Reviewerly/exHarmony huggingface dataset:
Synthetic Crime Scene Generation Using Deep Generative Networks

Synthetic crime scenes can provide an effective training tool for law enforcement personnel, enabling them to gain valuable experience without the need for real-world practice. However, creating realistic synthetic crime scenes is a challenging task that requires advanced artificial intelligence techniques. In this paper, we propose a novel architecture for generating synthetic crime scenes using a hybrid VAE + GAN model. The proposed architecture leverages scene graph information and input text embeddings to generate coarse images of the foreground and background using a conditional variational autoencoder (VAE). Two separate generators then generate more detailed images of the foreground and background, and a fusion generator combines them to create a final image. A discriminator evaluates the realism of the generated images. This approach represents a significant contribution to the field, as it enables the generation of highly realistic crime scenes from textual input. The proposed architecture has the potential to be used by law enforcement agencies to aid in crime scene reconstruction, and may also have applications in related fields such as forensic science and criminal justice.
YouTube Sentimental Analysis Using a Combined Approach of KNN and K-means Clustering Algorithm

Sentiment analysis is the method for learning what users think and feel about a service or a product. YouTube, one of the most widely used video-sharing websites, receives millions of views daily. Many businesses utilize YouTube, a well-known social media platform, to sell their goods through videos and advertisements. Popular YouTube channels are seeing a sharp increase in the daily volume of comments. We cannot easily notice and comprehend this enormous volume of comments, which are largely unstructured, so we need some applications or methods that use large amounts of data to perform sentiment analysis. So the sentiment analysis is, therefore, necessary to categorize the comments on a bigger platform to find meaningful ways to categorize. In this paper, we employed sentimental analysis and methods that may be applied to comments on YouTube videos. Additionally, it describes and groups various techniques that are helpful in sentiment analysis and data mining studies. For sentimental analysis, we merged the K-Nearest Neighbor (KNN) and K-means clustering approaches. For more discussion, the proposed technique is compared with the SVM classifier and Naive Bayes for better accuracy.
Proposing a Model to Enhance the IoMT-Based EHR Storage System Security

The Internet of Medical Things (IoMT) and Electronic Health Records (EHR) are core aspects of today's healthcare facilities, hence these technologies and storage platforms should be equipped with innate safeguarding and secrecy concerns for the welfare of individual human beings. The utmost feasible precautions need to be taken by healthcare organizations with regard to user consent, verifiability, scalability, and authentication protocols, aside from prospective vulnerability intrusions. Especially considering the explosive rise of modern health facilities, fraudsters are consistently searching for means to access healthcare information sources as their prime targets. The significance of data gleaned from the healthcare systems is highly valuable on the black market. Blockchain technology is recognized as a much more alluring way to facilitate information sharing via the entire healthcare distribution network while endangering data confidentiality and integrity. The purpose of this research is to strengthen the IoMT-based EHR storage system security utilizing Hyperledger Fabric infrastructure. The proposed model leverages the usage of Hyperledger Fabric's unalterableness and data protection characteristics to guarantee the confidentiality and integrity of EHRs while also ensuring secure data exchange and identity management for authorized individuals. Hyperledger Fabric strategies must be integrated with edge computing and cloud platforms to further their value-added best attributes. When our proposed model are objectively implemented in the near future, the key components that can be employed to minimize the exploitation of healthcare data storage would be recognized.
The Internet Backpack: Transforming STEM Education, Agriculture and Economic Development in Liberia, West Africa

STEM education is an important part of education systems around the world. It is especially important for developing countries, as they attempt to transition to modern growth and catch up with more advanced technologies for educating students. Investment in public education, especially STEM education, has sparked transformational changes in development processes, with improvements in living standards, healthcare and longevity, food security and other indicators of human development. However, climate change has the potential to erode these gains. For example, food and health insecurities due to the changing global environment and land degradation are major issues facing poor countries. With a focus on improving education for school children in post-war Liberia, our research examines the efficacy of the Internet Backpack in fostering STEM education. The Internet Backpack has been deployed globally as a self-sufficient technology that facilitates learning. In this paper, we discuss the Internet Backpack as a technology applied in STEM learning in Liberia through multi- stakeholder partnerships. We discuss efforts to develop project- based learning curricula which include environmental life sciences programs on sustainable agriculture and forestry, earth science, and life science to protect critically important species. We highlight the importance of implementing technologies such as the Internet Backpack for enhancement of STEM education. We also highlight the importance of STEM education in the 21st century development processes, including prioritizing the mitigation of climate change.
Concept-Aware Video Captioning: Describing Videos With Effective Prior Information

