Reviewerly/exHarmony huggingface dataset:
Towards Generating Contextual and Empathetic Response for Covid-related Queries

This work addresses the vital need of keeping people informed with relevant, correct and essential information during the pandemic. Advanced NLP and machine learning mechanisms have been leveraged to generate responses to user queries through contextual conversation. In order to help people be discerning about what information they receive, a conversational system is proposed that identifies the correct intent of the query and a reinforcement Learning based generation model is used to proceed with conversation. We propose an end-to-end real-time text generation model that can respond to users queries on covid19. We created a new dataset with 1200+ covid-related questions from various sources and pre-processed them for a brief and direct answer. The dataset has also been manually observed to identify depressed questions and the responses are converted to be more empathetic. The dataset has been used to fine-tune DailoGPT, a GPT2-based transformer model to generate the responses related to COVID. COVID-related queries are bucketed into 15 categories to identify the exact intent of people. Our model generated both contextual and empathetic responses and achieved a human evaluation score of 3.48 (on a scale of 5) in terms of contextual relevance and a score of 2.12 (on a scale of 3).
Design of Blockchain DApps to Simplify GST and Letter of Credit Processes in Deregulated Financial Services

Most banking sectors and government entities depend on a centralised mechanism to perform various operations. This mechanism is often slow, inefficient, and unreliable. The Government levies Goods and Service Tax (GST) on the supply of goods and services. GST is a complex multistage indirect tax law applied from manufacturing to the end-user. The current GST implementation has a few loopholes where a seller can easily evade tax payments. Similarly, a Letter of Credit (LC) is a trade process mediated by banking partners. This process is often manual for sharing and validating documents between traders with or within countries for commerce. This paper explores the possibility of implementing GST and LC processes using blockchain technology and aims to address some of the issues faced in the current system. This paper proposes a decentralised application (DApp) to ease the operation logic of GST or e-way bills using smart contracts. The paper also explores a decentralised finance (DeFi) system using blockchain technology to simplify the LC process. This paper also discusses the implementation of the proposed smart contracts on a private blockchain network.
HM-NCDC: Hybrid Model for Novel Concept Drift Classification via Streaming Data

Concept drift is a phenomena that occurs when changes under the underlying distribution cause previously taught models to become insecure & inaccurate, which is referred to as the concept drift phenomenon. A significant amount of stream data is utilized to produce information for online learning. These data are generally influenced through modifications under unwanted ways, &are executed mainly via IoT, social media, & stock market. Expanded development regards available work on this area of detectors & concept drift, that are generally seek to fore’s location regards modification under to enhance whole accuracy through modifying base line learner. It is shown under this research that a Hybrid Model for Novel Concept Drift Classification through Streamlining (HM-NCDC) has been developed & tested under comparison to current ADASYN, EACD, & HLFR approaches. The information are accumulated through a huge scope correlation of 15 drift finders. In particular, the objective of this work is to assess the commitment of a locator to a classifier & to decide the most effective matching Hybrid Model for Novel Concept Drift. Because of these Hybrid Model for Novel Concept Drift examination studies, the exactness rates & assessment seasons of the still up under the air. They were additionally estimated under terms of their misleading up-sides, genuine negatives, bogus up-sides, genuine up-sides, drift characterization delay, & MCC. Furthermore, the Nemenyi test is utilized to consider the sets of procedures under contrast to different strategies under request to decide the method(s) for which there is a measurably massive distinction under performance. Following the aftereffects of the investigations, it are found that the best identifier matches, which changed relying upon the dataset type & size, primarily incorporated the HDDMA, RDDM, WSTD, & FHDDM locators, under addition to other things.
Video Conferencing with Sign language Detection

Video conferencing software has become an essential tool for communicating with one another across long distances. There are several video conferencing software that are utilised for communication all over the world. Huge numbers of people are unable to interact via spoken language and find typical video conferencing solutions difficult to use. Our project intends to solve this problem by creating a user-friendly Video Conferencing App that can identify sign language in real time and provide correct subtitles. Due to a lack of communication skills, deaf and hard of hearing persons confront several obstacles in their everyday lives. The covid epidemic has made traditional ways of communication extremely challenging for these people. Our goal is to bridge the gap by giving them a platform to showcase their skills.
VetaQL: Video Metadata Querying System Powered with Video Recommendation

