Reviewerly/exHarmony huggingface dataset:
Explainable Artificial Intelligence for a high dimensional condition monitoring application using the SHAP Method

In this paper, a new visualization-based method for understanding industrial machine learning models trained on high dimensional data is proposed. For a view that includes all dimensions, a dimensionality reduction towards a 2D projection, the UMAP method, is used. With the TreeSHAP algorithm the most important features for each machine condition are identified, visualized and evaluated. A closer look at different data points of the most important features provides more information about the behavior of the model. In addition, this knowledge is used to derive a class-optimized 2D visualization to increase trustworthiness of individual classification results for domain experts.
Survey: Neural Network Authentication and Tampering Detection

Neural networks have become quite the buzzword in a decade, resulting in extensive research and extensive integration of neural networks in application development. From self-driving vehicles to IoT devices, each such area has seen some form of integration of a neural network(s). Image and video content have found application in medical, forensic, etc. Due to the excessive use of digital content, there has also been a rise in various advanced image editing applications such as Photoshop, making it easier for people to tamper with images. Therefore, coming up with techniques to validate or authenticate images has gained much interest in recent times. Current neural network-based methods can see all kinds of tampering because neural network capability extracts complex features from the images, making them more effective. Thus, in this study, we review some image forgery techniques and look over how neural networks find their application to detect forgery and authenticate images.
A Survey Paper on Text Analytics Methods for Classifying Tweets

Social media has demonstrated to be a strong medium that makes a durable effect on the manner in which individuals communicate and has now turned into a fundamental piece of their lives. Especially with the COVID-19 pandemic, people have taken to social media to voice their thoughts and views on various topics. Twitter ranks highest amongst all the social media platforms, when one wants to exchange ideas and information or express their opinions in the form of text. Hence, tweets are often the most useful in generating a vast amount of data in text source, and hence, it is preferred as a text analytics platform. Text analytics is used for gaining deeper insights from a piece of unstructured text. Specifically, for tweets, it has various applications such as identifying patterns and trends or understanding the thought process of the users. This has resulted in techniques such as sentiment analysis, opinion mining, argument mining, stance detection, and many more. This survey paper discusses the various text analytics methods on tweets with a focused literature survey on sentiment analysis, opinion mining, argument mining, and stance detection.
Localization for PMTTA algorithm at sport events: from concept to application

During the last decade, the digital revolution has impacted all associated fields, professions and skills, and in particular team sport. Thus, digital tools and distributed databases continue to develop and arouse strong interest in automated summarization and semantic indexing of video content. In this paper we have offered a Player Multi-Target Tracking Algorithm (PMTTA) whose purpose is to examine the impact of the new requirements implemented for the indexing of video by content. This algorithm forms a recursive estimator of complete formations including all player positions. Knowing that, the estimate is advanced at the time of the current measurement sweep. Prediction of target locations is done according to a particular motion pattern.
Methodology of Evaluation of Low Cost Electronic Devices : Raspberry PI and Nvidia Jetson Nano for Perception System Implementation in Robotic Applications

In order to realize perception systems on low cost embedded electronic devices as Raspberry PI 3, 4, and Nvidia Jetson Nano, an evaluation methodology to test these devices is proposed. These systems are dedicated for robotics applications for consummer uses as domestic autonomous robots, and systems to help visually impaired people to move safely. The methodology consists in choosing representative low level vision algorithms and in evaluating them using various implementations. CPU and GPU implementations are evaluated. The interest of the methodology is the speed of the evaluation. Indeed, the implementation of the reference algorithms is much faster than the implementation of the algorithms of the targeted application. The coarse time scale, between the treatments chosen as a reference and those to be implemented for the targeted application, gives an idea of the possibility of use. This methodology is then performed to evaluate and to compare the three devices : Raspberry PI 3, 4, and Nvidia Jetson Nano. The results shows the interest of the use of the GPU. The easy access to the GPU, and the simplicity of programming in Cuda is a great advantage for the Jetson Nano.
Masked Faces Recognition Using Transfer Learning

