Reviewerly/exHarmony huggingface dataset:
AutoDetect: A Novel Real-Time Intelligent Sensor Failure Detection for Connected Vehicles

This study addresses the need for developing new frameworks to monitor and detect sensor failures in connected commercial vehicles (CCV)s. The CCV’s sensor health is more important when performance predictions and other communication-related errors (e.g. cyber-physical attacks) can manipulate the sensory network’s resiliency. We developed a novel machine learning (ML)-based framework, AutoDetect, to equip the cloud-tied operators with tools for understanding the abnormal sensor data streaming from the vehicle on the cloud level which explains the sensor data errors due to sensor failures only. We developed an innovative autoencoder (AE) neural network algorithm coupled with K-means clustering to create patterns. To learn the relationship between operating samples and features, when streaming sensor data over high-dimensional datasets is collected in the United Kingdom (UK). Different profiles of sensor data are collected under various driving conditions to monitor the ground truth of the sensor’s confidence levels in CCVs. The new AutoDetect tracked real-time sensor failures with a minimum accuracy of 90%.
Design a Robust Real-Time Trash Detection System Using YOLOv5 Variants

The amount of garbage produced by individuals living the modern lifestyle is significantly higher than in the past. To preserve the planet, it is necessary to recycle this garbage; one of the first steps is to classify the garbage. Individuals have done the majority of the classification so far. Thus, automating this process will make it much faster and safer, and more trash will be recycled rather than polluting the environment. To detect and separate trash automatically, a large, balanced, and labelled dataset is required, which is currently not publicly available. As a result, a labelled and balanced trash dataset was collected for this study. The dataset includes 2,500 images of trash from five different categories (glass, paper, plastic, metal, and cardboard). Furthermore, the dataset was used to create a robust trash detection model using YOLOv5, a well-known real-time object detection algorithm. As a result, various YOLOv5 variant models were created. On the collected dataset, the model achieved a precision of 90.6%, a recall of 83.3%, a mAP (0.5) of 90.8%, and a mAP (0.5, 0.95) of 81.08%. Furthermore, to demonstrate the validity of the proposed system, a collection of 500 new images was collected and then external testing was performed to evaluate the system. The system produced the following results: 80.8%, 79.2%, and 74.2%, with confidence levels of 0.3, 0.5, and 0.7, respectively.
A Blockchain based framework for Lending Digital Assets implemented using NFT

In recent times, digital assets have been on the rise. Crypto-enthusiasts have been investing in this market segment for quite some time. The returns from these investments for the investors cannot be fathomed. Blockchain Technology is one of the main pillars of the Industry 4.0 revolution. As of now, the entities can buy or sell digital assets but the ability to lend/rent a digital asset is still a question that needs to be addressed. With this motivation, we have proposed a framework as a solution that will allow owners to lend their digital assets to buyers by determining the ownership rights of the asset dynamically. This research work presents the comprehensible approach to the buyer-seller protocol using NFT smart contracts and blockchain technology, to provide proof of ownership and secure transferability in the marketplace.
Transfer Learning-assisted Modulation Format Identification for Low OSNR

A simple and affordable transfer learning (TL) technique for digital modulation format recognition scheme is presented in an optical communication system. System configurations for four popular modulation formats i.e. QPSK, 8PSK, 16PSK, and 16QAM are created. Two distinct TL algorithms which are trained using constellation diagrams are used in the suggested approach to identify the modulation format. The produced results show that the suggested approach can successfully categorize all of these modulation forms over broad ranges of optical signal-to-noise ratio values with a prediction accuracy of 100%. Upcoming diverse optic communication systems can be analyzed using the suggested method with incredibly basic hardware, and the outputs of the analysis can aid with better management of optical networks without the need for hardware modifications.
Permissioned Blockchain Voting System Using Round-Robin

This paper proposes a voting system designed with an incentive-less, permissioned blockchain network for the people to minimize fraudulent activities in the voting system and conduct the transactions in a secure network. The 51% or the majority rule in the blockchain network makes it difficult to corrupt and thus becomes an immutable and transparent database for storing votes. The consensus protocol used in our blockchain network is a round-robin algorithm, which assigns transactions to miner nodes in a pseudo-random order. The user authentication with a pseudo key prevents a third party from directly knowing a particular user’s vote. The transaction pool is maintained for validating, verifying, and assigning transactions to nodes connected to it for the miners to mine the block and approve the transaction which is the vote in this scenario. The address is generated based on a private key and the signature is generated which can be used to validate transactions or votes in the transaction pool.
Particle Swarm Optimization for dietary recommendations

