Reviewerly/exHarmony huggingface dataset:
Explaining explainable AI

Financial Institutions need AI systems that are explainable in order to develop, use, and supervise these systems effectively and responsibly. However, the ever-expanding number of mathematical explainability techniques can obscure the fact that explanation is fundamentally a social interaction between an explainer and an explainee(the stakeholder receiving an explanation). Therefore, in operationalizing the concept of explainable AI financial institutions should keep in mind the many different types of stakeholders - who include decision-subjects, business users, internal model checkers, external auditors and regulators - and how their needs for different types of explanations vary. This variety of needs reflects the diverse sources of demand for explanations, which range from direct or indirect legal obligations, to enabling auditing, to appropriately calibrating trust and enhancing the performance of teams using AI. In this chapter we use a human-centric and cross-disciplinary approach to provide a holistic introduction to explainable AI. We cover: the different kinds of explanations; levels of explainability; example explainability techniques; the drivers of demand for explanations; the qualities of good explanations; and the challenges financial institutions are likely to face when trying to implement explainable AI systems.
Progressive feature-aware recurrent net for low-light image enhancement

Mixed mapping across illumination and color domains directly from low-light images is prone to cause domain ambiguity and mislead the holistic enhancement process. The results appear color distorted and unbalanced between overexposure and underexposure. To deal with this issue, we propose a progressive feature-aware recurrent network decoupling the enhancement task into domain-specific subtasks, which gradually restores the illumination and color information of the low-light image under different attention patterns. Specifically, the illumination is adaptively enhanced by the recurrent sub-network with pixel-wise attention. Then, under the guidance of normal illumination, the color enhancement sub-network uses channel attention to balance the restoration of color channels in HSV space. Finally, the preliminary restoration image is further enhanced by the recurrent refined sub-network with dual attention. This progressive learning pattern enables each sub-network to focus on the learning of the specific feature, and the whole network can realize the cumulative learning of multiple features. Experiments on public low-light image datasets demonstrate that our method outperforms the SOTA methods.
Achieving privacy-preserving cross-silo anomaly detection using federated XGBoost

Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. These data are often collected and stored across different institutions (banks, hospitals, etc.), or termed cross-silo. In this context, cross-silo federated learning has become prominent to tackle the privacy issues, where only model updates will be transmitted from institutions to servers without revealing institutions’ private information. In this paper, we propose a cross-silo federated XGBoost approach to solve the federated anomaly detection problem, which aims to identify abnormalities from extremely unbalanced datasets (e.g., credit card fraud detection) and can be considered a special classification problem. We design two privacy-preserving mechanisms that are tailored to the federated XGBoost: anonymity based data aggregation and local differential privacy. In the anonymity based data aggregation scenario, we cluster data into different groups and using a cluster-level data feature to train the model. In the local differential privacy scenario, we design a federated XGBoost framework by incorporate differential privacy in parameter transmission. Our experimental results over two datasets show the effectiveness of our proposed schemes compared with existing methods.
Virtual high-resolution MR angiography from non-angiographic multi-contrast MRIs: synthetic vascular model populations for in-silico trials

Despite success on multi-contrast MR image synthesis, generating specific modalities remains challenging. Those include Magnetic Resonance Angiography (MRA) that highlights details of vascular anatomy using specialised imaging sequences for emphasising inflow effect. This work proposes an end-to-end generative adversarial network that can synthesise anatomically plausible, high-resolution 3D MRA images using commonly acquired multi-contrast MR images (e.g. T1/T2/PD-weighted MR images) for the same subject whilst preserving the continuity of vascular anatomy. A reliable technique for MRA synthesis would unleash the research potential of very few population databases with imaging modalities (such as MRA) that enable quantitative characterisation of whole-brain vasculature. Our work is motivated by the need to generate digital twins and virtual patients of cerebrovascular anatomy for in-silico studies and/or in-silico trials. We propose a dedicated generator and discriminator that leverage the shared and complementary features of multi-source images. We design a composite loss function for emphasising vascular properties by minimising the statistical difference between the feature representations of the target images and the synthesised outputs in both 3D volumetric and 2D projection domains. Experimental results show that the proposed method can synthesise high-quality MRA images and outperform the state-of-the-art generative models both qualitatively and quantitatively. The importance assessment reveals that T2 and PD-weighted images are better predictors of MRA images than T1; and PD-weighted images contribute to better visibility of small vessel branches towards the peripheral regions. In addition, the proposed approach can generalise to unseen data acquired at different imaging centres with different scanners, whilst synthesising MRAs and vascular geometries that maintain vessel continuity. The results show the potential for use of the proposed approach to generating digital twin cohorts of cerebrovascular anatomy at scale from structural MR images typically acquired in population imaging initiatives.
AVA: A Photorealistic AI Bot for Human-like Interaction and Extended Reality

