Reviewerly/exHarmony huggingface dataset:
Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving

The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR point-cloud moving object segmentation (MOS) convolutional neural network (CNN) models are very complex and have large computation burden, it is difficult to perform real-time processing on embedded platforms. In this paper, we propose a lightweight MOS network structure based on LiDAR point-cloud sequence range images with only 2.3 M parameters, which is 66% less than the state-of-the-art network. When running on RTX 3090 GPU, the processing time is 35.82 ms per frame and it achieves an intersection-over-union(IoU) score of 51.3% on the SemanticKITTI dataset. In addition, the proposed CNN successfully runs the FPGA platform using an NVDLA-like hardware architecture, and the system achieves efficient and accurate moving-object segmentation of LiDAR point clouds at a speed of 32 fps, meeting the real-time requirements of autonomous vehicles.
Latest Advances on Security Architecture for 5GTechnology and Services

The roll out of the deployment of the 5G technology has been ongoing globally. The deployment of the technologies associated with 5G has seen mixed reaction as regards its prospects to improve communication services in all spares of life amid its security concerns. The security concerns of 5G network lies in its architecture and other technologies that optimize the performance of its architecture. There are many fractions of 5G security architecture in the literature, a holistic security architectural structure will go a long way in tackling the security challenges. In this paper, the review of the security challenges of the 5G technology based on its architecture is presented along with their proposed solutions. This review was carried out with some keywords relating to 5G securities and architecture; this was used to retrieve appropriate literature for fitness of purpose. The 5G security architectures are majorly centered around the seven network security layers; thereby making each of the layers a source of security concern on the 5G network. Many of the 5G security challenges are related to authentication and authorization such as denial-of-service attacks, man in the middle attack and eavesdropping. Different methods both hardware (Unmanned Aerial Vehicles, field programmable logic arrays) and software (Artificial intelligence, Machine learning, Blockchain, Statistical Process Control) has been proposed for mitigating the threats. Other technologies applicable to 5G security concerns includes: Multi-radio access technology, smart-grid network and light fidelity. The implementation of these solutions should be reviewed on a timely basis because of the dynamic nature of threats which will greatly reduce the occurrence of security attacks on the 5G network.
Prediction Map of Rainfall Classification Using Random Forest and Inverse Distance Weighted (IDW)

The amount of rainfall that occurs can affect natural disasters and even food production to economic activities. the factor of the area where the rain occurs is one of the main parameters for how the change occurs. So, it is necessary to have a rainfall prediction approach that aims to find out when and what type of rain will occur. Spatial classification and interpolation are two methods used to make predictions. Random Forest is a classification method that can be used to predict rainfall. and Inverse Distance Weighted is one of the stochastic interpolation techniques to calculate the estimated rainfall from the data points of rainfall that occur so that the distribution can be visualized. In the implementation of random forest, the model that is built on a daily basis gets the best level of accuracy in the 5D model sub model C with an accuracy of 0.8238 while the monthly model gets the best level of accuracy in the sub-model B 4M 0.9362. and the results of predictions and mapping using IDW show that daily predictions from June 1-4 2022 show that Most of Java Island will experience light rain, June 5-7 2022 most of Java Island will experience sunny cloudy days. And for monthly predictions, August and June 2022 show the distribution of monthly rainfall with predictions that most of Java is cloudy, while May, July, October, September have light rainfall in most of Java
A sustainable and secure load management model for green cloud data centres

Abstract The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emission, and security threats. In this context, a novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to enhance the security for users with sustainability for CDCs. The model estimates and reserves the required resources viz., compute, network, and storage and dynamically adjust the load subject to maximum security and sustainability. An evolutionary optimization algorithm named Dual-Phase Black Hole Optimization (DPBHO) is proposed for optimizing a multi-layered feed-forward neural network and allowing the model to estimate resource usage and detect probable congestion. Further, DPBHO is extended to a Multi-objective DPBHO algorithm for a secure and sustainable VM allocation and management to minimize the number of active server machines, carbon emission, and resource wastage for greener CDCs. SaS-LM is implemented and evaluated using benchmark real-world Google Cluster VM traces. The proposed model is compared with state-of-the-arts which reveals its efficacy in terms of reduced carbon emission and energy consumption up to 46.9% and 43.9%, respectively with improved resource utilization up to 16.5%.
RF-Chain