Through this study, we present our study that aims to implement a masked face recognition model by using a transfer learning algorithm for facial recognition and masked faces datasets. To achieve the goal, we first implemented an algorithm built on a Facenet model. Our choices come down to the considerable Facenet's outcomes model When training on the well-known LFW database. As a result, he gave an accuracy rate that exceeds 98%. About masked faces, we used two different datasets that are Simulated Masked Faces Recognition Dataset (SMFRD) and Real world Masked Faces Dataset (RWMFD). Then we have established a performance comparison between the two datasets. Thereby, we tried to adjust our model to be robust against partial occlusions caused by wearing the mask. Relative to other state-of-the-art models, obtained outcomes on the Real-World-Masked-Face-Dataset display high recognition achievement.
Augmented reality and image processing solutions for sport events and training : principles and practice

Emergence of new information technologies (NTIC), cameras and video cameras have become ubiquitous. Image processing algorithms and applications are used in many fields, particularly in sports. This is the context of our article. In the first section, we will present the interest of using these applications for sports competitions. In the second section, the identification of players experienced in various applications using a tracking camera during sports competitions will be described. In the third section, a real-time distributed solution that we have proposed allowing to automatically building models of the actors followed thanks to its capacity to support the probabilistic fusion of various sources of information and to structure the incoming perceptions will be described. Finally, this article will end with the description of a system solution that we have proposed and which has been tested with real images.
Tuning PSO Parameters For the Path Planning Problem

Particle swarm optimization (PSO) is one of the most used metaheuristic algorithms in the field of robotics path planning, because of its simplicity to implement and to its high efficiency in resolving such a problem. In this paper we propose a parameter tuning approach for PSO algorithm to enhance its effectiveness, in order to obtain a reasonable solution in a space with obstacles. We will try to have the best quality solution, the shortest path avoiding obstacles in our case, with the least cost, which is the number of iterations and the number of particles.
Automatic Arabic Dialect Identification Using Deep Learning

The language characteristics of a given community are referred to as dialect. Automatic speech recognition, e-health, and other applications benefit from being able to recognize dialect effectively. As a result, dialect identification has grown in importance and popularity as a study area. In this paper, we present an approach for automatic Arabic dialect identification based on language recognition system. It consists to apply the fundamental frequency, the energy, and the mel frequency cepstrum coefficients parameters based on multi-scale product analysis. These parameters are applied as input features to a convolutional neural network architecture for the classification of the dialect of speech signal. We can observe the efficiency of the application of multi-scale product to determine the characteristics of dialect. Our approach is evaluated on five dialect data set. The Experimental results show that the proposed approach outperforms several state-of-the-art methods, and is capable to identify the dialect.
Identification of SNP mutations linked to melanoma via a CNN network: application to the FGFR2 gene

One of the deadliest diseases in the world is skin cancer, especially melanoma. Detection and identification of this type of cancer is very difficult due to the strong similarity of its morphological characteristics with the characteristics of other skin cancer types, even with nevi. Early detection of melanoma can help in increasing the cure rate by minimizing diagnostic time and saving efforts. Different systems from deep learning are actually developed to help dermatologists early diagnose melanoma. These systems use several types of data for the classification between melanoma and non-melanoma, such as dermoscopic images and DNA or RNA genetic sequences. This paper proposes a new work that aims at recognizing melanoma lesions from non melanoma ones. The method uses the convolutional neural networks. The data we consider here are the SNP protein sequences of the human FGFR2 gene. We transform, then, these SNP sequences from their textual form into scalogram images to be taken as input to the CNN classifier. The images are encoded in different color spaces with the goal of constructing four databanks. For quantitative evaluation, we use different evaluation rates such as: accuracy, precision, specificity, and sensitivity. The values attained are very satisfactory. These results show the performance of the proposed system and demonstrate the existing relationship between the gene FGFR2 and the possible small protein changes.
Real time face detection and identification from video sequences combining LBP algorithm and convolutional neural network

In this work, we propose an algorithm for face detection and recognition in real time applications. The proposed algorithm combines the local binary pattern (LBP) with the convolutional neural network (CNN) and it is split into two steps: The first step is the face detecting from the video sequence and the second one is the face identification after features extraction operation using the LBP algorithm. These two steps are performed using a convolutional neural network which is a tending type of neural network based on deep learning. Extensive experiments on several test datasets are conducted to evaluate the proposed method. In fact simulation results are very interesting and show the efficiency of the suggested method.
Deep Learning for Face Detection and Correction in Information Flow

