Reviewerly/exHarmony huggingface dataset:
A Survey on Different Techniques for Anomaly Detection

The majority of current video surveillance systems rely on human operators to examine video material for any unexpected activity. Many applications, notably in the surveillance business, rely heavily on activity detection. When there is a lot of data to analyze, this strategy is not very useful. In this approach, analysis is usually done only after anything goes wrong. The automated way to analyzing and detecting suspicious conduct, on the other hand, can assist to rapidly and efficiently discover any such anomalous activity and may even offer warning before a major fatality occurs. In order to identify a button in the event of an emergency, such video surveillance systems require algorithms that are trustworthy, rapid, and resilient. Human operators will no longer be required to follow moving objects or analyze unexpected activities. This will be of great assistance to human operators, whose job will be made much easier by the fact that they will just have to click the panic button through each video frame for examination. In addition, the reaction time is greatly lowered. Video capture, recognizing moving objects, tracking, and identifying anomalous behavior in entry prohibited areas are the four phases in the fundamental method to autonomous video surveillance. The paper here discusses the various researches done under the abnormal activity detection.
Real-Time Quick Fog Removal Technique for Supporting Vehicles on Hilly Routes Amid Dense Fog

Particularly on a mountainous route, dense fog significantly increases the impression of risk when driving. Hilly locations make it difficult for vehicles to see. Due to heavy fog, it might be difficult to see neighboring objects clearly or to distinguish the roadways from them. Numerous traffic accidents are brought on by the strong fog, which also slows down moving vehicles. This article describes a real-time, quick fog method based on eyesight. It is suggested to use video processing techniques to get a clear image of the steep road during heavy fog. The suggested method is based on a dynamic patch applied after using a least-flattering technique and an atmospheric-based scattering model through individualized histogram averaging based on color channels. The ambient light is first calculated using a dynamic patch and the least-filtering approach every 6000 frames. Scenes with no fog are then restored when the transmission map has been approximated. Not least, independent histogram equalization is color channel dependent. Using the appropriate contrast adjustment, remove the hazy noise. The recommended technique may quickly provide any motorist with a clear view of roadways and any distant object. Several live exploratory experiments are performed on highways with hills and strong fog. The proposed technique offers improved performance compared to the most recent approaches, according to qualitative and quantitative test analysis. Defogging increases vision over 92% under heavy fog, with a high accuracy of 97.4%.
Deep Sea Debris Detection Using YOLOIncep Network

Marine debris usually has elements that are difficult to degrade and have a negative impact on the environment. Remote-controlled underwater vehicles can remove trash from the deep ocean, but the efficiency of the detection algorithm used by these vehicles plays a significant role in its success. The proposed work describes the YOLOIncep network, a novel and efficient deep sea debris detection model. The proposed work uses the advantage of both YOLO and the inception network for deep sea debris detection. The effectiveness of the proposed work is evaluated using three categories from the publicly available JAMSTEC data set. The performance of the work is compared to other existing models, and it is observed that YOLOIncep outperforms other YOLO models, with a MAP @ 0.5 of 0.979.
Hybrid Machine Learning Approach for Sentiment Analysis of Amazon Products: A Survey

Sentiment analysis (SA), also referred as Information Extraction, is among the research areas that is active and quickly increasing, which makes it difficult to keep up with all of the works there. It aims to investigate how people’s opinions and attitudes towards various subjects, events, problems, people, and things are conveyed via social media (such as blogs, forums, and social networking sites) through text evaluations or comments. The biggest online store in the world, Amazon, is an example of one that permits its consumers to freely rate its items and submit reviews. Assessing such reviews to determine whether they are positive or negative will help customers make decisions ranging from purchasing a product such as a webcam, cellphone, to compose a film critic and buy assets. Sentiment analysis has captured the interest of both scientific and market researches in the domains of natural language processing and machine learning. Machine learning in general includes supervised and unsupervised algorithms to train and test data. In this paper, recent studies of sentiment analysis using supervised machine learning and hybrid supervised machine learning methods are surveyed which will help gain insight into how the existing methods can be improved. We propose a model where covariance in input data in preprocessing can be reduced.
A novel deep learning driven robot path planning strategy: Q-learning approach