In the era of rapid technological advancement, artificial intelligence (AI) and machine learning (ML) are transforming the way we work and interact with the world around us. The hiring process is a crucial aspect of any organization, as it determines the quality of the workforce and the success of the business. However, traditional hiring methods can be time-consuming and prone to bias. In this paper, we propose a better approach to hiring that leverages the power of Artificial Intelligence (AI) and machine learning (ML) to automate and improve the efficiency of the process. Our proposed system allows managers to specify their requirements and receive a shortlist of candidates based on their skills, experience, and performance in a one-on-one interview with a photorealistic artificial intelligence bot. The bot also assesses candidates’ confidence and body language to rank them accordingly. By using AI and machine learning in the hiring process, we can save time and reduce bias, leading to better-quality hires and a more productive workforce.
English to Hindi Translation using Transformer

This study analyzes the effectiveness of transformer-based models for English to Hindi translation, as the lack of translation resources for this language pair poses challenges in various domains. Our literature review supports the remarkable success of transformer architecture in natural language processing tasks, including machine translation. Experiments were conducted on multiple datasets to evaluate the effectiveness of the transformer model, and the findings provide further evidence of its effectiveness for English to Hindi translation. The results contribute to the understanding of the effectiveness of the transformer architecture in machine translation for English to Hindi language pairs and the importance of developing and improving machine translation models for overcoming the challenges faced due to a lack of translation resources.
An Improved Key Management System - DES Ultimate v1.1

Significance of cryptographic encryption-decryption algorithms is well understood i.e. paramount objective is not only limited to build the secure system but also the efficient one too. The iterative structures behind various block-ciphers are very much popular and used in various software/hardware implementations to preserve the confidentiality, integrity of data, due to its ruthless nature against linear and differential cryptanalysis. Even though these popular structures are strong enough to shield against attacks, but still have certain vulnerabilities viz. week/single-key to be used, lack of randomization, identical nature of key management systems etc., which somehow provide backdoors to adversaries. This research work intends to address above mentioned flaws specifically by introducing contemporary key-management system to deduce a set of secret keys that has to be used during encryption-decryption process. The proposed enhanced version of DES not only makes it difficult for adversaries to succeed in attacks but also focuses on the efficiency of the implementation. The 16*16 s-box is used in proposed construction instead of 8/32 s-box due to the vulnerabilities in these implementations.
An Improved Leukemia Detection Model using CNN

Leukemia is a kind of cancer in which a significant number of immature lymphocyte blood cells form, interfering with the formation of other blood cells. Normally, Leukemia can be identified by using Complete Blood Count(CBC) test. If there is too few or too many of one type of blood cells are presents in CBC then that indicates Leukemia. Even though leukaemia affects children 80% of the time, it can be fatal when it affects adults. So Early detection of Leukemia is very important to save lives. Computer technology can help in quick Leukemia diagnosis. This study uses Convolution Neural Network (CNN) model for classifying microscopic blood cells images to predict whether the person is having Leukemia or not.
A Novel Technique for Converting Images from 2D to 3D using Deep Neural Networks

The conversion of 2D images into 3D images has been a crucial scientific problem. In the industries of computer graphics and animations, medical imaging augmented and virtual reality, and others, reconstruction of 3D images from 2D images is high in demand. Researchers deploy convolutional networks for the reconstruction of 3D images. This paper compares the existing techniques and proposes a MeshCNN-based nove1 technique to convert 2D images into 3D images. The publicly available dataset namely VisDrone is used for experimental study. The proposed technique has been validated against the existing techniques. It is concluded that the proposed model has the potential to effectively convert 2D images into 3D images.
Multilingual Global Translation using Machine Learning

