Reviewerly/exHarmony huggingface dataset:
EHML: An Efficient Hybrid Machine Learning Model for Cyber Threat Forecasting in CPS

Cyber-attacks have risen to prominence as the world’s most serious issue. The rise in cyber-attacks also introduces several new cyber crimes. Cyber-attack detection and prevention are challenging jobs. However, several investigators have developed Artificial intelligence-based cybercrime detection and prevention security models in their past research. Machine Learning (ML) methods play a vital role in cybercrime detection and classification. The most challenging part of machine learning techniques is identifying a suitable model to describe the training data set. This research presents an efficient hybrid ML model (EHML) for cybercrime detection and classification. The proposed EHML model utilizes supervised learning for crime detection and unsupervised learning-based data reduction technique to construct the perfect cyber crime analysis model. The proposed EHML model selects all the relevant and essential features by utilizing an enhanced decision tree (EDT) strategy and Enhanced Local Outlier Factor (ELOF) Technique. The EDT method is based on an enhanced feature selection concept that recursively eliminates all the essential characteristics of the dataset. The ELOF Technique helps to determine all the anomalies and outlier data from the dataset. This research utilizes Kaggle online cybercrime dataset to verify the performance of proposed EHML and existing ML methods, i.e., SVM, J-48, and Random forest. Experimental findings demonstrate that the proposed EHL model obtains an accuracy rate of 95.02%, a precision of 95.01%, a recall of 94.89%, and an F1-score of 95.89 %, which is 10 % better than existing ML methods.
Integrated Blockchain and AI Research Infrastructure for IoT Based Applications

The Internet of Things (IoT), that has undertaken a revolution over the course of decades, is now a vital part of several application fields, which include smart towns and smart shipping, which guarantee that human existence runs more dependably. These applications are created to make the world a better place for people to live. As more information has to be acquired, saved, and analyzed, there will be an increased need for a system that has the capability of carrying out these tasks in a manner that is more efficient. Machine learning will play a significant role in the fulfillment of these requirements. However, the use of AI will also result in the utilization of considerable tools, which are necessary in order to handle the scenario that is now occurring. Because of this, support for blockchain has been included so that it may establish a decentralized architecture. This is done to guarantee that AI can function in an unimpeded manner. The potential advantages of artificial intelligence might also be realized via the use of block chain, which will provide a risk-free setting in which users can trade information and resources. The purpose of the proposed research is to develop a layered architecture that combines the benefits of blockchain technology with those of artificial intelligence in order to make it easier to analyze vast amounts of data. We carried out an investigation, and the results were analyzed in order to arrive at a conclusion on the impact that blockchain technology has on artificial intelligence with regard to latency and accuracy. According to the results, the proposed design not only outperforms the alternatives but also has the potential to provide solutions to a number of the issues that are now being experienced.
Integration of Machine Learning Algorithms for E-Learning System Course Recommendation Based on Data Science

A big database is mined for hidden predictive information in a process known as data mining. This information can then be used for a variety of commercial purposes, including bioinformatics and e-commerce. There are three different data mining algorithms: association rules, classification, and clustering. The behaviour of students who are interested in a specific collection of courses can be recognized with the help of the course recommender system. For a particular collection of information, we get information on course enrolment. We use learning management systems like Moodle for gathering this data. Following data collection, several combinations of data mining algorithms are used, such as classification and association rule algorithms, clustering and association rule algorithms, mining in classified and clustered data, integrating clustering and classification algorithms in association rule algorithms, or just the cluster analysis algorithm. As various machine learning algorithms, we employ ADTree classification, Simple K-means, and Apriori Association Rules in this research. So, in order to determine the optimum algorithmic combination for proposing courses to students using online learning, we provide five distinct ways. We compare the outcomes of this combined strategy with those of using simply the association rule algorithm, and we propose the optimum algorithmic combination for recommending courses in online learning based on our simulation.
metaAR – AR/XR Shopping App using Unity

Reports say that around 3.5 billion people will choose and use mobile augmented reality applications in 2022. The next outbreak in the technology system is going to be in augmented reality (AR) and virtual reality (VR). The revolution in the domains of AR and VR is tremendous and is bringing a lot of enthusiasm within the people. This paper focuses the study on the emerging XR and AR technologies and their use cases in marketplace such as shoe stores. For VR technology the main aim is to emphasize on the sense of reality, so the users cannot distinguish between the real and the virtual environments. As metaverse is relatively a new technology, we have experimented by setting up a shoe shore in a virtual environment. This research examines how brands would be able to set their store in the metaverse and how the users would be able to interact with the products. We have presented a concept metaAR which will allow brands to host their virtual stores in the metaverse starting with a prototype of a shoe store.
Blockchain-enabled Device Authentication and Authorisation for Internet of Things

