Reviewerly/exHarmony huggingface dataset:
EAM: Ensemble of approximate multipliers for robust DNNs

Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in a wide range of real-world applications such as healthcare, online banking, etc. However, recent advances in deep learning have opened up a largely unexplored surface for adversarial attacks, jeopardizing the integrity of DNN systems. DNNs are vulnerable to adversarial samples that are generated by perturbing correctly classified inputs to cause DNN models to mispredict. This can potentially lead to catastrophic consequences, especially in critical and security-sensitive applications such as autonomous driving and video surveillance systems. We propose ensemble of approximate multipliers (EAM), an architectural technique that uses a combination of approximate multipliers to protect DNNs against adversarial attacks. Approximate computing is known for its effectiveness in improving the energy-efficiency of computing platforms at the cost of slight accuracy loss. Using approximation in the form of inexact multipliers is also effective for increasing robustness by injecting input-dependent noises into the outputs of DNNs. However, resiliency of DNNs against malicious attacks depends on the type of an approximate multiplier. Based on level of approximation, robustness of DNNs changes by a large margin. We exploit this variability and propose using an ensemble of different types of approximate multipliers. EAM increases robustness across a wide range of attack scenarios. In particular, we show that successful attacks against an exact DNN have poor transferability to EAM. The transferability is also poor for a black-box model where perturbed inputs are generated by a substitute model. In addition, EAM is resilient against a white-box attack scenario where an attacker has full knowledge of approximate hardware. Our ensemble technique does not require duplication of computing cores nor memory units. EAM only uses additional approximate compressors within the original multipliers. We conduct extensive experiments on a set of strong adversarial attacks. We empirically show that EAM increases robustness over the exact model with negligible impact on accuracy of benign inputs.
The construction of sets with strong nonlocality using fewer states

Reducing the number of states is significant for exploring which states are closely related to nonlocality. In this paper, we investigate how to construct the set exhibiting strong nonlocality using fewer states. Firstly, we focus on the tripartite system and establish a general set of orthogonal product states with the assistance of Rubik's cube. By removing some states which differ by relative phases, the size of the set decreases from Od2 to Od while the strength of nonlocality is not affected. Furthermore, similar to the tripartite system, two irreducible sets with different nonlocality strength in four-party quantum system are demonstrated, both of which contain fewer states. Our research significantly reduces the number of states and can be used in a variety of quantum cryptographic protocols.
Application of the convergent education model in the development of a smart learning environment

The article presents a scientific and practical approach to the development and implementation of the concept of convergent learning in the framework of building an open educational platform to manage the process of training specialists. The presented concept of convergence education is based on the integration of educational programs, learning content, competency matrices, methods and technologies for training specialists and personalized learning trajectories in smart learning environment. To implement the model and the convergent approach, it is vital to make use of an information and educational environment with mechanisms to support convergent learning based on regional labor market analysis, actualizing of educational programs and resources, harmonization and synchronization life-cycle models thereof, adaptive customization and personalization of specialist training trajectories. The article presents the developed convergence model of educational programs and employer vacancies as a weighted graph to illustrate the relationship between competence requirement vectors of educational programs and sought-after employment. To isolate the convergence model components and evaluate the convergence degree thereof, a graph clustering algorithm based on the Louvain method has been developed. A method for synthesizing a convergence model of educational content organized as a fuzzy weighted graph has been presented, and sets of topics and sections of electronic learning resources being its vertices. Search and extraction of convergence educational content sections have been implemented by means of an algorithm for extracting fuzzy isomorphic subgraphs, and assessing the convergence degree thereof. The results of the study are used to synthesize an information and educational environment with intelligent mechanisms for supporting learning processes based on an analysis of the requirements of labor markets and actualizing educational content and training programs, as well as adaptive customization and personalization of specialist training trajectories. The architecture of the educational environment includes a set of software and tools for managing the components of the information space of the university with support for mobile, cloud and blended learning technologies.
The interface between specialized translation and institutional translation: A selection of candidate terms validated by Aeronautical Meteorology corpora

