Reviewerly/exHarmony huggingface dataset:
A Short Review on New Development Achievements and Market Opportunities in Unmanned Systems

Unmanned systems are fundamental robotics technologies that, in the widest sense, do not require human components unless they have missions, may be operated remotely or autonomously, and perform specific responsibilities. Since they are not swayed by fear, tiredness, fury, frustration, or revenge, autonomous robots would behave more ethically than humans in warfare. Developers are simplifying control algorithms to let autonomous ground vehicles choose a job, operating condition, and route. Accurate slippage predictions and correcting mechanisms will help create a safer and more effective navigation system, along with other improvements. Software systems such as artificial intelligence and image processing should be researched for unmanned aerial vehicle systems development. In this chapter, the market opportunities have been assessed for UAV systems.
Onboard Trajectory Coordination of Multiple Unmanned Air Vehicles

The increasing proliferation of autonomous air vehicles (AAV) and their applications has created an increased need for effective aircraft trajectory coordination within airspaces. Here, a method to coordinate a flight formation of multiple AAVs using onboard command and control is introduced. The proposed system has been verified in simulation and validated in flight. The results show that it is possible to control a fleet of multi-rotor aircraft using equipment present onboard the aircraft. Areas of improvement to increase accuracy and reliability of the system have also been proposed.
Citizen Science for Disaster Risk Governance: Towards a Participative Seismological Monitoring of the Mayotte Volcanic Crisis

The United Nations (UN) Sendai Framework for Disaster Risk Reduction 2015–2030 aims to mitigate natural disasters, specifically in developing regions. It promotes the adoption of people-centered disaster risk reduction approaches. Hence, citizen science represents an interesting tool to engage populations in the mitigation of disaster risk, through data collection and analysis, and in the dissemination of scientific and safety information. Herein, we evaluate the potential and feasibility of a citizen science project on the island of Mayotte (in the Mozambique Channel). Mayotte has been experiencing an unexpected volcano-seismic crisis since 2018, which has generated strong anxiety in the population. To address this, we have developed a citizen seismology program to engage Mayotte’s inhabitants in seismic data processing. First, we conducted an initial test of our protocol to identify seismic events with a set of university students. We then conducted 15 interviews with members of local administrations and associations to assess the potential for engaging the general population in this project. The results show that we are able to collect reliable data from citizens with non-professional backgrounds using the protocol designed in the project. We also show a strong demand for scientific information from Mayotte’s inhabitants, associated with a robust trust in science and scientists, despite the circulation of alternative explanations for the seismicity among the population. Based on these results, our citizen science project could be positively received by Mayotte’s inhabitants, if advertised adequately. Finally, we discuss the value of these results for disaster risk reduction in vulnerable territories.
The Rise of Deepfake Technology

Deepfake technology is an emerging technology prevailing in today's digital world. It is used to create fake videos by exploiting some of the artificial intelligence (AI) based techniques and deep learning methodology. The facial expressions and motion effects are primarily used to train and manipulate the seed frame of someone to generate the desired morphed video frames that mimic as if they are real. Deepfake technology is used to make a highly realistic fake video that can be widely used to spread the wrong information or fake news by regarding any celebrity or political leader which is not created by them. Due to the high impact of social media, these fake videos can reach millions of views within an hour and create a negative impact on our society. This chapter includes the crucial points on methodology, approach, and counter applications pertinent to deep-fake technology highlighting the issues, challenges, and counter measures to be adopted. Through observations and analysis, the chapter will conclude with profound findings and establishes the future directions of this technology.
Handwriting Analysis AI-Based System for Assisting People with Dysgraphia

Dysgraphia is a learning disability of written expression, which affects the ability to write, mainly handwriting and coherence. Several studies have proposed approaches for assisting dysgraphic people based on AI algorithms. However, existing aids for dysgraphia take only one aspect of the problem faced by the patients into consideration. Indeed, while some provide writing assistance, others address spelling or grammatical problems. In this paper, a novel system for helping people suffering from dysgraphia is proposed. Our system tackles several problems, such as spelling mistakes, grammatical mistakes and poor handwriting quality. Further, a text-to-speech functionality is added to improve the results. The proposed system combines a plethora of solutions into a valuable approach for efficient handwriting correction: handwritten text recognition using a CNN-RNN-CTC model, a spelling correction model based on the SymSpell and Phoneme models, and a grammar correction using the GECToR model. Three machine learning models are proposed. The experimental results are compared based on the values of Character error rate, Word error rate, and the workflow of three handwritten text recognition models, and has led to an improvement of the quality of the results.
IoT-Based System for Crop Protection From Animals

