Reviewerly/exHarmony huggingface dataset:
Building Safe and Reliable AI Systems for Safety Critical Tasks with Vision-Language Processing

Although AI systems have been applied in various fields and achieved impressive performance, their safety and reliability are still a big concern. This is especially important for safety-critical tasks. One shared characteristic of these critical tasks is their risk sensitivity, where small mistakes can cause big consequences and even endanger life. There are several factors that could be guidelines for the successful deployment of AI systems in sensitive tasks: (i) failure detection and out-of-distribution (OOD) detection; (ii) overfitting identification; (iii) uncertainty quantification for predictions; (iv) robustness to data perturbations. These factors are also challenges of current AI systems, which are major blocks for building safe and reliable AI. Specifically, the current AI algorithms are unable to identify common causes for failure detection. Furthermore, additional techniques are required to quantify the quality of predictions. All these contribute to inaccurate uncertainty quantification, which lowers trust in predictions. Hence obtaining accurate model uncertainty quantification and its further improvement are challenging. To address these issues, many techniques have been proposed, such as regularization methods and learning strategies. As vision and language are the most typical data type and have many open source benchmark datasets, this thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering. In this thesis, we aim to build a safeguard by further developing current techniques to ensure the accurate model uncertainty for safety-critical tasks.
InfEval: Application for Object Detection Analysis

Object Detection is one of the most fundamental and challenging areas in computer vision. A detailed analysis and evaluation is key to understanding the performance of custom Deep Learning models. In this contribution, we present an application which is able to run inference on custom data for models created in different machine learning frameworks (e.g. TensorFlow, PyTorch), visualize the output and evaluate it in detail. Both, the Object Detection models and the data sets, are uploaded and executed locally without leaving the application. Numerous filtering options, for instance filtering on mAP, on NMS or on IoU, are provided.
Which Country Is This? Automatic Country Ranking of Street View Photos

In this demonstration, we present Country Guesser, a live system that guesses the country that a photo is taken in. In particular, given a Google Street View image, our federated ranking model uses a combination of computer vision, machine learning and text retrieval methods to compute a ranking of likely countries of the location shown in a given image from Street View. Interestingly, using text-based features to probe large pre-trained language models can assist to provide cross-modal supervision. We are not aware of previous country guessing systems informed by visual and textual features.
Development of Secured Authentication Contract Communication Network Protocol for IoT Environment

Internet of Things (IoT) is a subject of great interest and is the current technology. For the last few years, IoT application has increased many folds. In addition, systems security has been a wonderful field for research activities. The mutual authentication between the IoT devices and Users of the IoT Environment is a significant part of secure IoT systems. At present the widely used authentication mechanisms are Single password based and are likely vulnerable to side-channel and dictionary attacks. This paper introduces a multi-key-based mutual authentication mechanism to provide a contract for secure authentication and communication between the IoT devices and Users of the IoT Environment. It also deals with publish-subscribe-based Message Queuing Telemetry Transport (MQTT) protocol is used for secure communication, which is bandwidth-efficient and uses a small amount of battery power. The blockchain technology is used in the present work to store data helps to bring trust and transparency to the developed model. The implementation of this entire methodology, the authors has created a prototype using Node MCU ESP8266 and Arduino IDE IoT platform for IoT Environment. To have secured communication via the cloud has been designed for the client to control IoT devices, and the same will be stored using blockchain technology for future data validation. The Node MCU ESP8266 IoT platform used in the present work makes effective and fast IoT applications.
Securing Data Using Visual Cryptography

Data security is paramount in this modern era. Many corporations have established selective or their own cryptography schemes for information gathering and communication. The need of securing sensitive information is ever-so-increasing due to the rise of cyber thefts and online scams. Visual cryptography is a popular cryptographic technique that consists of hiding visual information like audio, video, image, text etc., in such a manner that nobody but the authorized groups who have complete access to the encrypted data, can collectively or individually reveal the original information. In this paper, we elaborate on two proposed visual cryptographic techniques for image encryption: one uses Caesar Cipher and RSA with a modified (k, N) share generation algorithm, and the other uses chaotic logistic mapping and DNA encoding. To analyze the security and robustness of these proposed algorithms, we will be performing several security tests such as time complexity, histograms, number of pixel change ratio, peak signal-to-noise ratios, information entropy, mean square error, and many other tests.
Experimental and Theoretical Investigations on Structural, Morphological, Optical and Dielectric behavior of novel BaO-MnO Metal Nanocomposites