As the basis of mobile navigation technology, path planning has attracted the attention of the majority of scholars. In this paper, the deep learning framework is integrated into Q-learning, and a Deep Q-Network (DQN) algorithm based on memory optimised mechanism is designed to improve the convergence of DQN, so that the robot can carry out good path planning in complex environment. Experimental results show that the proposed method has a good performance in path planning. Experimental results show that in the case of multi-round training using the algorithm proposed in this paper, the path planning steps of the robot are the shortest and the total training time and single round time of the robot in path planning are also the shortest.
Leveraging business experience and knowledge to boost entrepreneurial achievement

The purpose of this study is to establish a correlation between specific organisational factors and entrepreneurial success. The study emphasises the significance of business experience and knowledge in contributing to successful entrepreneurship. The study employs an ANOVA test and multiple regressions to verify the predictive value of variables, using a convenience sample of 314 Lebanese companies. The results of the study indicate that entrepreneurs have a higher success rate when they have an entrepreneur in their family, possess managerial experience, and have unique knowledge. Additionally, the study highlights the importance of working with employees who have unique knowledge, as this can contribute to achieving greater entrepreneurial success. The findings have practical implications for organisations funding companies and individuals planning to start their own businesses. Specifically, this study can provide organisations with valuable insights into the factors that contribute to entrepreneurial success, making it easier to determine one's chances for success. Individuals who are planning to start their own businesses can also benefit from these findings by prioritising the development of specific organisational factors, such as business experience and knowledge, in their entrepreneurial endeavours.
Epidemiological and therapeutic profile of progressive forms of multiple sclerosis followed up at a based reference center in the state of Ceará

Introduction: Multiple sclerosis (MS) is an inflammatory, immune-mediated disease of the central nervous system, characterized by multifocal areas of demyelination. There is a knowledge gap in understanding the disease, particularly the progressive forms in different regions of the country, such as the Northeast. Objectives: To report the profile of progressive MS at a reference center in Ceará. Methods: This observational and cross-sectional study was conducted through a review of medical records and interviews at a Reference Center for Demyelinating Diseases in the city of Fortaleza, Ceará. Results: From 283 patients with MS diagnosis according to the 2017 McDonald criteria, 36 (12.7%) had progressive phenotype, 20 (56%) secondary progressive and 16 (44%) primary progressive. From those 36 patients currently followed up, 25 (69.4%) were women, 19 (52.8%) were caucasian and 18 (50%) were high educated. The median follow-up time was five years and the peak prevalence was between the 4th and 5th decades. Regarding disability, 27.8% already used support in the first assessment (EDSS 6.0), and in the last assessment, considering the last EDSS recorded, the percentage of patients in need of support almost doubled: 52.8%. Regarding the first disease-modifying drugs (DMD), there was a prevalence of interferons, considering that it was the most used class in the 1990s. Considering the current DMD, there is a prevalence of monoclonal antibodies (47%), with Natalizumab (25%) and Ocrelizumab (22%). In a comparative analysis, there is a drop in the use of interferons (8%). Conclusion: Understanding the clinical-epidemiological profile of MS patients with a progressive phenotype is essential to guide the propaedeutic reasoning, guise the conduct and restructure the dynamics of health services.
AI, Competition and Antitrust Policies

FAANG is a widely used and very current acronym in the Western world. It stands for Facebook, Apple, Amazon, Netflix and Google. This can be contrasted with the acronym BAT (Baidu, Alibaba and Tencent) in China.
A Multi-View Semantic Autoencoder Algorithm for Human-Centric Text Sentiment Analysis