Language remains as a communication between two people while allowing them to understand and express each other's emotions, ideas, and views. In light of the globalization of the IT business, platforms that support several languages have become a standard. Text recognition is one of the most researched topics and is done with the help of Deep Neural Networks (DNN). People find it easy to read text, but computers have a hard time doing it. However, there are also several challenges associated with creating and managing multilingual websites, including: Content creation, Technical implementation, implementing a multilingual website can be technically challenging, content optimization, Maintenance and updates. By taking a paragraph as input and processing the handwritten English characters, the article hopes to convert them into a computer-readable format paragraph with support for symbols and cursive writing, then train a neural network system to do so. Machine Learning algorithms (LSTM, FLITE, VAD, etc.) are studied for the various multilingual language conversion. The model is based on numerous natural language processing concepts and was created by adding multilingual capabilities to Google's previous Speech Recognition model. The article aims to develop a multilingual recognition model that will make it possible for non-readers to interact with digital devices in their native tongue.
Text Detection based on Deep Learning

Optical Character Recognition (OCR), a system for automatic recognition, is used in a variety of application sectors to convert text or images into editable data. With the distinct outline and size, printable or typed letters are easy to identify. However, this is never the case while dealing with handwritten text since each person's handwriting is unique. Handwritten text is difficult for OCR to read. This study presents a two-phase method for recognizing and classifying the input data. Convolutional Neural Networks (CNNs) were used as a framework for categorization. The process starts with the recognition of input text. The second stage is to determine the language used to generate the input number. Further, Python is used to analyze the handwritten characters in MNIST database. The simulation results show an error-free recognition rate and extremely high efficiency. The proposed work has achieved a 1.4% training loss, 99% testing accuracy and 99.6% training accuracy.
Blockchain in Telecom: Challenges and The Way Forward

Blockchain is a disruptive technology that is increasingly being leveraged in different sectors due to its vast array of possibilities. Blockchain ensures a high level of security, immutability, and transparency, making it an attractive solution to multiple problems. Telecom industry can immensely benefit from this technology. In the highly competitive telecom sector, operators are looking for ways to optimize the processes and cut down on costs. The question is if Blockchain can help the telecom operators in reducing costs and increasing revenue or is blockchain a hype and not a necessity. This paper discusses specific areas in the telco ecosystem where blockchain can be utilized to add value to the operator and offer a competitive advantage It also analyses the challenges and the way forward.
Machine Learning Approach for Gesticulation System Using Hand

Hand gestures play a crucial role in both nonverbal cues and our interactions with the outside world. Notably, millions of individuals with disabilities benefit from sign language, a system of hand signals. The versatility of hand gesture recognition technologies to successfully collaborate with machines has spurred their rapid development in recent years. The most intuitive form of communication between humans and PCs in a virtual environment is seen to be gesturing. The proposed work focus on hand gestures to convey ideas because they are an evocative form of nonverbal communication. In this programme, the camera on our PC captures a live video from which a preview is obtained with the aid of its features or operations. The entry onto the market of inexpensive webcams with at least acceptable characteristics opens up new avenues for the application of human-computer interaction (HCI) interfaces. The paper offers an HCI mouse cursor control interface. The implemented solution's goal is to enable user hand motions to move the mouse cursor while viewed on a webcam.
EHR: Patient Electronic Health Records using Blockchain Security Framework

The new age calls for a new system for the storage and retrieval of health records. Traditional EHRs are no longer in use since they were never designed to work with multi-institutional control or data storage. Traditional EHRs are currently vulnerable to insider andinternet transaction assaults, among other threats. Healthcare systems are joint efforts of the health and information technologies. The digitized way of data sharing can raise the chances of security problems and along with the problem of the civilian medical records. Healthcare record sharing can be tedious because the same individual's records can be stored in multiple places which will raise the situation of duplicity in the system. To profit from the data, several fraud schemes attempt to change the information everywhere in the world. To produce bogus reports typically entails changing the records or hacking the records. The main reason for this is that patients are not permitted to access their information. In this framework, a new extension has been created to safeguard medical records using a cutting-edge method to create a reliable EHR scheme. It also assists in monitoring the lack of transparency among medical professionals by enabling only authorized users to access the EHR system. All of these issues would be resolved by the adoption of Blockchain-based EHR platforms because these systems are built on smart contracts that link each user's action to their user.
An Enhanced Framework for Data Protection in Cloud Environment using ECL-GWO Technique

