, Computer Vision techniques such as YOLOv3 [24] can be used. Once detected, object-tracking methods should be explored. This paper will compare the accuracy of three existing object-tracking methods: the minimum-distance approach, the Kalman Filter (KF) method, and the Sliding Innovation Filter (SIF) method. Following this comparison, the paper proposes a novel hybrid object-tracking approach, in which the benefits of the KF method and SIF method are combined to provide a time-gap tolerant object-tracking method. Each of the models are tested on their ability to track multiple objects that follow different trajectories and compared against one another to identify the most effective manner of tracking.
Generalizing the unscented Kalman filter for state estimation

The recent generalized unscented transform (GenUT) is formulated into a recursive Kalman filter framework. The GenUT constrains 2n + 1 sigma points and their weights to match the first four statistical moments of a probability distribution. The GenUT integrates well into the unscented Kalman filter framework, creating what we call the generalized unscented Kalman filter (GUKF). The measurement update equations for the skewness and kurtosis are derived within. Performance of the GUKF is compared to the UKF under two studies: noise described by a Gaussian distribution and noise described by a uniform distribution. The GUKF achieves lower errors in state estimation when the UKF uses the heuristic tuning parameter κ = 3 − n. It is also stated that when the parameter κ is tuned to an optimal value, the UKF performs identically to the GUKF. The advantage here is that GUKF requires no such tuning.
Derivation of the sliding innovation information filter for target tracking

An information filter is one that propagates the inverse of the state error covariance, which is used in the state and parameter estimation process. The term ‘information’ is based on the Cramer-Rao lower bound (CRLB), which states that the mean square error of an estimator cannot be smaller than an amount based on its corresponding likelihood function. The most common information filter (IF) is derived based on the inverse of the Kalman filter (KF) covariance. This paper introduces preliminary work completed on developing the information form of the sliding innovation filter. The SIF is a relatively new type of predictor-corrector estimator based on sliding mode concepts. In this brief paper, the recursive equations used in the sliding innovation information filter (SIIF) are derived and summarized. Preliminary results of application to a target tracking problem are also studied.
Estimating the complex maneuvering of a UGV using IMM-SIF

Estimating the position of a unmanned ground vehicle (UGV) that is navigating a complex road is a challenging task. Numerous algorithms have been developed to estimate the maneuvering status of the UGV. In this study, a newly developed filtering technique called the sliding innovation filter (SIF) is combined with multiple model technique to improve the estimation accuracy. The SIF uses the measured states as a discontinuous hyperplane to constrain the estimates to stay close to it. By combining the benefits of both methods, the proposed filter minimizes chatter during position estimation when the UGV is maneuvering. The effectiveness of the proposed method is evaluated on a UGV navigating an S-shaped road, and the results are compared to those obtained using the standard SIF.
A machine learning-based state estimation approach for varying noise distributions

The field of estimation theory is concerned with providing a system with the ability to extract relevant information about the environment, resulting in more effective interaction with the system’s surroundings through more well-informed, robust control actions. However, environments often exhibit high degrees of nonlinearity and other unwanted effects, posing a significant problem to popular techniques like the Kalman filter (KF), which yields an optimal only under specific conditions. One of these conditions is that the system and measurement noises are Gaussian, zero-mean with known covariance, a condition often hard to satisfy in practical applications. This research aims to address this issue by proposing a machine learning-based estimation approach capable of dealing with a wider range of noise types without the need for a known covariance. Harnessing the generative capabilities of machine learning techniques, we will demonstrate that the resultant model will prove to be a robust estimation strategy. Experimental simulations are carried out comparing the proposed approach with other conventional approaches on different varieties of functions corrupted by noises of varying distribution types.
Review of Visible-Infrared Cross-Modality Person Re-Identification

Person re-identification (ReID) is a sub-problem under image retrieval. It is a technology that uses computer vision to identify a specific pedestrian in a collection of pictures or videos. The pedestrian image under cross-device is taken from a monitored pedestrian image. At present, most ReID methods deal with the matching between visible and visible images, but with the continuous improvement of security monitoring system, more and more infrared cameras are used to monitor at night or in dim light. Due to the image differences between infrared camera and RGB camera, there is a huge visual difference between cross-modality images, so the traditional ReID method is difficult to apply in this scene. In view of this situation, studying the pedestrian matching between visible and infrared modalities is particularly crucial. Visible-infrared person re-identification (VI-ReID) was first proposed in 2017, and then attracted more and more attention, and many advanced methods emerged.
Human-robot teaming for a cooperative game in a shared partially observable space