The Internet of Things (IoT) is the network of multiple devices known as “things” which includes sensors, security cameras, smart lights, smart TV, traffic lights etc. in the smart home or industrial environment. In many applications, these IoT devices are installed in open areas for example traffic lights/ security cameras in a smart city. Strong authentication and authorisation for these devices need to be deployed to ensure trust among IoT networks. IoT devices produce and forward security-sensitive data and hence confidentiality, authentication and proper authorisation should be the primary priority of an IoT system. Implementing Certificate Authority-based digital certificate solutions is costly because of the number of devices involved in IoT networks. Blockchain is a decentralized ledger-based technology which can help to provide seamless yet cost-effective solutions for confidentiality, authentication, and authorisation for IoT environments. A blockchain-based system for device registration, authentication, authorisation, and data confidentiality is proposed. The paper shows the methodological and procedural details of the proposed security scheme.
Design and Performance Analysis of a Grid-Connected Solar Power System for Energy Efficient AR Building

In 2020, nearly all Saudi Arabia’s electricity generation was fueled by natural gas (61%) and crude oil (39%). As part of Saudi Arabia’s vision 2030, the Saudi government plans to diversify fuels to increase crude oil exports and reduce carbon dioxide emissions. In Saudi Arabia, the solar irradiance averages 5.2 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /day, photovoltaic (PV) technology is being embraced to achieve green growth and increase power generation. As a result of the technology’s proximity to the point of consumption, it ensures a continuous supply of energy while reducing the country’s transmission and distribution losses. A key parameter affecting the performance of PV panels in a photovoltaic system is the solar radiation incident on the panel. The tilt and azimuth angles of PV modules are two important factors in designing the PV system for the best performance. This study starts by utilizing Excel software to calculate the azimuth angle for the best adjustment of solar modules. Then, PVSyst software is used to design and simulate a grid-connected PV system for the Admission and Registration Building (AR) at Effat University in Jeddah. The study compares the solar system’s performance for mono-crystalline, poly-crystalline, and thin-film photovoltaic modules. The simulation results showed the effectiveness of the design in terms of the produced energy, meeting the estimated needs of the AR building, the save CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , and the annual savings using Saudi Arabia’s current electricity tariffs.
Transformers in Natural Language Processing

This chapter presents an overview of the state-of-the-art in natural language processing, exploring one specific computational architecture, the Transformer model, which plays a central role in a wide range of applications. This architecture condenses many advances in neural learning methods and can be exploited in many ways: to learn representations for linguistic entities; to generate coherent utterances and answer questions; to perform utterance transformations, a major application being machine translation. These different facets of the architecture will be successively presented, which will also allow us to discuss its limitations.
Learnable Loss Balancing in Anchor-Free Oriented Detectors for Aerial Object

Oriented object detection plays an important role in aerial image interpretation. Image processing speed is also essential due to massive amounts of aerial images. Anchor-free oriented detectors with fast processing speed are generally accepted despite the absence of pre-set anchors, contributing to their performance gap with anchor-based detectors. Most anchor-free oriented detectors are carefully designed by defining samples according to target characteristics, which require substantial prior knowledge, to realize improved performance. This study proposes an anchor-free oriented detector (termed as rfpoint) that requires minimal prior knowledge. Moreover, this study mainly aims to introduce a dynamic sample definition strategy. This strategy is modeled as a dynamic regulating process, wherein the classification and box regression interact until the model converges. A rotating quality-driven loss (RQDL) and adaptive-weight box loss (AWBL) are also proposed to realize the aforementioned process. RQDL redefines positive and negative attributes of samples according to the distribution of rotating Intersection-over-Unit (IoU) between predictions and ground truth. AWBL adjusts the importance degree of candidate samples in the box regression through classification scores. The proposed method is then tested on three mainstream aerial image datasets (DOTA, DIOR, and HRSC2016). Results reveal that the proposed method achieves the best performance compared with other oriented detectors, whose mAP are 79.92%, 70.88%, and 90.67%. Moreover, the method maintains the inference speed advantage of anchor-free detectors. An effective sample definition method can bridge the performance gap of anchor-free oriented detectors without minimizing inference speed.
Real-Time Monocular Depth Estimation Merging Vision Transformers on Edge Devices for AIoT