The purpose of this work is to revise and expand an aeronautical meteorology glossary, available at REDEMET, a homepage hosted on the Department of Airspace Control website, taking into consideration corpus data in the field. For that, to best meet the needs of institutions and users, data were compiled from some segments of the Aeronautical Meteorology domain. During the compilation of this corpus, it was noticed that there was a great scarcity of specialized sources of this Aviation subdomain in English and, mainly, in Portuguese, including material by the Department of Airspace Control (DECEA), the only official Brazilian institution with the role of regulating standards relevant to Aeronautical Meteorology. By taking into account that a given government institution is considered an authoritative source concerning terms used in a specialized domain, it would be advisable to align professional and academic expertise, and institutional interests. Therefore, based on contributions of corpus linguistics theories, terminology, and institutional translation, this work relied on established parameters for the compilation and processing of information for inclusion in the corpus, and focused, in this first stage, on the selection of candidate terms, according to corpus analysis. The first results showed that institutional and academic segments present some subtleties regarding terminology, as, on the one hand, some words are more specific to the academic register and, on the other hand, there are different uses of terms in the institutional setting, by ICAO, WMO, or FAA.
Evaluation of Deep Learning Methods and Face Detection Framework with Convolutional Neural Network

In the current technological evaluations artificial intelligence frameworks are increasing every day. Face detection has become one of the interesting areas of research where accurate face detection is required. The most commonly used face detection framework is Viola Jones algorithm using Haar feature cascade algorithm. Feature extraction is important to accurately detect the face. In order to rectify complications in feature extraction process a convolutional neural network CNN based novel robust framework is developed here. The Model considers the face images and performs three different face feature extraction method comparatively. Feature extraction using hog classifier, Face detection using Haar cascade and convolutional neural network (CNN) based feature extraction process are comparatively analyzed here. The proposed model is compared with various state of art approaches in terms of accuracy, and statistical parameters such as False rejection rate, false acceptance rate and error rate. Real time face detection has significant challenges over static face detection frameworks. The face detection models with frontal face, unique attribute detection are deeply correlated with deep convolutional neural network in the proposed model.
Random Key Generation based Double Image Encryption System

Encryption technique is widely used to ensure security in communication and wireless networks such as the Internet, Networking zone and Intranet. Every type of data has its own characteristics consequently, to safeguard private picture data from unwanted access, a variety of strategies are employed. In this paper an image encryption technology called Data Encryption Standard (DES) is combined with XOR to create a block cypher transformation algorithm for picture security. The suggested method is based on XOR with DES encryption, which emphasizes larger changes in the RGB combination as well as the histogram. The findings of the suggested method indicate more variety. The security of the system will be increased by increasing the variety.
Deep Learning Technique to generate lip-sync for live 2-D Animation

Currently, there are a couple of major trends in live 2-D animation like Immersive User Experience, Next-Gen Software, Mobile 2D Animation, VR 2D Animation, AR/VR Live Action Animation, and Web 2D Animation that are getting a lot of attention. For the industries like advertisement, entertainment as well as education, this new technology has become very important as each of them needs to reach their target audience through it. With the help of live 2-D animation, these industries can now reach out to new audiences through something that has never been done before, creating original content that is something that has never been done before. This has now become possible due to the rise of social media. With the advent of social media, it is now possible for anyone in the world to communicate with people who have never been contacted by any advertising or marketing agency before. The solution we are proposing is a deep learning technique for creating animated characters that can make lips synchronized just the way humans do without any assistance and intervention. This can save many working hours that are needed to make an animated character feel real while dialog delivery. The main requirement here is the exact lip sync as that of the human performs while talking the lip movement should get exactly matched. This is possible with the help of LSTM model [5].
A System for Medical Record Using Blockchain

By the mean of these paper brief description, we describe briefly the concept of system for medical record using blockchain system. Blockchain is a popular technology with a decentralized focus on security. So By the mean of these paper brief description, we express the concept of system for medical record management using blockchain. Instead a blockchain is a trending technology that we focus on security which is decentralized. So that is it does not have only one admin to handle medical records. Instead of that the recent system focusing on enhancement and advancement of system we work on concept of blockchain cryptography in that we pass simple query and convert it in complex query by using encoding and decoding mechanism. By viewing the influence of blockchain cryptography which is decentralized in nature which consist of more than one admin and focusing on security. It store large amount of patients data and retrieve easily and the blockchain allow user to make to secure authentic data by generating key which is private. As they are not vulnerable to a single point of neglect, these two properties of blockchain can be coupled together to build a data management system that is totally cyberpunk testimonial. Data security can be improved by using the concept of cryptography, which is one of the important benefit of blockchain that it provides higher security. Healthcare information is some of the most secretive and concealed data that is significant even under the strongest prohibition. This data can be shielded from manipulation and unwanted access thanks to the concept of blockchain technology.
Superpixel based Image Colorization with Automated Reference Image Selection