Increasingly video sharing and OTT platforms aim to hyper-personalize their content for the users. This paper proposes a novel approach to dynamically recommend videos or similar events in other videos to users based on the detailed context at a particular time in a video. This concept opens up a new dimension for video recommendation. We ascertain the exact details of the video by modeling the video metadata into a generalized event model semantically, then storing this semantically linked event modeled metadata efficiently to recommend videos/events to users and demonstrate semantic video querying through the proposed VetaQL language.
Human Gesture Recognition Using CNN

In recent years due to our busy routine, we don’t want to waste time communicating with handicapped people, so we are proposing CNN-primarily based totally gesture popularity system. To resource characteristic extraction, Preprocessing techniques including morphological filters, contour construction, polygonal approximation, and segmentation. are employed in the training process and testing, and the outcomes are in comparison to current architectures and procedures. To ensure that the system is stable for the provided technique, all generated metrics and convergence graphs created at some stage in evaluation are analyzed and disputed. We evolved our project, which utilizes the Raspberry Pi, that's one of the nice methods for photo processing and video recording, to gather real-time hand gestures as entering and forecast signal languages in written form.
quEASY: A tilting interface to alleviate vehicle-induced motion sickness in passengers

Motion sickness in vehicles is elevated when passengers perform visual tasks. These tasks include reading, messaging, and navigating; while the increase in the number of autonomous vehicles liberates people from driving, today’s busy lifestyles create a need for passengers to perform these tasks during their commute. quEASY is a tilting software interface that helps reduce passengers’ motion sickness while they perform tasks on their digital devices. The concept of tilting the user’s head in sync with the car’s lateral acceleration proves to be effective in reducing motion sickness. Our prototype pushes lateral acceleration values (Y-axis g-force, from an Adafruit board) to a Python application that converts the obtained data into a tilt angle incorporated into the interface. User-testing proved that the concept holds true, while it points to issues in the user adapting to the new design changes in the usage of a novel interface. While further testing is needed, quEASY has the potential to be a significant relief for those who experience motion sickness while in a moving vehicle.
Streaming Platform Implementation in Banking and Financial Systems

Rapid development in information technology leads to modernizing the applications with innovations and technologies to achieve time to market, meet customer needs, better UI, security, and efficient system. Several modernization options are present in the market to achieve the above needs in Banking and Financial applications. Banking and Financial application are critical and adopt the newer technologies quickly to achieve performance and security. Nowadays, the end-user is using the technologies at their fingertips. BFSI (Banking, Financial Services and Insurance) enterprises widely spread and interact with each other. Enterprise application integration plays a crucial role in the BFSI domain to achieve technical and business benefits. Kafka is one of the technical solutions to build a real-time streaming platform between applications on moving data from one system to another. Streaming applications are scalable, fast, and durable. Streaming platforms are credit and different from Message Queues for advantages like Scalability, Message retention, multiple consumers, replication, message order, and TCP protocol support. Banking and financial application backend applications, especially batch processing systems, migrate from batch to stream platform to achieve near real-time data. As a result, the processing/settlement processing time is minimized in banking and settlement systems. This paper discusses two case studies and identifies the advantages and disadvantages of streaming platforms.
(2,2) Visual Cryptography Based Biometric Authentication Mechanism for Online Elections

Voting is a process of choosing leader who we trust and will work for our benefit. The process of choosing the leader is currently through EVM having the facts to be considered like Voter Authentication, Security of vote casted and Vote anonymity along with Vote verification. The very first thing which requires to improve is moving from EVM voting to Secure Electronic Voting where digitization plays an important role in providing the access to vote from anywhere which meets the current need for keeping us updated with technology and serving the citizens with the advanced Technology. Keeping Electronic voting in mind here we propose the very first step towards new way of voting that is voter authentication. Using visual cryptography schemes, we can here implement the voter authentication mechanism using fingerprint matching. Fingerprint matching is one of the biometric which can be considered as one of the key steps towards voter authentication along with visual cryptography.
Comparative Analysis of Text-based Video Summarization Techniques using Deep Learning