The enrichment of predominant properties of materials by the mixture of two metal oxides has increased popularity such as to intensify in terms of fraction of atoms with high surface area that primarily relies on the shape, size, morphology and composition. Synthesized BaO-MnO metal nanocomposite exhibits crystalline nature which is exemplified by powder XRD characterization. The diffraction peaks with reflections (111)*, (110)*, (200) * belong to the tetragonal BaO structure and the peak reflections corresponding to planes (111) #, (220) #, (311) # belong to the cubic structure of MnO. The groupings of barium oxide (BaO) and manganese oxide (MnO) are chosen for their unique non-linear optic, magnetic and electric properties for the applications in rechargeable batteries, laser technology, wastewater treatment, etc. The metal nanocomposites' structure, morphology, dielectric, and optical characteristics were investigated, and the results were carefully examined. FTIR characterization has confirmed the existence of the functional group.
A Deep Investigation on News Aggregation and Recommendation System: NARS

A significant increase in news sources has been brought about by the growth of the online and the social web. The easy access to numerous news sources results in a flood of information that is frequently contradictory and confusing. When news circulates on the social web, it can be challenging to distinguish between legitimate and false reports. False news can be used for a variety of purposes, including as political influence, financial gain, and strict convictions to solve these problems, a new news aggregator was developed to help users distinguish between authentic, fake, and copy content. News from multiple news sources is reviewed, approved, and presented to the client in the suggested system. The news aggregator also suggests important news to the customer by anticipating consumer preference via a recommendation algorithm. It has been demonstrated that news recommendation systems can naturally handle lengthy articles and distinguish between them for readers who are taking into account established models.
Recognition Model of Transfer Learning for the Marathi Named Entity

Artificial intelligence (AI) has a subfield called information extraction (IE). In unstructured information sources, IE recognises data that complies to specified semantics, such as persons, places, etc. A important task of IE and natural language processing is the recognition of named entities (NEs) from computer-readable natural language material (NLP). Extraction of named entities (NEs) is a crucial stage in the processing of unstructured input. Computationally, unstructured data is opaque. Data that can be processed by computers must be computationally transparent. To make raw data easier for computers to process, IE gives it meaning. For the purpose of extracting entities from text, numerous alternative methods are used. In this piece, Marathi, a language of India that is widely spoken in Maharashtra state, is the subject of our attention. Since Marathi has little resources, there aren't many relevant NER materials available. We introduce L3Cube-MahaNER, the first significant Marathi named entity recognition dataset that adheres to the gold standard. We also go over the manual annotation procedures that were used. We compare the dataset using a variety of CNN, LSTM, and Transformer-based models, including mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc.
An Enhancing the Security of Cloud Data via an Attribute-Based Encryption Model and Linked Hashing

With the present IT, the sky is the limit on the web through sent thinking, permitting us to make, coordinate, use, and adjust sites, associations, and cutoff points. Most frequently, cryptography is used. Cryptography is the investigation of putting together figures, block figures, stream codes, and hash powers. Security associations like underwriting, accessibility, assurance, legitimacy, and non-repudiation ought to be upheld by cryptographic procedures in the cloud. It offers an attractive design with a large information portion to ensure the security of these organizations. The purpose of this work is to provide guidance on how best to address the security of scattered storage using a combination of hashing and encryption functions. Using Netbeans IDE 8.0.2, a JDK 1.7 device, and EyeOS 2.6 as the cloud tier, he proposes to perform his two calculations of Rivest-Shamir-Adleman and the Huge Level Encryption Standard on secure hash gauges. increase. This completes on Ubuntu 15.03.
Multi-Modal Colour Extraction Using Deep Learning Techniques

Multimodal learning research has advanced quickly over the past ten years in a variety of fields, especially computer vision. Due to the growing opportunities of multimodal streaming data and deep learning algorithms, deep multimodal learning is increasingly common. This calls for the development of models that can reliably handle and interpret the multimodal data. Unstructured real-world data, often known as modalities, can naturally assume many different shapes, including both text and images often. Deep learning researchers are still driven by the need to extract useful patterns from this type of data. It is crucial to have well-organized product catalogues to enhance customers' experiences as they explore the plethora of possibilities provided by online marketplaces. The availability of product characteristics like colour or material is a crucial component of it. However, attribute data is frequently erroneous or absent on several of the markets we focus on. Utilizing deep models that have been trained on huge corpora to predict features from unstructured data, such as product descriptions and photographs (referred to as modalities in this study), is one potential approach to solving this issue. To receive a comprehensive rundown of the various multi-modal colour extraction techniques and their advantages, drawbacks and open challenges.
Mining and Recognition of English Learning Patterns of Mobile Users Based on Intelligent Algorithm