In the artificial intelligence-empowered Internet of Things (IoT), the diversification of data collection makes the collected text data to be more complex together with the diversified characteristics of multidescription. Technically, such data are generally referred to multi-view data in text sentiment. How to mine and utilize the complementary and consistent knowledge to perform sentiment classification for text data is a core issue to be solved in multi-view learning. In this study, a multi-view semantic autoencoder (MVSA) algorithm is proposed based on semantic autoencoder model and label relaxation strategy. MVSA algorithm learns the mapping function from sample space to semantic space for maintaining the consistency of each view. This semantic space not only contains the discriminative information of text data, but also contains the information of the class relationships in the semantic space. The relaxation strategy and low-rank constraint on the label matrix ensure the compactness of predicted label and inter-class separation as large as possible. Finally, the test sample can be classified by various classifiers. The experimental results show the MVSA algorithm achieves high classification performance, which proves the effectiveness of the proposed algorithm in text sentiment analysis.
Workflows on Generating Geometric Patterns with Creative Coding

Observe the geometric pattern, and analyze it to distinguish its constituent repeating motif.
Visualization Comparison of Vision Transformers and Convolutional Neural Networks

Recent research has demonstrated that Vision Transformers (ViTs) are capable of comparable or even better performance than convolutional neural network (CNN) baselines. The differences in their structural designs are obvious, but our understanding of the differences in their feature representations remains limited. In this work, we propose several techniques to achieve high-quality visualization of representations in ViTs. Both qualitative and quantitative experiments show that our technical improvements can observably improve ViT visualization quality compared to previous studies. Furthermore, we conduct visualizations to explore the disparities between ViTs and CNNs pre-trained on ImageNet1K, revealing three intriguing properties of ViTs: (a) ViT feature propagation retains image detail information with minimal loss, whereas CNNs discard most image details for class discrimination. (b) Different from CNNs, object-related features do not show in ViT higher layers, suggesting that class-discriminative features may not be required for ViT classification. (c) Our visualization-assisted texture-bias experiment reveals that both ViTs and CNNs exhibit texture bias, of which ViTs seem to be more biased towards local textures.
COUNTERFEIT PRODUCT DETECTION USING BLOCKCHAIN TECHNOLOGY

Fake Products is a serious issue, as in today’s world we can’t really distinguish between real and fake products. And some people make these fake products just to make some profit without thinking about their impact on users and also affecting the company’s name, profit, and sales. Blockchain technology can be used to detect whether the product is real or fake and assure users about the authenticity of the product. Blockchain is a trending technology and a lot of applications are using this technology. Blockchain technology is the technology where information is stored in the form of blocks in many databases which are connected with the chains and it doesn’t require any third-party users' permission. The benefit of blockchain is that it is immutable and secure. It is decentralized and distributed. We can use Quick Response (QR) code or an encrypted unique code which is a very efficient technique to detect fake products. When the QR code is scanned or the unique code is entered, it will redirect us to the blockchain containing the information of the product and provides us with the details of the manufacturer and information of the owner to make the decision easy for the buyer if they are looking to buy the product.
Development of Image Annotation Tool by using Region Grow Algorithm

Image annotation conjointly called as automatic picture tagging or linguistic compartmentalization. It is the method through which computing systems mechanically provide the information within the style of keywords to an image. Several techniques are planned for picture annotation from the previous decades that provide enforcement on common place datasets. However, most of those works fail to match their ways with easy baseline techniques to justify the necessity for advanced models and subsequent coaching. In this paper, we propose a region grow algorithmic program for development of image annotation tool. This method uses low-level model options and a straight forward collection of the distances to find out closest homogenized pixels of a given picture and mix one another to make a region of image.
Noise influence on recurrent neural network with nonlinear neurons