Blockchain-based supply chains provide a new solution to decentralized multi-party product management. However, existing methods, including ID-based and cryptographic-based solutions, cannot achieve both counterfeit resistance and decentralization in supply chain management. We argue that this dilemma comes from the disconnection and inconsistency of the data records and physical product entities. This paper proposes RF-Chain, a novel decentralized supply chain management solution that uniquely combines data record authentication and physical-layer RFID tag authentication to effectively achieve credibility and counterfeit resistance. The main contribution of this work is to integrate physical-layer authentication of cheap commodity RFID tags into a blockchain-based information management system, and RF-Chain is the first to do so. The proposed cross-layer authentication can effectively defend against counterfeit attacks without relying on a central key management service. Real-world experiments utilizing the Ethereum (ETH) platform and more than 100 RFID tags demonstrate that RF-Chain is secure, effective, time-efficient, and cost-efficient.
X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tremely large-scale</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ulti-mod</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ensor dataset</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X-MAS</i> ) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms.
Online Education Management: A Multivariate Analysis of Students’ Perspectives and Challenges during Online Classes

The aim of the present study is to find solutions for better management of online education, starting from students’ perspectives regarding the challenges they encountered in the last two years when online courses were imposed during the COVID-19 pandemic. The research methodology we used was partial least squares structural equation modelling based on data collected by applying a survey among students in Romanian universities. The novelty of our study consists in the proposed model, which has five variables: communication problems specific to online education, professors’ skill in conducting online classes, the quality of online education, the stress felt by students during online education, and the technical requirements of online education. The results revealed that despite challenges during online classes students benefited from a high-quality education because they had the support of their professors, all the educational resources that they needed, a device to connect from, and a very good internet connection. These findings are helpful for managers in the higher education system to create better educational strategies meant to satisfy the educational needs of students in the digital age.
RiDOP: A Rotation-Invariant Detector with Simple Oriented Proposals in Remote Sensing Images

Compared with general object detection with horizontal bounding boxes in natural images, oriented object detection in remote sensing images is an active and challenging research topic as objects are usually displayed in arbitrary orientations. To model the variant orientations of oriented objects, general CNN-based methods usually adopt more parameters or well-designed modules, which are often complex and inefficient. To address this issue, the detector requires two key components to deal with: (i) generating oriented proposals in a light-weight network to achieve effective representation of arbitrarily oriented objects; (ii) extracting the rotation-invariant feature map in both spatial and orientation dimensions. In this paper, we propose a novel, lightweight rotated region proposal network to produce arbitrary-oriented proposals by sliding two vertexes only on adjacent sides and adopt a simple yet effective representation to describe oriented objects. This may decrease the complexity of modeling orientation information. Meanwhile, we adopt the rotation-equivariant backbone to generate the feature map with explicit orientation channel information and utilize the spatial and orientation modules to obtain completely rotation-invariant features in both dimensions. Without tricks, extensive experiments performed on three challenging datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016 demonstrate that our proposed method can reach state-of-the-art accuracy while reducing the model size by 40% in comparison with the previous best method.
Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression

The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel “Flicker Test Software” is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.
Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems.
Protecting Data Integrity of Web Applications with Database Constraints Inferred from Application Code

Database-backed web applications persist a large amount of production data and have high requirements for integrity. To protect data integrity against application code bugs and operator mistakes, most RDBMSes allow application developers to specify various types of integrity constraints. Unfortunately, applications (e.g., e-commerce web apps) often do not take full advantage of this capability and miss specifying many database constraints, resulting in many severe consequences, such as crashing the order placement page and corrupting the store inventory data.
Medical image encryption techniques: a technical survey and potential challenges

&lt;span lang="EN-US"&gt;Among the most sensitive and important data in telemedicine systems are medical images. It is necessary to use a robust encryption method that is resistant to cryptographic assaults while transferring medical images over the internet. Confidentiality is the most crucial of the three security goals for protecting information systems, along with availability, integrity, and compliance. Encryption and watermarking of medical images address problems with confidentiality and integrity in telemedicine applications. The need to prioritize security issues in telemedicine applications makes the choice of a trustworthy and efficient strategy or framework all the more crucial. The paper examines various security issues and cutting-edge methods to secure medical images for use with telemedicine systems.&lt;/span&gt;
Towards more relevance in forensic science research and development