The latest research results show that activity recognition based on frequent pattern mining is the main method. Its advantages are that the activity patterns obtained through learning mining are comprehensive and accurate, and the recognition effect of activities in the corresponding environment is good. Besides, some activities that can only be carried out in a specific environment can be identified. This study attempts to use data mining technology to help foreign language education systems to further transform massive data, so as to mine more valuable content, helping educators to further tap teaching potential, and improving the efficiency of teaching resources utilization.
Application and Research of Logistics Information Platform Based on Blockchain Technology

Because blockchain has the feature of decentralization, Non distorting and information transparency, which meets the needs of information transmission, sharing and verification in the logistics industry., the logistics industry will be a typical application field of blockchain technology. Starting from the basic concepts and feature of blockchain technology, this paper will comprehensively discuss the utilization of blockchain technology in the logistics industry, focusing on the construction and implementation of logistics information platform.
Analysis of Fraud Credit Transaction and its Detection

In the current economy, credit cards are an important factor. It becomes an essential component of domestic, commercial, and international activities. Although using credit cards responsibly and reliably offers enormous benefits, fraudulent activity has the potential to cause significant credit and financial harm. Many strategies have been put out to combat the rise in credit card fraud. The detection of credit card theft is currently the most well-known issue. Companies that issue credit cards are searching for the greatest tools and conditions to spot and lessen fraudulent credit card transactions. Recently, AI has gained some attention in the areas of photo analysis, natural language processing, and speech recognition. In order to mitigate these setbacks and assist fraud specialists, effective fraud detection calculations leveraging AI technologies are essential. In this study, credit card theft is detected using an AI-based calculated relapse technique. The findings indicate that computed relapse-based techniques outperform with the highest level of accuracy, and they may very well be used in practice by fraud examiners.
Feasible Virtual Reality Application for Special Kids

When caring for special kids with neuro-development disorders, much needs to be done as there is no medical test to detect some of the special needs. The main problems faced by special kids are social skills and learning disabilities. They cannot communicate like others in the real physical world. To develop these skills a virtual imaginary world or virtual reality can be used where they will learn skills in a virtual manner. It reduces the fear of social intimacy. Augmented-Reality and Virtual-Reality are used to develop their skills and provide a platform for phobia treatments too. These technologies give the kids simple strategies that lead to awareness and acceptance of the actual physical world by stimulation in the virtual world. The use of Augmented-Reality and Virtual-Reality in the field of teaching special kids improves the concepts of understanding. Rather than learning from old or domestic teaching methods technology, where the stimulation of the actual world and the objects in it can enhance the experience of learning and understanding and implementing the knowledge or the discipline that occurred from the process in the real world without the fear of being wrong and removes the margin of the mistakes. This leads to a child having a normal and educational life. In this article proposed Feasible Virtual Reality Application for Special Kids.
A Practical Approach of Recognizing and Detecting Traffic Signs using Deep Neural Network Model

As a result of many technological developments and growing usage of artificial intelligence in our daily routines, the number of autonomous and self-driving vehicles has expanded dramatically. To be effective and efficient, autonomous cars must be able to recognize and interpret a variety of traffic signs and take appropriate responses. A technology known as Traffic Sign Recognition can be used to determine a large number of different traffic signs. Traffic Sign Recognition is a technique that enables self-driving or autonomous vehicles to recognize traffic signs on the road. We must classify the images into their appropriate categories or groupings once they have been recognized. We accomplish this by creating a Convolutional Neural Network model. We must apply a discipline of artificial intelligence known as computer vision to derive information from the images acquired and recognized by the Traffic Sign Recognition and make recommendations based on that knowledge. In our paper, we'll use a Convolutional Neural Network model to create distinct indications in the image that may be sorted into several categories. As a result, the system can read and understand traffic signs, which is a crucial duty in the creation and improvement of autonomous vehicles. Because this would be used in a real-time setting, we have included blurry images in our dataset to emulate real-time capturing scenarios like as when the vehicle is moving or when direct light is shining on the subject.
A Survey on Healthcare Standards and Security Requirements for Electronic Health Records