No one can imagine a world without agriculture. Due to overpopulation and deforestation, animal interference in residential areas is increasing day by day affecting human life and property, and causing human-animal conflict. At the present time, farmers in India and other countries are facing a serious threat from wildlife animals which causes less production in crop yielding. It is not possible to stay in the field for 24 hours to guard the crops against animals. In spite of good production, the crops on farms are many times ravaged by animals and it leads to huge losses for the farmers in terms of production, effort, quality, and money. To overcome this problem, an IoT-based crop protection system is being proposed in the present work. The system uses a microprocessor and a set of sensors to detect wild animals approaching near the field. The sensor data is analyzed through a machine learning algorithm and helps the microprocessor to take action. This system helps farmers to protect their crops from animals.
RuMedSpellchecker: Correcting Spelling Errors for Natural Russian Language in Electronic Health Records Using Machine Learning Techniques

The incredible advances in machine learning have created a variety of predictive and decision-making medical models that greatly improve the efficacy of treatment and improve the quality of care. In healthcare, such models are often based on electronic health records (EHRs). The quality of this models depends on the quality of the EHRs, which are usually presented as plain unstructured text. Such records often contain spelling errors, which reduce the quality of intelligent systems based on them. In this paper we present a method and tool for correcting spelling errors in medical texts in Russian. By combining the Symmetrical Deletion algorithm and a finely tuned BERT model to correct spelling errors, the tool can improve the quality of original medical texts without significant cost. We have evaluated the correction precision and performance of the presented tool and compared it with other popular spelling error correction tools that support Russian language. Experiments have shown that the presented approach and tool are 7% superior to existing open-source tools for automatically correcting spelling errors in Russian medical texts. The proposed tool and its source code are available on GitHub $$^{1}$$ and pip $$^{2}$$ repositories( $$^1$$ https://github.com/DmitryPogrebnoy/MedSpellChecker $$^{2}$$ https://pypi.org/project/medspellchecker ).
Design of inception ResNet V2 for detecting malarial infection using the cell image captured from microscopic slide

Over the past decades, malarial infection is considered a dreadful disease which ruins the lives of millions of people all over the globe. Several research works were developed based on machine learning algorithms to categorize the malarial infected person. However effective prediction with precise results is not attained in conventional approaches. For accurate prediction of malarial transmission deep learning technology is designed in this proposed model. This proposed design utilized inception ResNet V2 for the prediction of malarial-infected individuals. Before disease prediction, certain pre-processing techniques such as noise removal, contrast enhancement and segmentation are used to minimize the error rate during the classification process. The function of the proposed model is evaluated using metrics such as accuracy, recall, precision, etc. The simulation analysis shows that the proposed method obtains 0.98% accuracy, 0.02% error, precision is 0.92%, specificity is 0.94% so on. Thus the designed model predicted the malarial disease effectively.
Named Entity Recognition for De-identifying Real-World Health Records in Spanish

A growing and renewed interest has emerged in Electronic Health Records (EHRs) as a source of information for decision-making in clinical practice. In this context, the automatic de-identification of EHRs constitutes an essential task, since their dissociation from personal data is a mandatory first step before their distribution. However, the majority of previous studies on this subject have been conducted on English EHRs, due to the limited availability of annotated corpora in other languages, such as Spanish. In this study, we addressed the automatic de-identification of medical documents in Spanish. A private corpus of 599 real-world clinical cases have been annotated with 8 different protected health information categories. We have tackled the predictive problem as a named entity recognition task, developing two different deep learning-based methodologies, namely a first strategy based on recurrent neural networks (RNN) and an end-to-end approach based on transformers. Additionally, we have developed a data augmentation procedure to increase the number of texts used to train the models. The results obtained show that transformers outperform RNN on the de-identification of Spanish clinical data. In particular, the best performance was obtained by the XLM-RoBERTa large transformer, with a strict-match micro-averaged value of 0.946 for precision, 0.954 for recall and 0.95 for F1-score, when trained on the augmented version of the corpus. The performance achieved by transformers in this study proves the viability of applying these state-of-the-art models in real-world clinical scenarios.
Forensics Analysis of NTFS File Systems