Colorization has received a lot of attention recently. The use of colorization can revitalise dated images, videos, and films. In scientific and medical photographs, colorization can be used to draw attention to certain details. If we can transmit compact grayscale data and restore it to colour on the receiver, we can lower the expenses associated with sending photos and movies over the network. Colorizing images has been tried using scribble-based, example-based, and deep learning techniques over the years. Scribble-based and example-based coloring requires manual supervision for selecting the right color for image regions and for the selection of reference images for color transfer. Deep learning techniques are self-sufficient, but they must be trained on a large corpus of image data to colourize efficiently. We propose a variant of the color-by-example technique in this paper. Normally, the performance of color-by-example relies heavily on the selection of reference images for colouring, necessitating the use of a human operator. We propose a technique for automated reference image selection that employs a hybrid texture matching model to select the best reference image for colouring the given query image. A superpixel-based method is used for transferring the color from the reference image to the query image. The proposed method performs well at identifying appropriate reference images and producing visually appealing coloured images.
A Comprehensive survey on Real Time human pose estimation

Estimating a person's pose in real time in photos or videos is called “real-time estimate of human posture”. We make an attempt to present a thorough analysis of current models for estimating deep human poses from the bottom up and top down, additionally, posture estimation techniques are being used to recognise actions. The research papers covered in this study focuses on the recognition of 2D and 3D human poses. In this research, we concentrate on pose estimation on depth images and seek out computationally efficient methods without GPU exploitation. We give an overview of how recent action recognition systems-which take pose estimates from colour photographs as input-perform before demonstrating how much potential there is for development in this field.
Smart Contract-Based Land Registration System Using Blockchain

Blockchain is a database that records transactions on many computers. It is a decentralized technology, so all the copies of the blocks are distributed in a network. This solution will remove the problems that exist in the traditional system. The old system has many issues. We still use the conventional registration system, where a broker is between buyer and seller. And he is responsible for all transactions and document verification. The broker will guarantee that the documents are registered with an approved government office, where all the property-related information is kept in a public digital ledger, and the transaction between the two parties takes place. However, anyone with authorization to access these papers may lose, replace or modify them, which risks the protection of property and lands. Blockchain enables us to construct a digital ledger of transactions, events and data secured by a cryptographic algorithm.
Improved Soft Actor-Critic: Reducing Bias and Estimation Error for Fast Learning

Soft actor critic (SAC) is an off policy deep reinfor-cment learning algorithm that learns by using the principle of maximum entropy regularization. It updates the policy stochas-tically. The actor-critic formulation is combined with off policy learning procedure in SAC which helped it to attain state-of-art performance. This paper suggests some methods to enhance the performance of SAC. Firstly, multi-step look ahead is suggested which deals with the estimation error of q - values followed by an approach of dual actor network and delayed updates of the target and policy networks which minimize the errors transferred during the training. The resulted ImprovedSAC is compared with the original SAC interms of maximum rewards obtained. The simulation results in the benchmark control tasks of MUJOCO environments shows the effectiveness of the suggested methods.
Government Infra Assessment System

Without involving any other parties and abstaining from any other compromised operations across India, Government Infra Assessment System would provide all the necessary information regarding Government Infrastructure to the Government itself. Government employees will not only benefit from time savings, but also from the ability to view the database whenever they want without waiting.
Generalized Immutable Ledger (GILED) using Blockchain Technology