Concepts, a collective term for meaningful words that correspond to objects, actions, and attributes, can act as an intermediary for video captioning. While many efforts have been made to augment video captioning with concepts, most methods suffer from limited precision of concept detection and insufficient utilization of concepts, which could provide caption generation with inaccurate and inadequate prior information. Considering these issues, we propose a Concept-awARE video captioning framework (CARE) to facilitate plausible caption generation. Based on the encoder-decoder structure, CARE detects concepts precisely via multimodal-driven concept detection (MCD) and offers sufficient prior information to caption generation by global-local semantic guidance (G-LSG). Specifically, we implement MCD by leveraging video-to-text retrieval and the multimedia nature of videos. To achieve G-LSG, given the concept probabilities predicted by MCD, we weight and aggregate concepts to mine the video's latent topic to affect decoding globally and devise a simple yet efficient hybrid attention module to exploit concepts and video content to impact decoding locally. Finally, to develop CARE, we emphasize on the knowledge transfer of a contrastive vision-language pre-trained model (i.e., CLIP) in terms of visual understanding and video-to-text retrieval. With the multi-role CLIP, CARE can outperform CLIP-based strong video captioning baselines with affordable extra parameter and inference latency costs. Extensive experiments on MSVD, MSR-VTT, and VATEX datasets demonstrate the versatility of our approach for different encoder-decoder networks and the superiority of CARE against state-of-the-art methods. Our code is available at https://github.com/yangbang18/CARE.
A remote sensing satellite image compression method based on conditional generative adversarial network

With the sharp increase of images on satellites, the efficiency of satellite-to-ground data transmission has become a bottleneck that restricts the effectiveness of remote sensing satellites. To alleviate the pressure of data transmission, we conducted in-depth research on remote sensing satellite image compression technology. Traditional methods and existing deep learning methods are prone to losing detailed information when dealing with remote sensing satellite images with complex textures and rich details. Given that Generative Adversarial Networks (GAN) have advantages in texture generation and detail restoration, we propose a remote sensing satellite image compression method based on conditional GAN. Our main innovations are: 1) proposing a compression framework for remote sensing satellite images based on conditional GAN, which improves the reconstruction quality through adversarial learning between the conditional generator and discriminator. 2) introducing the Laplacian of Gaussian loss to train the model, which emphasizes details such as edges, contours, and textures in remote sensing images. 3) introducing multiple perceptual metrics to calculate the similarity between images, which comprehensively evaluates the quality of reconstructed images. Experimental results show that our method has better visual effects and objective evaluation indicators than traditional methods and existing deep learning methods at the same compression ratio.
Distributed Invariant Extended Kalman Filter Using Lie Groups: Algorithm and Experiments

Distributed Kalman filters have been widely studied in vector space and have been applied to 2-D target state estimation using sensor networks. In this article, we introduce a novel distributed invariant extended Kalman filer (DIEKF) that exploits matrix Lie groups and is suitable to track the target’s 6-DOF motion in a 3-D environment. The DIEKF is based on the proposed extended covariance intersection (CI) algorithm that guarantees consistency in matrix Lie groups. The DIEKF is fully distributed as each agent only uses the information from itself and the one-hop communication neighbors, and it is robust to a time-varying communication topology and changing blind agents. In addition to assuming a known target model, we study the case where the target’s true motion is unknown. To evaluate the performance, first, we apply the algorithm in a camera network to track a target pose. Extensive Monte-Carlo simulations have been performed to analyze the performance. More importantly, the performance is further verified with real data collected by using a quadrotor with multiple ultra-wideband (UWB) anchor receivers. Overall, the proposed algorithm is more accurate and more consistent in comparison with our recent work on the quaternion-based distributed extended Kalman filter (QDEKF).
Improving Educational Standards Using Visualization Dashboards for Decision Making

Education is one of the most important predictors for success for young children around the world. In the United States, there have been significant movements and progress to advance education to foster the next generation of talent, as seen with legislation such as the No Child Left Behind Act, and, more recently, Every Student Succeeds Act (ESSA). As a way to provide the public with information and to create a convenient way to view, interpret, compare and contrast educational data, many states in the U.S. have created dashboards at different levels of government that encapsulate performance, along with environmental factors, such as income. These dashboards allow for public accountability and for individuals to see how well certain schools, districts, or areas are performing. By providing these visualizations to educators, principals, and lawmakers, people with authority and power to make meaningful change in K-12 education are better equipped to do so. Such dashboards can carry some unintended consequences, such as the possibility of incorrectly correlating performance with action. But when the underlying issues may be more systemic, for example, based on differences in demographics, these effects can be minimized by creating better quality dashboards that clearly depict demographics alongside performance metrics and outcomes. This paper is significant in reviewing education-specific dashboards and the need to build better dashboards, ensuring that benefits are achieved for a variety of stakeholders.
Rational Coordination in Cognitive Agents: A Decision-Theoretic Approach Using ERMM