The internet and computers are reaching everywhere, and all are getting connected through it. Users are utilizing computers to make life easier and work faster. At the same time, many attacks and instances of cybercrime have happened. Therefore, digital forensics is necessary and plays a crucial role. NTFS is one of the most popular file systems used by the Windows operating system, and this chapter provides information for forensic analysis of NTFS file system. This chapter describes digital forensics, stages of digital forensics, and types of digital forensics. NTFS is discussed in brief along with the master file table (MFT). In the same section, it also discusses the method to detect the hidden data in the boot sector, analysis of registry, prefetch, shellbags, and web browsers. They have discussed the collection of volatile and non-volatile data. It also provides the artifacts which an investigator must be seeking, along with the tools used to collect and analyze them and strategies used for investigation and analysis. Data recovery and file carving are also discussed.
Block size, parallelism and predictive performance: finding the sweet spot in distributed learning

As distributed and multi-organization Machine Learning emerges, new challenges must be solved, such as diverse and low-quality data or real-time delivery.In this paper, we use a distributed learning environment to analyze the relationship between block size, parallelism, and predictor quality.Specifically, the goal is to find the optimum block size and the best heuristic to create distributed Ensembles.We evaluated three different heuristics and five block sizes on four publicly available datasets.Results show that using fewer but better base models matches or outperforms a standard Random Forest, and that 32 MB is the best block size.
The Critical Impact of Cyber Threats on Digital Economy

Internet users need to know that there are many different kinds of threats in the online world. Improving cyber security and keeping private information safe is important for a country's safety and economy. For a country's safety and economy, it's important to improve cyber security and keep private information safe. The term “digital economy” refers to a business for digitally delivered goods and services that are created using electronic business models and linked to a global system of economic and social networks. Cyber risk is the most complex issue of the twenty-first century, arising from a wide diversity of causes such as a hacker, terrorists, criminals, insider groups, foreign states, etc. This study includes cyber threats in the digital world. It emphasizes challenges in the healthcare sector, agriculture and food industries.
Cloud Computing Cyber Threats and Vulnerabilities

Cloud computing has become an integral part of modern business operations, with organizations of all sizes and in all industries turning to the cloud to improve efficiency, reduce costs, and increase flexibility. However, the adoption of cloud computing has also introduced a range of new security challenges, as sensitive data and critical systems are moved to cloud-based infrastructure that may be managed by third parties. This chapter discusses the various types of cyber threats that can occur in the cloud, including data breaches, malware, and cloud-based attacks, as well as the measures that organizations can take to protect themselves against these threats on their data, infrastructure, and applications. This chapter examines also the role of cloud providers in securing data in the cloud, and the importance of implementing strong security measures to protect against these threats. By understanding the nature of these threats and the steps that can be taken to mitigate them, organizations and individuals can more effectively protect themselves and their sensitive data in the cloud.
Computer-aided recognition and assessment of a porous bioelastomer in ultrasound images for regenerative medicine applications

It is difficult to use a single edge operator in image processing to extract continuous and accurate contours of a porous bioelastomer due to the fuzzy boundary and complex background in ultrasound images. To solve this problem, this paper proposes a joint algorithm for bioelastomer contour detection and a texture feature extraction method for monitoring the degradation performance of bioelastomers. First, the mean-shift clustering method is utilized to obtain the clustering feature information of bioelastomers and native tissue from manually segmented images, and this information is used as the initial information in the image binarization algorithm for image partitioning. Second, Otsu's thresholding method and mathematical morphology are applied in the process of image binarization. Finally, the Canny edge detector is employed to extract the complete bioelastomers contour from the binary image. To verify the robustness of the proposed joint algorithm, the results using the proposed joint algorithm, where mean-shift clustering is replaced with k-means clustering are also obtained. The proposed joint algorithm based on mean-shift clustering outperforms the joint algorithm based on k-means clustering, as well as algorithms that directly apply the Canny, Sobel and Laplacian methods. Texture feature extraction is based on the computer-aided recognition of bioelastomers. The region of interest (ROI) is set in the scaffold region, and the first-order statistical features and second-order statistical features of the greyscale values of the ROI are extracted and analysed. The proposed joint algorithm can not only extract ideal bioelastomers contours from ultrasound images but also provide valuable feedback on the degradation behaviour of bioelastomers at implant sites.
An adaptive mean denoising algorithm for pepper and salt noised image