Computing that is done over the internet, or “cloud computing,” is a relatively recent innovation. In cloud computing keeping data private, secure, anonymous, and reliable are just some of the cloud's most pressing issues. The most crucial aspect is the security and how the doud service provider guarantees it. The proposed framework aims to address these challenges by providing a comprehensive data protection solution that incorporates both encryption and optimization techniques. The framework uses the ECL algorithm to optimize the selection of cryptographic keys and the Grey Wolf Optimization (GWO) technique to optimize the placement of data in the cloud. By doing so, the framework is designed to enhance data confidentiality, integrity, and availability in doud environments while minimizing the risk of security breaches and other types of attacks. Based on the varying opinions of doud users, this study proposes a Grey Wolf Optimization (GWO) model for addressing privacy issues in the cloud by employing encryption algorithms to enhance the security. The ECL-GWO technique aims to improve the performance and efficiency of data encryption and decryption in doud environments by optimizing the key generation process. Data integrity ensures by evaluate the performance of ECL and ECL-GWO algorithm with the different workflows. This study examines the potential benefits and drawbacks of using an encryption algorithm in cloud storage to safeguard sensitive data. Proposed approach improve the mitigate of man in the middle attack, so it improves security and authentication. Data security achieved with the proposed ECL-GWO algorithm uses encryption to secure data before it is stored on the server and it ensures that only authorized parties with the decryption keys can access the data. The GWO algorithm can be used to optimize the security parameters of the encryption algorithm used by the ECL-GWO algorithm. This ensures that the encryption is strong enough to protect the data from attacks. In the proposed GWO method is used to enhance storage and time by 50%, and 4.6% respectively. Few techniques aim to improve the security and efficiency of data protection in cloud environments using the ECL-GWO technique like Hybrid Genetic Algorithm and ECL-GWO, Parallel ECL-GWO and Multi-level Encryption using ECL-GWO.
Deep learning-based video anonymization for security and privacy

In recent years, the design of systems has been heavily influenced by concerns about privacy and security. Companies have been particularly affected by the introduction of GDPR, which requires them to consider the privacy of individuals when storing personal data or facing significant fines. To address this issue, we have proposed a system that can detect and censor faces in a way that prioritizes privacy. Our system can process videos and automatically identify and blur facial features. We have customized our implementation using Facebook's object detector, Detectron2, and further developed it to enable facial detection and censorship. To evaluate the effectiveness of our approach, we conducted statistical tests and a small user study using videos that our system had censored. The results suggest that our implementation can reliably blur facial features to the point where the censored individuals are unrecognizable.
Non-Invasive Method of Detecting Anemia using AI &amp; IoT

In the current scenario, healthcare devices play a vital role in an individual's life. Nowadays, it is one of the most important things that a person should own. Due to the increase in the number of basic instruments such as thermometer, oximeter, spirometer, etc., the difficulty in owning all of those have increased. To solve that, the proposed model introduces an all-in-one portable and pocket-friendly device, which can measure temperature, heart rate, SpO2 level, and lungs capacity. This device consists of sensors from extremely reputed companies such as Maxim Integrated and few more. It is a rechargeable device thus, does not require the changing of battery once it runs out. It also features mobile connectivity (optional) through which the user can diagnose his/her health by comparing the data obtained from the device that has been stored in the app. As the demand for healthcare devices is increasing, the cost to own one is also increasing day by day. To make sure that a huge sector of people can own one, the proposed method made sure to make Any Time Healthcare Device as affordable as possible when compared to the other multipurpose devices.
Project Activity as a Consensus Management Tool in the Modern Library

Project activity is a tool for diversifying the management of modern library in the conditions of high complexity and uncertainty; it is a way to complete and strengthen the power vertical by horizontal ties of self-organizing peer groups. Participation in project activities allows building a space of trust between the administration and the library staff, forming shared goals and values, and contributes to strengthening consensus. Project activities contribute to the effective solution of complex tasks facing the modern library: embedding libraries in the knowledge economy, working in the format of social hub, implementing the agenda of creative industries. By helping to build partnerships with representatives of different communities, the project activities contribute to the development of material and technical resources of library, as well as the creation of new services, products and services. In addition, project activity is an important tool for developing the creative and organizational potential of library specialists. The article discusses the experience of the Irkutsk Regional Universal Scientific Library named after I.I. Molchanov-Sibirskiy on the systematic implementation of project activities in the central regional library and the network of libraries of the Angara region. The author analyses the main tools of project activity: the Regulations on project activity of the Irkutsk Regional Universal Scientific Library, the Project Council, strategic sessions and innovative project “descents”. The paper describes the concept of the regional network socially oriented project “Library for government, society, individual”. The author gives statistics of the implemented projects, as well as determines the areas of effective application of process and project management in the library.