Many different issues have been identified in forensic science for more than 10 years. While quality management has often been suggested as a path forward, research is generally considered as an essential part of the solution. Through an overview of current forensic science research, this paper aims at evaluating if and how research answer the challenges forensic science is currently facing. While forensic related publications have massively increased over the years, approximately half of the publications were published in non-forensic sources, indicating that forensic science research tends to be led by other disciplines. Over the years, forensic science research has remained largely oriented towards methodological and technological development rather than relevance to the forensic science discipline and practice. Practical implementation of the techniques is rarely discussed from a forensic perspective, and thus research rarely move from the “proof-of-concept” stage to its utilisation in case investigation. The digital transformation also generated a massive increase of data, making it challenging to find the relevant pieces of information in the mass of “forensic” publications available on-line. Thus, we propose to refocus forensic science research on forensic fundamental and practical questions to strengthen the discipline and its impact on crime investigation and security issues. Our propositions represent an incentive to further discuss forensic science research and knowledge transmission through the definition of a common culture within the community, focusing on common fundamental knowledge such as a better understanding of the concept of trace and its case-based information content.
Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods

Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly based on PV time series has some advantages over solar irradiance forecasting first and PV power modeling afterwards. In this paper, the power forecasting for BIPV systems in a vertical façade is studied using machine learning algorithms based on decision trees. The forecasting scheme employs the skforecast library from the Python environment, which facilitates the implementation of different schemes for both deterministic and probabilistic forecasting applications. Firstly, deterministic forecasting of hourly BIPV power was performed with XGBoost and Random Forest algorithms for different cases, showing an improvement in forecasting accuracy when some exogenous variables were used. Secondly, probabilistic forecasting was performed with XGBoost combined with the Bootstrap method. The results of this paper show the capabilities of Random Forest and gradient boosting algorithms, such as XGBoost, to work as regressors in time series forecasting of BIPV power. Mean absolute error in the deterministic forecast, using the most influencing exogenous variables, were around 40% and close below 30% for the south and east array, respectively.
Anti-spoofing-enabled Contactless Attendance Monitoring System in the COVID-19 Pandemic

The Global Novel Coronavirus Disease-2019 (COVID-19) pandemic has forced social distancing norms that have been followed worldwide. Thus, traditional biometric-based attendance marking systems are replaced with contactless attendance marking schemes. However, there are limitations of manufacturing cost, spoofing attacks, and security vulnerabilities. Thus, the paper proposes a contactless camera-based attendance system with the equipped functionalities of anti-spoofing. The proposed scheme can detect liveliness, so fake attendance marking is eliminated. The proposed scheme is also scalable and cost-effective, with generic solutions adaptable to schools, colleges, or other places where attendance is required. The system also eliminates the limitation of one-entry by multiple face-marking systems that allow simultaneous attendance marking. In performance analysis, parameters like image precision, storage cost, retrieval latency, and analysis of the anti-spoofing module is presented against existing schemes. An accuracy of 95.85% is reported for the model, with a significant improvement of 33.52% in storage cost through the Firebase database, which outperforms existing state-of-the-art schemes.
A Non-Inductive Magnetic Eye-Tracker: From Dipole Tracking to Gaze Retrieval

We analyze the information that can be retrieved from the tracking parameters produced by an innovative wearable eye tracker. The latter is based on a permanent-magnet marked corneal lens and by an array of magnetoresistive detectors that measure the magnetostatic field in several positions in the eye proximity. We demonstrate that, despite missing information due to the axial symmetry of the measured field, physiological constraints or measurement conditions make possible to infer complete eye-pose data. Angular precision and accuracy achieved with the current prototypical device are also assessed and briefly discussed. The results show that the instrumentation considered is suitable as a new, moderately invasive medical diagnostics for the characterization of ocular movements and associated disorders.
Parallel Machine Learning Algorithm

To expedite the learning process, a group of algorithms known as parallel machine learning algorithms can be executed simultaneously on several computers or processors. As data grows in both size and complexity, and as businesses seek efficient ways to mine that data for insights, algorithms like these will become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are just some of the methods described in this article for speeding up machine learning algorithms. We also cover the benefits and threats associated with parallel machine learning, such as data splitting, communication, and scalability. We compare how well various methods perform on a variety of machine learning tasks and datasets, and we talk about the advantages and disadvantages of these methods. Finally, we offer our thoughts on where this field of study is headed and where further research is needed. The importance of parallel machine learning for businesses that want to glean insights from massive datasets is emphasised, and the paper provides a thorough introduction of the discipline.