To improve the quality of the pepper and salt noised image, an adaptive mean filter algorithm is proposed. Firstly, the appropriate filter window size is automatically selected according to the intensity of noise in the image. Secondly, whether each pixel is a pepper and salt noise point is detected . The noised point outputs the mean gray value of the unnoised pixels in the current window. The non-noised point outputs the original value. Thirdly, the denoising effect of the three algorithms was compared by simulation experiments, and the influence of different pepper and salt noise density on denoising effect was tested. From the objective and subjective judgement, the propose method achieves higher PSNR and better visual effect.
Real-time clothing detection networks for surveillance videos

Real-time clothing recognition is a useful technique for describing individuals from the video stream captured by surveillance cameras. In most cases, surveillance cameras are shot from a high angle and a long distance, so the clothing objects in the captured images are small and blurred. Traditional object detection models are designed to locate obvious and clear objects and then classify them into different categories. They are not sensitive enough to detect or even neglect small and blurry clothes in surveillance camera images. To this end, we propose an effective real-time clothing detection model- Multi-Scale Tiny attention-based networks, called MuST. We also collect 5000 surveillance camera images and artificially annotate clothes into 112 categories to build an accurate benchmark dataset for clothing recognition. In particular, we develop a special tiny decoupled prediction head that helps to detect small and fuzzy clothes more accurately. Moreover, clothes are usually sheltered or affected by distracting environments, which may negatively affect the classification accuracy. Therefore, we introduce the novel multi-scale concatenation module to integrate and contrastingly analyze the information of the clothing objects and their local environment. Finally, MuST can better localize and classify small and fuzzy clothing objects. Experimental results on real-world collected clothing recognition datasets prove that MuST achieves the best recognition accuracy among all real-time object recognition models. Moreover, MuST achieves optimal inference speed among all real-time and non-real-time object recognition models.
An improved GhostNet for unsafe driving behavior algorithm

The existing unsafe driving behavior detection algorithms are difficult to meet the requirements of good real-time performance and high precision. This paper proposes an improved GhostNet unsafe driving behavior detection algorithm. The algorithm uses the Ghost convolution operator to reduce the complexity of the algorithm and improve the real-time performance. In addition, a dual-scale time adaptive module (DCTAM) is designed to extract the temporal information. Through fusing spatial and temporal information to improve the accuracy of detecting unsafe driving behaviors. The experimental results show that the algorithm in this paper reduces the computational complexity and stabilizes the detection accuracy.
Image restoration method based on adaptive multiple priors fusion in scattering scenes

Image is usually characterized by low contrast, blurry detail and distorted color due to complex imaging mechanism of scattering scene, which veils many valuable image information. To improve images quality in scattering scenes, we propose a novel image restoration method based on adaptive multiple priors fusion, which mainly includes two components: a multiple priors constraint-based transmission estimation component and an adaptive multiple priors fusion-based backscattered light estimation component. Firstly, we proposed a scoring formula by fusing lightness prior, contrast prior and saturation prior to locate the backscattered light, which can effectively avoid the limitation of using any single priors. Afterward, we explore a new prior called extended dark channel prior (EDCP), and then adaptive combining EDCP, dark channel prior (DCP) and underwater dark channel prior (UDCP) to estimate the transmission robustly. Finally, Extensive experiments on images of different scattering scenes demonstrate that the proposed method is effective and superior for image restoration.
MR image reconstruction via non-local attention networks

As an advanced medical imaging technology, magnetic resonance imaging (MRI) has great advantages and application potentials in medical clinical diagnosis. However, since the long scanning time and the artifacts caused by patient movements, the imaging results are always not satisfactory. Therefore, accelerating MRI and improving the imaging quality are the key problems. In this work, we propose a novel deep network that combines the U-net architecture with non-local attention blocks for MRI reconstruction. We employ the U-net to construct the basic network. The non-local attention is exploited to capture the remote dependencies in MRI images which calculates the weighted average of the remaining multiple location features as the value of the response location. The U-net has limitations in capturing long-term dependencies, however, the non-local attention can solve this problem well. Furthermore, we develop the residual module to better retain the detail information. The proposed model is compared with some recent leading MRI reconstruction methods, including the state-of-the-art deep learning-based methods. Compared with these methods, the proposed residual non-local attention network provides superior MRI reconstruction results and retains better perceptual image details.