The purpose of this study is to establish the features of noise propagation and accumulation in a recurrent neural network using a simplified echo network as an example. In this work, we studied the influence of activation function of artificial neurons and the connection matrices between them. Methods. We have considered white Gaussian noise sources. We used additive, multiplicative and mixed noise depending on how the noise is introduced into artificial neurons. The noise impact was estimated using the dispersion (variance) of the output signal. Results. It is shown that the activation function plays a significant role in noise accumulation. Two nonlinear activation functions have been considered: the hyperbolic tangent and the sigmoid function with range form 0 to 1. It is shown that some types of noise are suppressed in the case of the second function. As a result of considering the influence of coupling matrices, it was found that diagonal coupling matrices with a large blurring coefficient lead to less noise accumulation in the echo network reservoir with an increase in the reservoir memory influence. Conclusion. It is shown that activation functions of the form of sigmoid with range from 0 to 1 are suitable for suppressing multiplicative and mixed noise. The accumulation of noise in the reservoir was considered for three types of coupling matrices inside the reservoir: a uniform matrix, a band matrix with a small blurring coefficient, and a band matrix with a large blurring coefficient. It has been found that the band matrix echo networks with a high blurring coefficient accumulates the least noise. This holds for both additive and multiplicative noise.
The Effects of Global Citizenship, Vocational Value, Cruise Crew’s Vocational Image on Career Identity:For aspiring young individuals seeking employment in the service industry

The aim of this study was to investigate the direction of career guidance for young job seekers by examining the relationships between global citizenship, vocational values, cruise crew vocational image, and career identity. A survey was conducted on young job seekers who prefer the service industry, and the measurement variables were analyzed for their structural relationships. The results indicate that, first, social responsibility and global competence, among the dimensions of global citizenship, did not have a significant effect on vocational values. Second, global civic engagement had a significant positive effect on vocational values. Third, vocational values had a significant positive effect on cruise crew
Selection of pedagogical tools based on the efficiency indicators for methodological practices of teaching mathematics to middle school students with academic failure risks

The aim of the study is to substantiate that in order to implement efficient methodological practices of teaching mathematics to middle school students with academic failure risks, it is necessary to refer to certain indicators for the selection of appropriate pedagogical tools. The paper considers the parameters of assessing the efficiency of pedagogical tools for teaching mathematics offered in scientific-pedagogical literature. The scientific novelty of the study lies in the formulation of the notion of a “methodological practice of teaching mathematics” and the identification of the efficiency indicators for methodological practices of teaching mathematics to students with academic failure risks. As a result, the efficiency indicators for methodological practices of teaching mathematics to students with academic failure risks, which are proposed as guidelines for the selection of appropriate pedagogical tools, were identified and characterized.
Research on policy option for the expansion of play culture in early childhood: Focusing on the perception of play