Robust face recognition in information flow is among the most important interests of researchers in recent years. Facial feature detection and extraction play a very important role in the field of deep learning. The development of deep learning methods and models for facial detection and recognition leads to enhance the results for facial classification. This paper proposes a deep learning-based method for the detection and correction of facial images in an information flow. The proposed method is based on the histograms of face images generated using Local Binary Patterns algorithm, skewness coefficients, and morphological transformations applied in a Convolutional Neural Network framework. The experimental results conducted on Yalefaces database have shown that the proposed method allows to achieve a higher accuracy up to 99.98% with ResNet model.
MANAJEMEN BENCANA BANJIR DI KECAMATAN PANGEAN KABUPATEN KUANTAN SINGINGI

Pangean District is one of the sub-districts affected by flooding in Kuantan Singingi Regency. So that good flood disaster management is needed in order to reduce or minimize the risk caused by flood disasters. This study focuses on disaster management carried out by the Kuantan Singingi Regency Government, whether it is in accordance with the stages of disaster management, namely Pre-disaster, Emergency Response, and Post-disaster. so that this study is to find out how disaster management is carried out by the Kuantan Singingi Regency Government in tackling flood disasters. the theory used in this study is the theory of Ramli (2010:31) disaster management. There are three stages in managing a disaster, first there is a pre-disaster stage, the second is emergency response and the third is post-disaster. The research method used in this research is descriptive type qualitative method. The results of this study indicate that the stages of disaster management carried out by the Kuantan Singingi district government are starting from the pre-disaster where there are still shortcomings. When doing socialization, many people do not get this information. Then when a disaster occurs, there are shortages, namely logistical limitations and also disaster emergency personnel, and the last stage, namely post-disaster, there are still shortages where the form of rehabilitation carried out by the government is still not well targeted and lacks funds.
PENGARUH MEDIA PEMBELAJARAN SPARKOL VIDEOSCRIBE DALAM MENINGKATKAN HASIL PADAMATA PELAJARAN SISTEM KOMPUTER

The purpose of this study was to determine the effect of the Sparkol VideoScribe learning media in improving student learning outcomes in computer systems subjects with number systemmaterial. The research method used is the survey method. The population used in this study were students of class X Multimedia &#x0D; 1 SMK Manba'ul 'Ulum Cirebon with a sample of 40 students. The sampling technique used in this research is purposive sampling, in which a sampling technique is used with certain considerations. The data collection techniques used by the researcher are research (survey and observation), literature/documentation studies, questionnaires/questionnaires, and interviews. The results of the study show: 1) The application of sparkol videoscribe learning media in teaching and learning activities in the classroom, students who are assisted with learning media have a much more significant increase compared to conventional methods. That way, with sparkol videoscribe media, the material presented will be much more concrete and interesting which will produce outputs, one of which is increased learning outcomes; 2) Sparkol VideoScribe learning media has an effect on improving student learning outcomes. With that, there is a positive and significant relationship between the sparkol videoscribe learning media and student learning outcomes, where the value of R = 0.356 and R Square is 0.287 (0.536 x 0.536 = 0.287) which means that the contribution of Sparkol VideoScribe (X) on Student Learning Outcomes (Y) is 28.8%.
INFORMASI OBAT CAIR UNTUK ANAK MENGGUNAKAN AUGMENTED REALITY BERBASIS ANDROID

The purpose of this research is to know the Android liguid drug information for children using Augmented Riality base. In ApotikAbidfarmaparepare. This research showed that image processing methadthay applied by vuforia using this Aplication allowed for user to see liguid drug information in ApotikAbidFarmaparepare just shine the camera which provided to marker then push the information menu, composition, and indication in that marker. Acoording to this explanation abtained the research result like the limition form image processing used, where from pour samples to do, all of the test has cortion detection limits in minimal rang e 20 cm, and maximum range variesbetwen 30 cm – 35 cm, Because many factors like3 the things guality which detected and low shining factor. Beside that, also abtained results that the best degree for showing optimal resuit in 90 or the camera position in front of the object. Moreover, the level of user satisfuction showing positive respond to words use of the create application.
Experimenting With AI-Powered Learning Tools and Mobile-Assisted Learning to Improve Student Learning Performance, Autonomy, and Metacognitive Skills