An open research question is how to best pair a human and agent (e.g., AI, autonomous) relative to a complex, multi-objective task in a dynamic and unknown partially observable environment. At the heart of this challenge resides even deeper questions like what AI is needed and how can bi-directional and multi-directional human-robot trust be established. In this paper, the theoretical framework for a simple 2D grid world-based cooperative search and rescue game is explored. The resultant prototype interface enables the study of human-robot interaction for human-robot teaming. First, the design and implementation of a prototype interface is discussed. A 2D grid-world was selected to simplify the investigation and eliminate confounding factors that arise in more complicated simulated 3D and real world experiments. Next, different types of autonomous agents are introduced, as they impact our studies and ultimately are an integral element of the underlying research question. This is followed by three levels of increasing complexity open-ended games, easy, medium, and hard. The current paper does not contain human experimentation results. That is the next step in this research. Instead, this article introduces, explains, and defends a set of design choices and working examples are provided to facilitate open discussion.
A cognitive dynamics framework for practical blockchain applications

Blockchain technology has gained notoriety as the foundation for cryptocurrencies like Bitcoin. However, its possibilities go well beyond that, enabling the deployment of new applications that were not previously feasible as well as enormous improvements to already existing technological applications. Several factors impacting the consensus mechanism must fall within a specific range for a blockchain network to be efficient, sustainable and secure. The long-term sustainability of current networks, like Bitcoin, is in jeopardy due to their relatively uncompromising reconfiguration, which tends to be inflexible, and somewhat independent of environmental circumstances. To provide a systematic methodology for integrating a sustainable and secure adaptive framework, we propose the amalgamation of cognitive dynamic systems theory with blockchain technology, specifically regarding variant network difficulty. A respective architecture was designed with the employment of Long-Short Term Memory (LSTM) to control the difficulty of a network with Proof-of- Work Consensus.
Single Image Desnow Based on Vision Transformer and Conditional Generative Adversarial Network for Internet of Vehicles

With the increasing popularity of artificial intelligence applications, machine learning is also playing an increasingly important role in the Internet of Things (IoT) and the Internet of Vehicles (IoV). As an essential part of the IoV, smart transportation relies heavily on information obtained from images. However, inclement weather, such as snowy weather, negatively impacts the process and can hinder the regular operation of imaging equipment and the acquisition of conventional image information. Not only that, but the snow also makes intelligent transportation systems make the wrong judgment of road conditions and the entire system of the Internet of Vehicles adverse. This paper describes the single image snow removal task and the use of a vision transformer to generate adversarial networks. The residual structure is used in the algorithm, and the Transformer structure is used in the network structure of the generator in the generative adversarial networks, which improves the accuracy of the snow removal task. Moreover, the vision transformer has good scalability and versatility for larger models and has a more vital fitting ability than the previously popular convolutional neural networks. The Snow100K dataset is used for training, testing and comparison, and the peak signal-to-noise ratio and structural similarity are used as evaluation indicators. The experimental results show that the improved snow removal algorithm performs well and can obtain high-quality snow removal images.
CSNMG: constructing sequence neighbourhood mapping graphs to compress FASTQ files

With advances in sequencing technology, long-read sequences in single molecular sequencing (SMS) technologies have more structural variation and sequencing errors than short-read sequences in next-generation sequencing (NGS). Most of the current gene compression schemes have been developed for short read sequences, and further research is needed to develop more suitable compressors for long read sequences. We propose a reference-free compression scheme for FASTQ file based on the construction of d-neighbourhood mapping graph, and obtain better experimental results.
Navigating co-design of actor-networks

In this paper, we illustrate the importance of involving different types of users in collaborative design and innovation processes to allow end-users to speak on their own behalf. However, involving multiple actors with diverse concerns requires designers to carefully stage spaces for negotiation. Through a case study of the design of a pilot program to make insulin more accessible to marginalised diabetes patients in India, we explore how designers navigate the design process by staging and facilitating several spaces for negotiation populated by patients with diabetes, doctors, company representatives, design games and management templates.
Multiple correlation based decision tree model for classification of software requirements