Depth estimation is requisite to build 3D perceiving capability of artificial intelligence of things (AIoT). Real-time inference with extremely low computing resource consumption is critical on edge devices. However, most single-view depth estimation networks focus on the improvement of accuracy running on high-end GPUs, which go opposite to the real-time requirement on edge devices. To address this issue, this article proposed a novel encoder-decoder network to realize real-time monocular depth estimation on edge devices. The proposed network merges semantic information at global field via an efficient transformer-based module to provide more details of the object for depth assignment. The transformer-based module is integrated in the lowest level resolution of an encoder-decoder architecture to largely reduce the parameters of the Vision Transformer (ViT). In particular, we proposed a novel patch convolutional layer for low-latency feature extraction in the encoder and a SConv5 layer for effective depth assignment in the decoder. The proposed network achieves outstanding balance between accuracy and speed on the NYU Depth v2 dataset. A low RMSE of 0.554 and a fast speed of 58.98 FPS on NVIDIA Jetson Nano device with TensorRT optimization are obtained on NYU Depth v2, outperforming most state-of-the-art real-time results.
GloveSense: A Hand Gesture Recognition System Based on Inductive Sensing

Gesture recognition, in particular, hand gesture recognition, has received great attention in the field of human–computer interaction (HCI). Although several hand gesture recognition designs have been proposed in the past, they still suffer many limitations in practice. Hence, we present a wearable device, called GloveSense, which is made on a regular glove using conductive threads. The system consists of ten coils, with each pair of coils sewn on both sides of each finger and connected in series. To achieve high sensitivity, a data acquisition system is employed, in which a tank circuit made of each sewn coil is measured. Since the utilized commercial data acquisition board has limited channels, a switching mechanism is employed to measure some of the extra coils. The utilized switching mechanism reduces the crosstalk effect between the readings. The GloveSense system is tested for the recognition of numbers 1–10 in American Sign Language (ASL). A dataset of 1400 responses is employed along with a machine learning algorithm (MLA) for gesture recognition. The proposed system demonstrates a high accuracy of 99% and a short recognition time of less than 1 s.
No-Reference Light Field Image Quality Assessment Exploiting Saliency

In the near future, the broadcasting scenario will be characterized by immersive content. One of the systems for capturing the 3D content of a scene is the Light Field imaging. The huge amount of data and the specific transmission scenario impose strong constraints on services and applications. Among others, the evaluation of the quality of the received media cannot rely on the original signal but should be based only on the received data. In this direction, we propose a no-reference quality metric for light field images which is based on spatial and angular characteristics. In more details, the estimated saliency and cyclopean maps of light field images are exploited to extract the spatial features. The angular consistency features are, instead, measured with the use of the Global Luminance Distribution knowledge and the Weighted Local Binary Patterns operator on Epipolar Plane Images. The effectiveness of the proposed metric is assessed by comparing its performance with state-of-the-art quality metrics using 4 datasets: SMART, Win5-LID, VALID 10-bit, and VALID 8-bit. Furthermore, the performance is analyzed in cross-datasets, with different distortions, and for different saliency maps. The achieved results show that the performance of the proposed model outperforms state-of-the-art approaches and perform well for different distortion types and with various saliency models.
IoT and Blockchain Integration Challenges

By 2025, more than 96% of products will have Internet of Things (IoT) components at their core, the rapid spread of IoT inspires the development of novel applications for the end user, illustrating just how dominant this technology will become. However, if proper safety measures are not taken, serious issues may arise, such as the victimization of innocent people in crimes like burglary caused by the unauthorized disabling of a smart system. Blockchain, a distributed ledger system, is rapidly becoming a match in the field of data transfer with security. The Internet of Things (IoT) network will get the benefit from blockchain technology, which makes IoT possible to build trust in distributed networks without the need for a governing body to intervene. In this article, we focus on the most significant challenges that have still to be solved in order to achieve integration in between IoT and the Blockchain.
STDFormer: Spatial-Temporal Motion Transformer for Multiple Object Tracking