본 연구는 우리나라의 유아기 조기교육에 대한 인식으로 놀이가 학습의 수단으로써 인식되는 경향과교육 및 보육 현장에서 놀이와 쉼보다 문자지도 및 특별활동으로 유아기 놀이문화가 퇴색되고 있는 상황속에서 유아의 놀이문화 확산을 위한 정책방안의 일환으로 놀이에 대한 인식을 알아보는데 그 목적을 두고있다. 이를 위해 우리 사회를 구성하고 있는 유아, 부모, 교사, 정책가 등 다양한 주체들의 놀이에 대한본질적인 인식을 알고자 메타포 분석과 초점집단면담(FGI)을 실시하였다. 메타포 분석의 결과 부모님의경우 놀이는 필수적이면서 동시에 성장이라고 생각했으며 교사는 필수적이면서 즐거운 것, 정책가의 경우근본이면서 즐거운 것이라고 인식하고 있었다. 초점집단면담(FGI)의 결과 유아의 놀이문화 확산을 위해서는놀이에 대한 경험을 기반으로 한 놀이인식에 대한 사회적 합의가 놀이문화 확산을 위한 정책적 지원에영향을 줄 것이라고 생각하였다. 즉, 놀이에 대한 경험이 다양하게 나타났으며 이러한 차이로 인하여 가정과기관에서는 놀이실천의 어려움을 갖고 있었다. 따라서 놀이문화 확산을 위한 실천으로 놀이에 대한 사회적소통과 합의를 위한 시간이 필요하며 놀이사례공유 등의 통합적 차원에서의 주체별 아카이브 구성이 필요함을 이야기하였다. 이러한 결과는 향후 유아의 놀이문화 확산을 위한 다양한 정책을 수립할 때 보다 더 근본적인 차원에서 접근함으로써 사회적 공감을 얻을 수 있으며 유아놀이에 대한 중요성을 인식함으로써 유아놀이문화 확산을 위한 재원을 확보하고 사회적 통합의 기틀을 마련할 것이다.The purpose of this study is to investigate the perception of play as part of a policy plan for the spread of play culture among young children in a situation where play is being recognized as a means of learning due to the awareness of early childhood education in Korea, and the fact that the play culture in early childhood is fading due to the use of text guidance and special activities rather than play and rest in education and childcare settings. To that end, we conducted metaphor analysis and focus group interviews (FGI) to understand the essential perception of play among various stakeholders, including young children, parents, teachers, and policymakers. The results of the metaphor analysis showed that parents considered play as essential and a form of growth, teachers recognized play as essential and enjoyable and policymakers recognized play as fundamental and enjoyable. The results of the FGI suggested that a social consensus on the perception of play based on experiences of play would influence policy support for the expansion of the play culture of young children. In other words, due to the differences in the various experience of play, there was difficulty in implementing play practices at home and in institutions. Therefore, we suggested that social communication and consensus-building time for play and the establishment of archives for different stake holders on an integrative level such as sharing examples of play, are needed for the expansion of the play. These results indicate that by taking a more fundamental approach when developing policies for the future spread of children's play culture, we can gain social understanding and recognition of the importance of children's play. This will enable us to secure resources for the expansion of children's play culture and lay the groundwork for social integration.
Neural-Network-Based OFDM-SNM Detection: Emerging Technique of Future Data Communication

Neural Network-based Orthogonal Frequency Division Multiplexing–Subcarrier Number Modulation (OFDM-SNM) bit detector is developed for exploiting the signals at the receiver. Fully connected layers of Neural networks are employed with deep learning to recover the signals in the OFDM-SNM. The pre-processed data is essential to map the received signal with the estimated signal with known channel state information. The channel vectors are formulated with the knowledge of the channel characteristics. Data sets are randomly generated using Pseudo Random Sequence Generator (PRSG) and simulated OFDM-SNM data sets are taken as samples. The system is trained with $${10}^{3}$$ data samples with 20 batch to optimize the Bit Error Rate. Simulation optimizes the BER rate more equivalent to conventional Maximum Likelihood (ML) detector and outperform OFDM Greedy Detector (GD). Simulation shows BER perform better with ML in 10 dB and 15 dB SNR with different data rates of 1 and 2 bps respectively.
Hashtag2vec: Ranking of Disaster Related Tourism Spot’s Hashtag on Twitter Data

The ever expanding disasters based tourism spots industry is drawing attention of folks through social networking sites, as people are travelling to corners of the world and encouraging many to fulfill their dreams. Our work is based upon retrieving the people experiences shared on twitter’s hash tag and automatically generating the rank of disasters related tourist spots in search which are safe, easily approachable and economical as per the tourist reviews. In this paper, our work is divided into data collection and filtration and then using deep learning approaches, LSTM such as word2vec, tweet2vec, hash tag2vec and NCA for feature extraction and Long Short Term Memory techniques for classification we have applied to generate results. (1) Collections of disasters based tourist spot related hashtags datasets in english and non-english language both. After that, manually classify the datasets between English and non-english languages. (2) Hash2vec has been used for making labeled datasets called Semantic-tagged tokens and (3) Apply Long Short Term Memory approach with hash2vec model have been used for representing the document and predict the rank of disasters based tourism spots hash tag. This tool will help people and travel companies to classify tourist places as per their needs and it will also help to retrieve the world’s top cities chart depending on the online ratings and reviews..