Recommender systems find relevant information that is correctly tailored to the consumers’ preferences. Diet recommender systems provide alternative food recommendations that not only satisfy the user’s interests but also adhere to the best dietary practices. This study introduces an adaptive meal recommendation system that provides consumers tailored, nutrientdense items that take into consideration both their preferences and needs. The proposed system uses a swarm intelligence based approach to generate alternate food recommendations for food that the user wishes to substitute in their diet, ensuring that their nutritional requirements are met and their behaviour in selecting food displays an upward trend. A recommendation is modelled as a particle in the swarm and the nutrient constituents are the dimensions. The binary particle swarm algorithm generates a set of solutions and over the iterations improves the global best solution which is the recommended alternative food item. The local and global acceleration coefficients are maintained as constants; the sigmoid function is used to update the position of each particle. The goodness of the recommendations of the system was evaluated by conducting surveys with clinicians and other users too. The recommendations of the system had 21.93 units higher fitness score compared to the recommendations of the clinicians. The system can be used by clinicians, dieticians, and also general users who seek alternative food for a food item.
Molecular Dynamics Simulation by Implementing Parallel Computing and Big Data Principles

Several researchers are now able to run large-scale molecular dynamics simulations that can mimic the behaviour of a large number of atoms thanks to the emergence of big data and improvements in computing power. Large amounts of data are produced by these simulations, which can be processed, saved, and analysed using big data and cloud computing technologies. These algorithms can be applied to optimise the behaviour of quantum systems as well as forecast the characteristics of materials. We discuss the implementation of molecular dynamics simulations and aim to study the computational time for different number of atoms in simulation of a parallel computing model based on molecular dynamics. Molecular dynamics is an essential tool for understanding molecular reactions, and we aim to study the computational methods to make it faster and more accurate. However, such simulations may be computationally inefficient and memory-costly. The proposed solution is to parallelise the simulation algorithm, which could be a small step towards a larger solution. We propose a Charm++ based parallel algorithm that helps process large amount of big data and may see potential success in quantum computing.
Finding Type 2 Diabetes Using Sparse Balanced Support Vector Machine in Electronic Health Records

Early Type 2 Diabetes (T2D) diagnosis is crucial for optimal integrated therapy and patient follow- up. There has been a huge growth in the quantity of accessible Electronic Health Record (EHR) data, and developments in Machine Learning (ML) methodologies have been as amazing. Maintaining and modelling a huge data set can cause over fitting, model interpretability, and computational expenditure. We used Sparse Balanced Support Vector Machine (SB-SVM) to detect type 2 diabetes in an original EHR dataset (named FIMMG dataset). In particular, we have chosen just those aspects of the EHR collected from a homogeneous group of participants before the diagnosis of T2D, including exemptions, examinations, and medicine prescriptions. The proposed solution is trustworthy compared to other ML and Deep Learning methods utilised by the state-of-the-art to handle this problem. The SB- SVM offers the best prediction performance- computation time tradeoff, according to the data. In addition, the induced sparsity makes the model more interpretable while automatically handling high-dimensional data and the normally uneven distribution of classes.
Deep Learning Technique for Recognition of Deep Fake Videos

New computing methods and digital content have been created thanks to recent advancements in digital media technology. They have also contributed to advancing recent AI-based innovations and provide straightforward instruments for producing real video changes. These "Deep Fakes" or fraudulent films might seriously jeopardise the public’s perceptions of a case or society. These films’ consequences on spreading fake news, particularly, are significant when they act as accurate depictions. These false films may, however, be created by manipulating software. Data protection, identifying deep fakes, and preventing media manipulation are just a few ways deep fake detection contributes to cybersecurity. In light of this, it is essential and mandatory to be able to spot this sort of misleading data. This paper examines the most promising new approaches to deep fake video detection by analysing the latest findings from the research community. It analysed the results from two research and proposed using convolutional neural networks and long short-term memory to distinguish fake from real video frames. The report suggested using these and other detection methods and the unique method for identifying deep fakes that used the YOLO face detector to distinguish facial video frames (YOLO-CNN-XGBoost) and suggested investigating other novel detection methods.
Novel Shoebill and Strategy on Combatting Terrorist inspired Optimization Algorithms for Real Power Loss Diminution