Mainstream multi-object tracking methods exploit appearance information and/or motion information to achieve interframe association. However, dealing with similar appearance and occlusion is a challenge for appearance information, while motion information is limited by linear assumptions and is prone to failure in nonlinear motion patterns. In this work, we disregard appearance clues and propose a pure motion tracker to address the above issues. It dexterously utilizes Transformer to estimate complex motion and achieves high-performance tracking with low computing resources. Furthermore, contrastive learning is introduced to optimize feature representation for robust association. Specifically, we first exploit the long-range modeling capability of Transformer to mine intention information in temporal motion and decision information in spatial interaction and introduce prior detection to constrain the range of motion estimation. Then, we introduce contrastive learning as an auxiliary task to extract reliable motion features to compute affinity and introduce bidirectional matching to improve the affinity computation distribution. In addition, given that both tasks are dedicated to narrowing the embedding distance between the motion features of the tracked object and the detection features, we design a joint-motion-and-association framework to unify the above two tasks in one framework for optimization. The experimental results achieved with three benchmark datasets, MOT17, MOT20 and DanceTrack, verify the effectiveness of our proposed method. Compared with state-of-the-art methods, the proposed STDFormer sets a new state-of-the-art on DanceTrack and achieves competitive performance on MOT17 and MOT20. This demonstrates the advantage of our method in handling associations under similar appearance, occlusion or nonlinear motion. At the same time, the significant advantages of the proposed method over Transformer-based and contrastive learning-based methods suggest a new direction for the application of Transformer and contrastive learning in MOT. In addition, to verify the generalization of STDFormer in unmanned aerial vehicle (UAV) videos, we also evaluate STDFormer on VisDrone2019. The results show that STDFormer achieves state-of-the-art performance on VisDrone2019, which proves that it can handle small-scale object associations in UAV videos well. The code is available at https://github.com/Xiaotong-Zhu/STDFormer.
Pandemic Outbreak Time: Evaluation of Public Tweet Opinion by Machine Learning

In this work, a Twitter data-set was utilized to do sentiment analysis of people's thoughts on the corona-virus (COVID-19) period, which is a major concern throughout the world these days, impacting a number of nations. To better understand people's feelings about the epidemic, machine learning approaches (mla) and sentiment methodology such as Bert Model (BMO), Naive_Bayes_Bernoulli (nBB), Multi Nominal Naive_Bayes (mnNB), Support_ Vector_Machine (svM), Logistic_Regression (IR), Gradient_Boosting_ Classifier (gbR), Decision Tree Classifiers (dtC), K N eighbors(knN) and Random Forest Classifier (rfC) have been presented in this work. Also, we have classified that which Classifiers provides highest accuracy. Additionally, in this paper, we also analysis from the data set, the most that has been tweeted (hashtag), positive, negative as well as neutral with data visualization in the Covid-19 epidemic time.
Towards Sustainable Serverless Processing of Massive Graphs on the Computing Continuum

With the ever-increasing volume of data and the demand to analyze and comprehend it, graph processing has become an essential approach for solving complex problems in various domains, like social networks, bioinformatics, and finance. Despite the potential benefits of current graph processing platforms, they often encounter difficulties supporting diverse workloads, models, and languages. Moreover, existing platforms suffer from limited portability and interoperability, resulting in redundant efforts and inefficient resource and energy utilization due to vendor and even platform lock-in. To bridge the aforementioned gaps, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. In this paper, we briefly introduce the Graph-Massivizer platform. We explore how the emerging serverless computing paradigm can be leveraged to devise a scalable graph analytics tool over a codesigned computing continuum infrastructure. Finally, we sketch seven crucial research questions in our design and outline three ongoing and future research directions for addressing them.
Development criterion of estimating hourly global solar radiation for all sky conditions in China

The high quality hourly global solar radiation data becomes more and more important under the trend of low building energy consumption and carbon emission. The models based on daily global solar radiation and hourly meteorological parameters are two kinds of efficient methods to compensate for measured data deficiency. However, the applicable climate and sky conditions of two models are needed to be clarified. Based on 45 stations in China, the coupling correlations between hourly global solar radiation and meteorological parameters are obtained by correlation analysis. The eight decomposition models and four models based on the main hourly meteorological parameters are developed and evaluated for climates and sky conditions using the mean absolute bias error, relative root mean squared error, and Nash-Sutcliffe efficiency coefficient. Considering the advantages of two kinds of models, the criterion of estimating hourly global solar radiation data is developed. The results show that hourly sunshine duration and hourly surface temperature are the main factors reflecting the change of hourly global solar radiation. In humid and sub-humid climates, the hourly relative humidity can further characterize the fluctuation of hourly global solar radiation. CPR model performs best among decomposition models, especially in sunny sky conditions. The meteorological parameters models own higher accuracy in cloudy conditions. In regions with hourly sunshine duration measurement, CPR model is recommended when daily clearness index is higher than 0.7 and lower than 0.2. When daily clearness index varies from 0.2 to 0.7, the model should be established by selecting hourly sunshine duration and hourly surface temperature in arid and semi-arid climates and hourly sunshine duration, hourly surface temperature and hourly relative humidity in humid and subhumid climates, respectively. In regions without hourly sunshine duration measurement, CPR model should be used regardless of weather conditions and climate types.