Over the years, research in multi-agent systems has become increasingly popular. Agents evolve by interacting with their environment and must communicate with other agents in order to do various cooperative tasks. The research aims to provide efficient coordination among cooperative cognitive agents in unpredictable multi-agent situations. Xiang’s rational agent model addresses scenarios when no social conventions or predefined communication protocols exist for the agents’ interaction and then makes decisions by recursive modeling. We address the deficiencies of the loosely coupled framework and the problem of mispredictions in Xiang’s architecture. It is based on Lawniczak’s Architecture for generic cognitive agents and an enhanced model of Xiang’s Recursive Modeling Method for coordinated decision-making in multi-agent situations. We instruct the cognitive agent to learn about other agents from past mispredictions and then consider its best choice. The feedback module is incorporated so agents can learn to maximize their joint expected reward. It filters the mispredictions and evaluates the error rate. We compare the enhanced method with the Recursive Modeling Method. The results show that mispredictions are corrected from 33% to 10.9% and errors in perception get reduced 22% to 0.097%, as the system progresses. Overall, the approach demonstrates superior performance. It significantly lowers the rate of mispredictions about other agents’ actions and takes 30% to 42% less time and 55.4 % fewer moves than RMM.
Vehicle-Road Multi-View Interactive Data Fusion

Aiming at solving the problem of target occlusion in crowded scenes, the aggregation of multiple camera views can outperform a single view. Some researchers project the feature map or RGB image getting from the feature extraction network to the ground plane by perspective transformation and then use the spatial aggregation without anchor frame to aggregate the feature maps of multiple views. However, the projected feature maps are limited by a lack of semantic information. In order to obtain feature maps with rich semantic information, we will introduce a new multi-view detection method MVT, which effectively extracts features from multiple perspectives. To improve TP and the overall robustness of the model, we will also introduce a method for predicting the positioning uncertainty of bounding box reliability.
Multi-Sensor Object Detection

3D object detection is becoming an indispensable functional module for environmental perception in autonomous driving, and LiDAR-based detection methods have made remarkable progress in terms of accuracy. However, point clouds often fail to distinguish objects with similar structures, leading to false detection. Therefore, other sensors and LiDAR fusion are naturally considered a solution. Nevertheless, current fusion methods are either limited to poor precision or efficiency. To this end, this chapter proposes a plug-and-play module named RI-Fusion to achieve the effective fusion of LiDAR and camera, and the module can be easily accessed by existing LiDAR-based algorithms. Furthermore, a particular fusion method of RaDAR and 16-line LiDAR based on multimodal and multi-scale fusion is proposed, called $$M^{2}$$ -Fusion. The interaction is achieved by learning the features of each modality by exchanging the information of the intermediate feature layers with a self-attention mechanism. Experiments show that the method has better environmental adaptability and low cost.
Information Quality in Data Fusion

As an essential element of multi-sensor fusion, uncertainty provides theoretical support for making better decisions using multi-source data. This chapter begins by introducing the prerequisite knowledge. Following that, two types of uncertainty are introduced from the perspective of models and data, known as epistemic uncertainty and theoretical uncertainty. It is possible to deal with the contingencies for a given model or set of sensors on a vehicle by delving into these two types of uncertainty. This chapter proposes a novel multimodal fusion architecture from an information-theoretic perspective. The proposed model is presented from four aspects: baseline, uncertainty modeling, fusion step, and implementation. Furthermore, the experimental procedure, including data preprocessing, noise simulation, experimental results, and analysis, is described in detail.
Action Encoding in Algorithms for Learning Controllable Environment

The work considers the Learning Controllable Embedding (LCE) algorithmic framework which is tasked to learn a lower-dimensional latent state space steerable using simpler control algorithms like iLQR. However, most LCE papers consider raw action spaces, which limits their applicability to simpler low-dimensional continuous action spaces. This paper introduces the support of a larger set of action spaces by adding the action encoding to two LCE frameworks and provides empirical evidence on the algorithms’ performance. Algorithms are rederived for this purpose, and the suboptimality analysis is extended. The experiments are discussed, including benchmark tasks, data collection policies, and a comparison of the considered algorithms. Moreover, the paper investigates the effect of the size of the action encoding on the performance and explores the possibilities of using LCE for more complex action spaces, such as discrete or high-dimensional ones. The results show that the proposed method can effectively learn controllable embeddings for a wider range of action spaces.
Investigations of Different Classes Hybrid Deep Learning Networks and Analysis of Their Efficiency in Forecasting