In this paper Shoebill optimization (SO) algorithm and Strategy on Combatting Terrorist inspired optimization (CO) algorithm applied for solving the real power loss reduction. Shoebill optimization (SO) algorithm pretends the performance and stratagem of Shoebill while confronting and stalking the prey to appraise candidate elucidations. This stalking stratagem is done through Stirring in the direction of prey which acts as exploration segment and Heading on the aquatic shallow is taken as exploitation segment. In the Shoebill optimization (SO) algorithm, the fresh location for a Shoebill is recognized if the rate of the objective functional value is amended in that location. Through this modernizing the procedure is barred from stirring to non–optimal zones CO algorithm is based on the vigorous drive of Anti–Terrorism Squad, in the course of Combatting the Terrorist. Every Anti–Terrorism Squad endlessly modernizes his location grounded on the locations of the Commander–in–chief of Anti–Terrorism Squad in the Combatting zone. In proposed procedure every Anti–Terrorism Squad member will vigorously passages in the direction of the best position. In the Grade Weight and Updating process the locations appraise of every exploration agent be contingent on the location of the Hostages, the communication between Commander–in–chief and Anti–Terrorism Squad members. Grade of each Anti–Terrorism Squad member imitates how adjacent the squad member (exploration agent) is to the objective (fitness rate).
Content-aware convex hull prediction

Efficient bitrate ladders can enhance on-demand streaming services when bandwidth is constrained. Moreover, content-agnostic bitrate-resolution pairs in fixed bitrate ladders can lead to inefficient compression of video contents. Convex hull, which encloses multiple Rate-Distortion (RD) curves generated for multiple resolutions, can be used to determine the optimal bitrate-resolution pairs for a given video sequence. While literature is abundant with the determination of Quantization Parameters (QPs) that correspond to the cross-over point between adjacent RD curves, rate-controlled encodings require prior knowledge of the target bitrate instead of the target QP during the encoding. To this end, this research predicts the target bitrates for a set of picture quality values thereby generating a content gnostic optimal convex hull for a given video sequence. To support this, a machine learning based two-stage prediction framework is applied to predict a pool of bitrates from the uncompressed source videos at their native resolutions. The first stage of the framework predicts spatio-temporal features in the compressed domain which are subsequently employed to predict bitrates in the second stage. Exploiting the predicted bitrates, multiple strategies are proposed to select the bitrate-quality pairs to generate the convex hulls. Ground truth convex hull and a state-of-the-art fixed bitrate ladder are further used to evaluate the performance of the proposed convex hull prediction. Extensive experiments conducted with x265 based encoding report minimal variance from the ground truth convex hull and significant benefits over the fixed bitrate ladder which reports over 30% compression losses.
CDN Performance Evaluation with Edge-Embedded Watermarking

Piracy of copyrighted video content is an everlasting concern for copyrights holders, such as major studios who want to protect their latest movies/series or video distributors who want to safeguard their exclusive rights investments. To prevent piracy, the video technology providers have designed solutions based on the concept of watermarking, i.e., the video frames contain marks, which can be reliably extracted by a watermark extractor, while remaining invisible for the human eye. Yet, distributing watermarked streams is hard to scale in practice: to prepare and deliver a unique watermarked stream to each user, the service providers should implement a specific system in their Content Delivery Network (CDN) [3].
Improving the Performance of Web-Streaming by Super-Resolution Upscaling

In recent years, we have seen significant progress in advanced image and video upscaling techniques, sometimes called super-resolution, or AI-based upscaling. Such algorithms are now broadly available in the forms of software SDKs, as well as functions natively supported by modern graphics cards. However, to take advantage of such technologies in video streaming applications, one needs to (a) add support for super-resolution upscaling in the video rendering chain, (b) develop means for quantifying the effects of using different upscaling techniques on perceived quality, and c) modify streaming clients to use such more advanced scaling techniques in a way that leads to improvements in quality, efficiency, or both.
Improving ABR Encoding by Adaptation to "True Resolution" of the Content

As well known, when the input video is upscaled, the effectiveness of its transcoding and delivery may suffer. The encoded stream may not look sharp and use more bits than necessary. Then with adaptive streaming, extra streams may be added to reach such a maximum resolution and bitrate. The result is a significant waste of storage, bandwidth, and compute resources. In this paper, we explain the origins of this problem, survey existing methods for addressing it, and then propose our solution. Our proposed design incorporates a novel "true resolution" detection technique and a traditional CAE (context-aware encoding) encoding ladder generator. The CAE generator receives the detected "true resolution" of content as a limit for resolutions to include in the ladder. Such a limit enables all subsequent savings. We describe the details of our proposed resolution detection method, bring examples explaining how it works, and then study the performance of our proposed system in practice. Our study, performed using 500 video assets representing 120 hours of real-world production material, confirms the effectiveness of this technique. It shows that in many practical cases, the incoming content is, in fact, upscaled and that adding a "true resolution" detector to CAE brings very appreciable savings in bandwidth, storage, and compute costs.
Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation Framework