Applications of centralised databases have been found in many areas. Almost every organization, private or public, makes use of centralised databases. These databases hold the entire scenario about the organization, like records of their customers, transactions occurring in the organization, past analysis, current performance, future prediction, and a lot more things that can be calculated over them. Basically, these databases are the roots of a particular organization. But the issue regarding these centralised databases is that their access is totally open to some individuals or a particular group of people within the organization. The issue that has been identified is corruption. It is difficult for an outsider to manipulate the data, but it is very simple for anyone within the organisation to do so. Now here the owner of the organization, whether an individual or the government, and the customers of the organisation face trouble because these manipulations are done in such a fine manner that only the person manipulating them knows the changes, while the owner and the customer are unaware of them. These things are more likely to be seen in government sectors. So in order to overcome the existing problem, we are considering using block chain technology in our project. Since the block chain is a distributed network, manipulation of the data is impossible by a single corrupt person. Each minute change made in a ledger can be tracked using a block chain. Many applications have already been deployed to address this issue, but this paper is about generalization, which will be based on a multi-purpose use case rather than a specific use case. This paper gives a generalised solution to solve the above-mentioned problem in any field or use case.
Digital Image Watermarking and Its Applications: A Detailed Review

The protection of digital information has drawn significant attention in recent years. Data authentication and copyright protection issues are widely considered as two major concerns. Digital watermarking can be considered as a solution to ensure copyright protection and data authentication in which a watermark will be embedded into a digital content. Later, the watermark can be extracted to ensure copyright protection or to ensure data authentication. Robust watermarking schemes are widely used for copyright protection of digital content and fragile watermarking schemes are preferred for data authentication. The purpose of this work is to provide a thorough analysis of the domain of research regarding image watermarking methods. This manuscript gives an overview of the digital image watermarking approach, the classification of digital image watermarking schemes, and the research challenges in this domain.
Music Streaming Using Blockchain - Blockics

Blockics is a music and video player which works on a buy once method (user can buy song or entire album). User can use the platform to access its content s by starting a superfluid stream to the platform and, the accumulated funds from users will be directly transferred to the respective artists. The creator when they are about to go live we will have a feature of crowdfunding, form where they can collect funds in a short period of time. The top donors in crowdfunding will be rewarded with special perks like early release access to next releases, discounts, rare NFTs, etc. Blockchain has several uses that go far beyond virtual currency like bitcoin. The technology is having an impact on a wide range of businesses, changing everything from how contracts are upheld to how efficiently government is managed. In addition to saving businesses time and money, it can enhance transparency and fairness. Smart contracts are similar to regular contracts, but they eliminate the middleman and offer degrees of accountability that is not possible with conventional agreements for all parties involved. Instead, a blockchain is used to enforce the contract's terms in real-time. By doing so, firms can save time and money while ensuring that all parties involved comply. Blockchain-based contracts are becoming more and more common as sectors including government, healthcare, and real estate learn about the benefits..
Building Intelligent Embodied AI Agents for Asking Clarifying Questions

Interactive embodied agents are intelligent agents present inside some virtual environment and interact with humans to do tasks we want them to simulate. The communication takes place in some verbal or non-verbal form. This kind of communication gives a better and richer experience and more possibility for users to be satisfied with the carried-out task. An intelligent agent analyzes the environment it is in and performs tasks with all its knowledge based on the interaction. The task is carried out in the game environment where the Agent resides. There are some challenges to an agent that it needs to address, as well as to understand the instruction given to it. The next challenge is asking for clarification if the instruction given is unclear. In this research paper, we have discussed the problems mentioned above using the dataset provided by the NeurIPS 2022 IGLU Challenge which is a labeled dataset. We have discussed the various approaches for the problem mentioned.
Sarcasm Detection Analysis – Comparative Analysis

Sarcasm is a unique class of figurative language, that is in particular utilized in on-line social media. Such messages are used for sarcastic feedback the usage of contemptuous, taunt, and mock-associated phrases or phrases. Normally, the term 'rule-primarily based totally method is implemented to structures concerning human-crafted or curated rule sets. There are many algorithms for figuring out whether or not a sentence is sarcastic or now no longer. This paper complies with the Rule-primarily based totally method this is evolved to address the trouble of sarcasm detection and additionally describes numerous datasets. However we can in particular cognizance on Random Forest, Decision Tree, Support Vector Machine. In our project we are going to compare the accuracies from the model used and find the most accurate model giving high accuracy.