Recent research in requirements engineering (RE) includes requirements classification and use of machine learning (ML) algorithms to solve RE problems. The limitation of existing techniques is that they consider only one feature at a time to map the requirements without considering the correlation of two features and are biased. To understand these limitations, our study compares and extends the ML algorithms to classify requirements in terms of precision and accuracy. Our literature survey shows that decision tree (DT) algorithm can identify different requirements and outperforms existing ML algorithms. As the number of features increases, the accuracy using the DT is improved by 1.65%. To overcome the limitations of DT, we propose a multiple correlation coefficient based DT algorithm. When compared to existing ML approaches, the results showed that the proposed algorithm can improve classification performance. The accuracy of the proposed algorithm is improved by 5.49% compared to the DT algorithm.
BITSAT: an efficient approach to modern image cryptography

This paper proposes a new approach towards efficient image cryptography that works using the concepts of BITplane decomposition and bit-level scrambling. This method does not require the involvement of additional or extra images. Users have the flexibility of choosing: 1) any BITplane decomposition method; 2) logic that runs the key generation block; 3) customisation in the bit-level permutations performed during scrambling. The implementation of the method is simple and free of heavily complex steps and operations. This makes the algorithm applicable in a real-world scenario. The BITSAT algorithm is applied to a variety of images and consequently, the encrypted images generated showcase a high level of encryption. The analysis and evaluation of the algorithm and its security aspects are performed and described in detail. The paper also presents the application domains of the method. Overall, the results and analysis indicate a positive working scope and suitability for real-life applications.
THE ROLE OF IPNU-IPPNU IN CONTROLLING YOUTH DETERMINATION IN PRINGLANGU VILLAGE

Juvenile delinquency is a social phenomenon that is rife in society. Many teenagers now engage in deviant behavior such as fighting, skipping school, and stealing. One of the efforts to overcome juvenile delinquency is to actively participate in the IPNU-IPPNU social organization where they live. The purpose of this study was to find out the forms of juvenile delinquency in Pringlangu Village, the role of the IPNU-IPPNU Pringlangu branch in tackling juvenile delinquency, the supporting and inhibiting factors of the role of the IPNU-IPPNU organization in tackling juvenile delinquency. This research uses a descriptive qualitative approach. Data collection methods are in the form of observation, interviews, and documentation. The results of this study reveal that the most prevalent juvenile delinquency is online gambling. The role of IPNU-IPPNU in tackling juvenile delinquency through routine religious activities. Supporting factors for IPNU-IPPNU in tackling juvenile delinquency are a supportive community environment, coaches who always guide, and self-motivation. Meanwhile, the inhibiting factor was the lack of awareness of Pringlangu Village youths about the IPNU-IPPNU and participating in it. Difficult to get parental permission, different busy background, and lack of active members.
Douglas Leslie Miles (1944–2022)

Geostandards and Geoanalytical ResearchEarly View Obituary Douglas Leslie Miles (1944–2022) Jenny Cook, Corresponding Author Jenny Cook Honorary Secretary, International Association of Geoanalysts [email protected] Search for more papers by this author Jenny Cook, Corresponding Author Jenny Cook Honorary Secretary, International Association of Geoanalysts [email protected] Search for more papers by this author First published: 30 June 2023 https://doi.org/10.1111/ggr.12514 with heartfelt contributions by Susan Miles, Phil Potts and Jacinta Enzweiler Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Moment-Based Kalman Filter: Nonlinear Kalman Filtering with Exact Moment Propagation

This paper develops a new nonlinear filter, called Moment-based Kalman Filter (MKF), using the exact moment propagation method. Existing state estimation methods use linearization techniques or sampling points to compute approximate values of moments. However, moment propagation of probability distributions of random variables through nonlinear process and measurement models play a key role in the development of state estimation and directly affects their performance. The proposed moment propagation procedure can compute exact moments for non-Gaussian as well as non-independent Gaussian random variables. Thus, MKF can propagate exact moments of uncertain state variables up to any desired order. MKF is derivative-free and does not require tuning parameters. Moreover, MKF has the same computation time complexity as the extended or unscented Kalman filters, i.e., EKF and UKF. The experimental evaluations show that MKF is the preferred filter in comparison to EKF and UKF and outperforms both filters in non-Gaussian noise regimes.
Software Cost Estimation Using Neural Networks