Rapid advancements in video technology, as well as the widespread availability of video capturing equipment, generate large amounts of data that must be managed efficiently in order to improve usability. Automatic video summarizing is a method of navigating through large amounts of data and producing a short summary that can be used in a variety of applications. Traditional video summary, on the other hand, creates concise videos with no discernible qualitative or quantitative loss of content. It just considers the most significant aspects of the video while providing a summary that is independent of the user’s interest. Because video summarizing is a highly subjective activity that cannot be performed by traditional approaches, people are more interested in customized summaries. The solution to this challenge is multi-model video summarization, which aids in the production of user-interested summaries by taking into account a variety of factors. This paper discusses the different approaches used in query based i.e. text-based video summarization and enlighten the pros and cons of existing methods to help research community to work in this direction.
A Passive-Aggressive Classifier for Finding Actors and Use Cases from Requirement Documents

The Software Requirement Specification (SRS) document, which is written in Natural Language, contains the various functional and non-functional requirements of the system. In our work, we investigate the passive-aggressive classifier for finding actors and use cases from a collection of requirement documents. The actors and use cases extracted from the requirement document can be used for plotting the Use Case model of the system. This work is a small step in our attempt to automate the Requirement Engineering phase of the Software Development Life cycle (SDLC). In today's world, to meet varying market dynamics, technological disruptions, and customer's ever-changing needs, agile practices are heavily used for software development. Automating the requirement engineering phase is highly necessary in such a context of fast-changing requirements.
A Novel Approach for Small-Object Detection based on Fine-tuning and Sliced Inference

Small object detection has a wide range of applications in a variety of fields, including unmanned aerial vehicles (UAVs), surveillance, agriculture, and many more. A significant problem in surveillance applications is the detection of small objects and items that are located far away in the image. As a result, they are difficult to identify with traditional detectors since they are represented in the image by a small number of pixels and insufficient information. Investigation of the state-of-the-art performance of YOLO-based object detection models for the problem of small object detection is motivated by the fact that they are one of the most popular and straightforward object detection models to use. In this research, YOLOv5 and YOLOX models have been used. Aside from that, the consequences of sliced inference as well as the effects of modifying the model for slicing aided inference have been taken care of. The model was trained and evaluated using the VisDrone2019-Det dataset. This dataset presents a challenge in that the majority of the items are small when compared to the image sizes in the dataset. The purpose of this work is to contrast and compare the YOLOv5 and YOLOX models for small item detection. The use of sliced inference has been shown to raise the AP50 score in all tests; however, this effect was more pronounced for the YOLOv5 models when compared to the YOLOX models. A significant improvement was observed for all models when the combined effects of slice fine-tuning and slice inference were considered. A total of 48.8 points was obtained by the YOLOv5-Large model on the VisDrone2019-Det testdev subset, making it the model with the highest AP50 score.
A Classification Approach in the Probability of Credit Card Approval using Relief-Based Feature Selection

Credit card approval is important in all financial businesses; an applicant may deny and approve their credit card based on their financial record and personal information. Right decision-making for issuing credit cards for applicants is important, it may lose the financial asset of the companies if may had a bad decision in issuing the credit card. This study used Credit Card Approval in an Open Dataset online repository with 19 attributes and 304, 356 instances, and also used different classification models to predict the best attributes in the dataset. 10-fold-cross validation is used for testing and training the data, and ReliefF-Based feature selection to rank the best attributes in the dataset and shows that 12 attributes and 132, 492 instances remained. As a result, occupation type is the best attribute that may consider in applicants' records, and the flag of their own car is the least. The best classification model in the study is Random Forest gained 95.76%, followed by KNN with 94.37%, and lastly NN has 71.56%. This result is evaluated using a confusion matrix for error rate and ROC curve for handling overfitting in an unbiased result. This approach can be a significant input in a financial institution when implemented in an online pre-evaluation system instead of a manual assessment.
Project Depolymerization: Tokenization of Blockchains