Artificial intelligence can be defined as the capacity of a computer system to complete human activities (i.e., thinking, selecting, and learning) that as a norm may only be achieved by utilizing human intelligence. AI technology in education enables some flexibility and customization that was impossible so far. It is revolutionizing universities and lectures, making a lecturer's job considerably easier. It is poised to change education dramatically. The current study analyzed how Quizlet, a digital vocabulary learning platform, was used to support students as they tried to memorize various terms, theories, scholars, and researchers related to leadership and examine how learners felt about this application.
VESPo: Verified Evaluation of Secret Polynomials (with application to dynamic proofs of retrievability)

Proofs of Retrievability are protocols which allow a Client to store data remotely and to efficiently ensure, via audits, that the entirety of that data is still intact. Dynamic Proofs of Retrievability (DPoR) also support efficient retrieval and update of any small portion of the data. We propose a novel protocol for arbitrary outsourced data storage that achieves both low remote storage size and audit complexity. A key ingredient, that can be also of intrinsic interest, reduces to efficiently evaluating a secret polynomial at given public points, when the (encrypted) polynomial is stored on an untrusted Server. The Server performs the evaluations and also returns associated certificates. A Client can check that the evaluations are correct using the certificates and some pre-computed keys, more efficiently than re-evaluating the polynomial. Our protocols support two important features: the polynomial itself can be encrypted on the Server, and it can be dynamically updated by changing individual coefficients cheaply without redoing the entire setup. Our methods rely on linearly homomorphic encryption and pairings, and our implementation shows good performance for polynomial evaluations with millions of coefficients, and efficient DPoR with terabytes of data. For instance, for a 1TB database, compared to the state of art, we can reduce the Client storage by 5000x, communication size by 20x, and client-side audit time by 2x, at the cost of one order of magnitude increase in server-side audit time.
Modelling the Barriers of Artificial Intelligence Adoption in the Organisations: An Interpretive Structural Modelling and MICMAC Analysis

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.
Importance of Least Cybersecurity Controls for Small and Medium Enterprises (SMEs) for Better Global Digitalised Economy

Purpose: Small and medium enterprises (SMEs) are the most significant contributors to maximum employment generation, the gross domestic product (GDP) of many countries, and the overall global economy. It is also evident that cyber threats are becoming a big challenge for SMEs, which is directly impacting global economy.Methodology: Existing research inputs were accessed to understand current cyber threats for SMEs and their cybersecurity posture. Additionally, this research has collected the latest insights by taking direct inputs from SMEs and conducting a well-designed research survey. It has provided a few direct inputs to designing solutions for the SME segment. For analysis and recommendations, cybersecurity best practices and core cybersecurity concepts are considered at the centre of the solution.Findings: Implementing existing cybersecurity standards or frameworks is not easy for SMEs, as they generally have limited resources and different priorities for their business when it comes to the implementation of any cybersecurity controls. Currently, many cybersecurity standards are not able to support the implementation of business domain-specific controls.Practical implications: Along with the research findings shared in this chapter, as a resolution to the problems faced by SMEs, the authors will propose a new framework as a solution. This framework is designed using core concepts of cybersecurity such as confidentiality, integrity, and availability (CIA triad) as well as defence in depth (DiD) mechanisms in each layer of organisation. The authors will also share a high-level idea about how reliable artificial intelligence-based software can help identify recommended controls for particular SMEs.
Numerical verification of the existence of shock waves in trombones

The brassy timbre associated with loudly played trombone notes is due to the accumulated effects of nonlinear wave propagation. Hirschberg et al. experimentally showed in [J. Acoust. Soc. Am. 99, 1754–1758] that wave steepening of such tones can be strong enough to produce shock waves. In this paper, pressure measurements collected along the trombone bore are presented and then numerically simulated using a three-dimensional time-domain model. The numerical solutions reveal that shock waves do form within the computational instrument within the vicinity of the approximated shock distance.