Software Cost Estimation (SCE) is one of the most vital parts when starting a new software engineering project; it helps with allocating resources, managing risks, making informed decisions, and stands in correlation with the success or the failure of a project. Because Software Cost Estimation (SCE) is prone to human bias, solutions started being researched with the aid of Artificial Intelligence (AI) and Machine Learning (ML). This paper will investigate the importance of Software Cost Estimation (SCE). Further, the existing taxonomies and methodologies regarding using neural networks with Software Cost estimation will be compared (COCOMO, GEHO-ANN, OLCE, and -ANN-NEAT). This will be done using evaluation metrics such as RMSE, MMRE, PRED, MAE, etc. After, further research is proposed on why using Deep Reinforcement Learning (DRL) could be very beneficial for developing Software Cost Prediction Models. This technique combines Deep Learning (DL) and Machine Learning (ML) and can solve complex tasks with many variables and a rapidly developing environment.
A case study on class design and operation of self-exploration teacher education using problem-based learning(PBL)

Objectives The aim of this study is to present examples of problem-based learning(PBL) classes that have been designed and run, and to investigate the effects of class operation based on student feedback and evaluation findings.&#x0D; Methods Self-exploration writing instruction utilizing PBL was used in the first semester of 2023 at D University in Seoul for 5 of the 15 weeks of the required general elective writing classes. 30 students who are enrolled in the researcher's writing class serve as the study's subjects. The outcomes of group activity sheets, group task performance plans, individual reports, conversations, reflection diaries, self-assessment, peer evaluation, and instructor evaluation were statistically and qualitatively assessed in order to corroborate the results of class operation.&#x0D; Results The research confirmed, first, that students' self-esteem increased in the final writing product as a result of reinforcing voluntary learning motivation so that learners can solve problems in real life through critical interpretation and reflective reflection. Second, it was demonstrated that by actively resolving real-world issues, students can improve their self-efficacy. Third, it was established that talking and sharing about the decisions and outcomes of other students can help them develop a variety of worldviews and perspectives as well as effective sympathetic communication abilities.&#x0D; Conclusions The case for expanding the subject from first grade to all grades for the development of a comparative writing program, as well as self-exploratory writing education using problem-based learning(PBL), which was only dealt with at the level of general elective subjects, were made in order to develop the discussion of this study. It is meant to be applied to fundamental research.
Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference.

Simulation-based inference (SBI) methods tackle complex scientific models with challenging inverse problems. However, SBI models often face a significant hurdle due to their non-differentiable nature, which hampers the use of gradient-based optimization techniques. Bayesian Optimal Experimental Design (BOED) is a powerful approach that aims to make the most efficient use of experimental resources for improved inferences. While stochastic gradient BOED methods have shown promising results in high-dimensional design problems, they have mostly neglected the integration of BOED with SBI due to the difficult non-differentiable property of many SBI simulators. In this work, we establish a crucial connection between ratio-based SBI inference algorithms and stochastic gradient-based variational inference by leveraging mutual information bounds. This connection allows us to extend BOED to SBI applications, enabling the simultaneous optimization of experimental designs and amortized inference functions. We demonstrate our approach on a simple linear model and offer implementation details for practitioners.
Learning to Learn to Predict Performance Regressions in Production at Meta

Catching and attributing code change-induced performance regressions in production is hard; predicting them beforehand, even harder. A primer on automatically learning to predict performance regressions in software, this article gives an account of the experiences we gained when researching and deploying an ML-based regression prediction pipeline at Meta.In this paper, we report on a comparative study with four ML models of increasing complexity, from (1) code-opaque, over (2) Bag of Words, (3) off-the-shelve Transformer-based, to (4) a bespoke Transformer-based model, coined SuperPerforator. Our investigation shows the inherent difficulty of the performance prediction problem, which is characterized by a large imbalance of benign onto regressing changes. Our results also call into question the general applicability of Transformer-based architectures for performance prediction: an off-the-shelve CodeBERT-based approach had surprisingly poor performance; even the highly customized SuperPerforator architecture achieved offline results that were on par with simpler Bag of Words models; it only started to significantly outperform it for down-stream use cases in an online setting. To gain further insight into SuperPerforator, we explored it via a series of experiments computing counterfactual explanations. These highlight which parts of a code change the model deems important, thereby validating it.The ability of SuperPerforator to transfer to an application with few learning examples afforded an opportunity to deploy it in practice at Meta: it can act as a pre-filter to sort out changes that are unlikely to introduce a regression, truncating the space of changes to search a regression in by up to 43%, a 45x improvement over a random baseline.
Latent Class Analysis of Multiple Parameters of the Individual Socioeconomic Context on Sleepiness

© 2023. Brazilian Sleep Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)