Investigations of hybrid GMDH deep learning neuro-fuzzy networks and neo-fuzzy networks in the problem of forecasting in the financial sphere were carried out. The problem of forecasting NASDAQ stock prices with an interval of 1 day, 1 week and month was considered. In the process of forecasting the hyperparameters of hybrid fuzzy neural networks were optimized, namely the number of inputs, the number of rules and the size of the sliding window. Optimal structures of hybrid fuzzy neural networks for different prediction intervals were synthesized. Experimental studies of the efficiency of forecasting hybrid neo-fuzzy and neuro-fuzzy networks were carried out and compared with conventional DL networks according to MAPE criterion. Training time of hybrid DL networks was estimated and compared.
Design and Analysis of Hose Clamp

A hose is attached to and sealed off of a fitting such a barb or nipple using a hose clamp, hose clip, or hose lock. This study examines the relationship between experimental results and theoretical models to analyse the bending stress in hose clamps. in the experiment, stiff cylinder was encircled by flat section band clamps, and strain and related displacement were measured. To steadily increase torque till failure, the clamp nut was tightened. The theoretical torque value and the torque obtained from FEM analysis are compared and further analyzed to prepare the hose clamp.
Adding Filters in Nonlinear Observers

One of the main performance limiting factors of an observer is the presence of sensor noise. This chapter proposes the use of filters to reduce the effect of noise on the accuracy of an observer. First, a framework to implement low-pass filters in nonlinear observers is presented. Second, a novel internal-model filter to exactly cancel noise from a known generating model is proposed. Finally, results on the use of dynamic dead-zones are recalled and linked with the low-pass filter design framework.
Ball Detection and Tracking through Image Processing using Python

In this paper, the ball detection and tracking through image processing using python is presented. The Ball Detection and Tracking through Image Processing using Python is a project that aims to identify and track a ball in real-time using computer vision techniques. The project involves the use of a camera to capture the video of the ball, which is then processed by the software using OpenCV library in Python. The project comprises two stages, i.e., ball detection and tracking. In the first stage, the algorithm identifies the ball in the video frame by detecting the circular shape of the ball. The second stage involves tracking the ball's movement by using optical flow techniques. The software is designed to run on a Raspberry Pi or a PC, and it is capable of processing video in real-time. The output of the software is the position of the ball, which is displayed on the screen. The position can also be used to control other devices, such as a robot, to follow the ball's movement. The project has several applications, such as in sports analytics, where it can be used to track the movement of a ball in a game like football or basketball. It can also be used in industrial automation, where it can be used to track the movement of objects on a conveyor belt. Overall, the Ball Detection and Tracking through Image Processing using Python project is an excellent example of the application of computer vision techniques in real-time video processing. It has the potential to revolutionize various industries and open up new possibilities for automation and analytics.
Making Data Management Plans Machine Actionable: Templates and Tools

Since September of 2019, a task group within the European Open Science Cloud - EOSC Nordic Project, work-package 5 (T5.3.2), has focused its attention on machine-actionable Data Management Plans (maDMPs). A delivery working-paper from the group (Hasan et al. 2021) concluded in summary that extracting useful information from traditional free-text based DMPs is problematic. While maDMPs are generally more FAIR compliant, and as such accessible to both humans and machines, more interoperable with other systems, and serving different stakeholders for processing, sharing, evaluation and reuse. Different DMP tools and templates have developed independently, to a varying degree, allowing for the creation of genuinely machine actionable DMPs. Here we will describe the first three tools or projects for creating maDMPs that were central parts of the original task group mission. We will get into a more detailed account of one of these, specifically the Stockholm University &ndash; EOSC Nordic maDMP project using the DMP Online tool, as described by Philipson (2021). We will also briefly touch upon some other current tools and projects for creating maDMPs that are compliant with the RDA DMP Common Standard (RDCS), aiming for integration with other research information systems or research data repositories. A possible conclusion from this overview is that the development of tools for maDMPs is progressing fast and seems to converge towards a common standard. Nonetheless, there remains an immense amount of work to get there.
K-LR Modeling with Neural Economy and Its Utilization in Unclear Data

In this chapter, we explain the K-LR model, a cognitive hierarchical model that combines linear regression and k-means clustering to improve the interpretability and generalization performance of linear models. We explain the principles of cognitive hierarchy models and provide examples of how K-LR can be applied to logistic regression and supply chain optimization. We also created a dynamic K-LR model with stochastic gradient descent, which allows the model to adapt to changes in the data over time and improve its accuracy and generalization performance. Finally, we propose some possible extensions and alternatives to K-LR, e.g. B. The integration of nonlinear functions and deep learning architectures is discussed, as well as the limitations and trade-offs of these approaches.