We present an open-source cloud-based adaptive video streaming evaluation framework; a testbed that can be instantiated in a cloud infrastructure, examine multiple media players with different network profiles, and conclude the evaluation with statistics and insights into the result [3]. It has been used in multiple projects to evaluate the performance of media players' Adaptive Bitrate (ABR) algorithms and to conduct subjective evaluations to compare with known QoE models like ITU-T P.1203. We used this framework to address unanswered questions such as (i) the minimum noticeable duration for stall events in HAS; (ii) the correlation between the media quality and the impact of stall events on QoE; (iii) the end-user preference regarding multiple shorter stall events versus a single longer stall event; and (iv) the end-user preference of media quality switches over stall events [2].
Determining Video Complexity to optimise Video Quality Assessment

In this paper, we will present the results of our experiments with open-source encoders to understand the complexity of video content. We will show how the bitrate profile over time of an asset encoded in constant quality mode can be used as a robust estimate of content complexity. We will test three hypotheses: whether both Content Quality (CQ) and Constant Rate Factor (CRF) rate-control modes can be used interchangeably for complexity estimation, if a complexity estimate depends on the CQ/CRF parameter used and finally, the assertion that complexity depends on the upstream pre-processing applied to source content. A set of recommendations and parameters will be proposed that give a good estimate of content complexity. We will show how we used this technique to analyse video quality of statistical multiplexing systems, allowing us to efficiently process a large number of live sources and quickly pin-point potential deficiencies of the target statistical multiplexing implementation.
EEG-Assisted EMG-Controlled Wheelchair for Improved Mobility of Paresis Patients

A motorised wheelchair is a type of mobility aid that helps people with physical limitations get around independently. Historically, powered wheelchairs have been operated by joysticks, switches, or other input devices that require the user to operate physically. To promote mobility and freedom for those unable to use conventional wheelchair control methods, this research aims to develop an electroencephalogram (EEG) assisted electromyography (EMG) controlled wheelchair. The major scope of this research is divided into two sections: gesture controller (signal processing) and automated wheelchair design. The proposed system was evaluated using human test participants under approved ethical standards. The accuracy of the proposed system was achieved at 88% with a weighted average precision of 0.89.
Decentralized learning over a network with Nyström approximation using SGD

Nowadays we often meet with a learning problem when data are distributed on different machines connected via a network, instead of stored centrally. Here we consider decentralized supervised learning in a reproducing kernel Hilbert space. We note that standard gradient descent in a reproducing kernel Hilbert space is difficult to implement with multiple communications between worker machines. On the other hand, the Nyström approximation using gradient descent is more suited for the decentralized setting since only a small number of data points need to be shared at the beginning of the algorithm. In the setting of decentralized distributed learning in a reproducing kernel Hilbert space, we establish the optimal learning rate of stochastic gradient descent based on mini-batches, allowing multiple passes over the data set. The proposal provides a scalable approach to nonparametric estimation combining gradient method, distributed estimation, and random projection.
Research on semantic matching algorithm of BERT intelligent question answering system

With the rapid growth and widespread popularity of the Internet, people's access to information is diversified and convenient. The existing text representation methods have some problems, such as insufficient semantic information extraction, high dimension of representation model, and high complexity of model construction. In this paper, a semantic matching algorithm of intelligent question answering system based on BERT is proposed, and the semantic similarity of a concept at the next level pointed by the attribute is found under certain attribute matching rules. Finally, the concept similarity method is recursively called to calculate the similarity of each concept, so that the similarity of all concepts at all levels is weighted and integrated to obtain the semantic similarity between ontologies. The simulation results show that after the system is deployed, the ideal effect can be obtained by comparing the accuracy and response time of text selection. This shows that the improved method proposed in this paper can effectively improve the performance of BERT model, and then effectively reduce the model parameters and accelerate the pre-training speed under the same performance.
Application research of artificial intelligence in computer network technology

In the era of big data, artificial intelligence is widely used in computer network technology, which makes the functional and structural layers of computer network system comprehensively optimized. The use of natural language generation, speech recognition and virtual assistants make people's lives easier and faster. At the same time, the application of big data artificial intelligence has effectively strengthened the construction of computer network security system, and realized the high-end, intelligent and automatic operation of computer network. In this paper, the concepts of computer network technology and artificial intelligence technology are described in detail. And summarizes the advantages of using artificial intelligence technology in computer network technology and its specific application in computer network technology. Then it makes a detailed analysis and summary of its application strategy in computer network technology. In order to accelerate the development of artificial intelligence technology at the same time to improve the overall effect of computer network technology, so that it further towards the direction of big data development.