Healthcare in cloud is fundamental enforcement in terms of security and privacy. Research on the various security issues surrounding healthcare information systems has been carried out in recent years. The security and privacy standards for EHR are defined in ISO/TS 18308 standard. Adoption of Electronic Health Record (EHR) in healthcare industry has major importance in terms of security and privacy of patient's information. Health Insurance Portability and Accountability Act (HIPAA) address confidentiality and security of Protected Health Information (PHI) that includes patient's electronic health record. This paper describes the extensive survey on different standards required for security and privacy of EHR and propose a security framework.
Modelling of Eye Blink Monitoring Mechanism utilizing ML Techniques

One of the main areas of exploration, PC vision has focused on making various applications that have demonstrated advantageous for both intellectual and cultural purposes. One of the most dependable strategies for correspondence utilized by contemporary human-PC communication (HCI) frameworks is eye flicker identification. Volunteer eye-flickering is recommended as a sign for human-PC communication in this paper, and a savvy PC vision identifier was made to deal with the information continuously utilizing a modest webcam. A moving typical channel, a turn compensator, and a returns on initial capital investment evaluator were incorporated as helper models to this pipeline. The YouTube Eye-state Portrayal (YEC) dataset, created by wiping out face photos from the AVS peech dataset, and the Autonomous Squint Dataset (ABD), built totally as a result of the continuous work, were both made. The eye-gathering task was workable through the YEC, and the ABD was unequivocally arranged considering volunteer eye-glinting distinguishing proof. The YEC dataset was used to set up the proposed models, a Convolutional Cerebrum Association (CNN) and an Assist Vector with machining (SVM), and execution evaluation peruses up for the two models were finished across various informational collections: Eyeblink, CeW, and ABD (public datasets).
An Intelligent Model of Email Spam Classification

One of the biggest dangers to the modern Internet is email spam. Numerous anti-spam filters have been developed to counter this danger. Predicting the labels of emails in a personalised mailbox is one of these filters' biggest challenges. Private data loss is another risk posed by these spam mailings. Modern studies have classified text messages as spam by using some stylistic characteristics of the texts. The detection of email spam can be significantly impacted by the use of well-known terms, phrases, abbreviations, and idioms. Nowadays, spam emails are successfully filtered automatically using machine learning algorithms. We evaluated the absolute most famous machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Counterfeit safe framework, and Unpleasant sets) and of their appropriateness to the issue of spam in this paper, where we introduced the NLP component that can channel spam and non-spam emails and furthermore classify into various spam sends. categorization of email. The algorithms' descriptions and a comparison of how well they performed on the SpamAssassin spam corpus are offered.
Comparative Analysis of Deepfake Video Detection Using Inception Net and Efficient Net

Human beings have the most distinctive feature that is human face. We can exchange somebody faces with anybody else's faces that appear realistic because many have another type of algo is based upon deepfake tech. Deepfake videos / photos is revolutionary subdual of AI tech by using someones human face can overwrite of someones face. More generously, with many different methods based on productive pictures. Unwillingly the overuse of smartphone and organizing by multiple internet web using AI manipulated data is reaching quicker in something which can we see in the 20th century, global danger is made up by these products Deepfakes are digital manipulation techniques that use machine learning to produce misleading videos. Identification is most difficult part to find from the original. Previously, CNN networks were used to perform identify the deep fake verification. Due to the increasing popularity of deep fakes identification of real one is more important find ways to detect manipulated videos that are presented as real ones. In this project, we will study different methods that can be used to detect such images as well as videos. This study shows that they can also be done using a convolutional algorithm known as Efficient Net and Inception Net. In this Paper, we compare various versions of Convolutional Inception Net with various versions of convolutional Efficient Net combined with Vision Transformers and different Data files to obtain best possible results in Deepfake detection. To get the highly accurate percentage to identify the video is fake or real by using efficient net and by inception net. tract)
Application of Artificial Intelligence Algorithm in Indoor Virtual Display System

In view of the shortcomings of traditional interior design, such as small changes, less experience, low work efficiency and high development cost, this paper proposes an indoor display system based on artificial intelligence algorithm, analyzes the importance and value of system design, and verifies the realization algorithm of the main functions of the system. Methods According to the element type, the model file was modeled and generated by artificial intelligence algorithm. At the same time, through the development of module functions, the system roaming and interactive functions are realized Therefore, the virtual interior design system shows the overall result of the final interior design. Users can view virtual space or change its design in real time as they wish, including changing wallpaper or floor style, moving furniture or changing furniture location, deleting unsatisfied furniture, adding interesting furniture, etc. Conclusion Virtual interior decoration system can improve user experience and satisfaction, improve the efficiency of design companies, and reduce design costs.