GNU Taler is an electronic payment system implemented as Free Software. The goal of this project is to enable payments with blockchain-based cryptocurrencies in GNU Taler.Blockchain-based cryptocurrencies come with their own special set of challenges, such as lack of finality, unpredictable delays and fees in transactions. Our work analyzes the limitations that arise from using a blockchain as a settlement layer for GNU Taler and describes ways to mitigate their impact. By proving that blockchains can be used as a settlement layer for GNU Taler, we show that it is not only capable of handling bank money, but also widely used cryptocurrencies.For cryptocurrencies owners, this integration offers a new solution for instant and low-cost payments that can scale beyond blockchains limitations while preserving or enhancing privacy.
Optimizing multi-party agreement protocols

Blockchain technology has developed rapidly over the last few years. A Blockchain network uses the multi-party agreement concept to reach an agreement on the transaction confirmation. As the technology is new, tools and techniques for blockchain verification are still under development. We explore optimization techniques of weighted multi-party agreements with stochastic properties in the current work. We represent a multiparty agreement as a discrete-time Markov chain and specification in a probabilistic linear temporal logic form. We demonstrate how to change the protocol parameters to satisfy the specification and illustrate the work of algorithms in experiments. Finally, we discuss the usage of proposed algorithms in the Hyperledger Fabric framework.
Spatially-Excited Attention Learning for Fine-Grained Visual Categorization

Learning distinguishable feature embedding plays an important role in fine-grained visual categorization. The existing methods focus on either designing a complex attention mechanism to boost the overall classification performance or proposing a specific training strategy to enhance the learning of the backbone network to achieve a low-cost backbone-only inference. Unlike all of them, an alternative approach called Spatially-Excited Attention Learning (SEAL) is proposed in this paper. The training of SEAL is similar to that of most of the existing methods, but it provides two alternative streams during a network inference: one stream requires higher effort but provides higher performance; the other is a low-cost backbone-only inference with lower but still comparative performance. Note that both the streams are trained at the same time by SEAL. The experiments show that SEAL achieves the state-of-the-art performance under both complex architecture and backbone-only inference conditions.
Byzantine Fault-Tolerant Time in Hyperledger Fabric

We have recently become witnesses of the constantly growing adoption of distributed ledger technology utilized in various domains, whereas for enterprise-grade solutions, the most prominent platform is Hyperledger Fabric [1]. However, the lack of a trusted source of time within the platform poses certain limitations in how this platform could be used. In particular, there is no built-in mechanism to ensure the expiration of the signing certificates during the validation phase of the transactions processing pipeline, which introduces severe concern from the platform’s usability and security point of view. Therefore, we would like to introduce a solution that incorporates Byzantine Fault Tolerant source of time into Hyperledger Fabric, where the suggested approach utilizes opened sourced implementation of SmartBFT [2] distributed consensus protocol.
Availability-Aware Multi-Objective Cluster Allocation Optimization in Energy-Efficient Datacenters

Changing workload structures in data centers due to network virtualization puts much pressure on data center networks. This is especially true for service-oriented applications that have a time-bounded response. For simplicity of administration, network virtualization consolidates computer resources and network operations into a single software-based administrative unit called a virtual network. As more people send requests to the server at once, it becomes slower. Buying a new server is too costly. Therefore, we build a virtual system to make it easier to manage this issue. As the number of cloud apps deployed in datacenters grows, this tendency will continue. There are several physical machines (PM) in a data center that are linked together via switches. Virtualization technology is often used to share PM hardware resources to provide scalable and elastic computing capabilities. This virtual cluster includes several virtual machines, each of which uses PMs' resources as needed. As more applications are installed, the energy consumption of a data center increases. The assignment is referred to as virtual cluster allocation. This paper discusses cloud computing’s virtual networking, cluster building, and virtualized data center architecture and automation. VMs and virtual clusters are used to demonstrate dynamically organized group, grid, and cloud architectures.
Algorithmic balancing of hashrate in a Proof-of-Work (PoW) consensus protocol

We present an algorithmic protocol which has been developed as a solution to the inherent centralisation trend within blockchain mining networks (driven largely by competitive greed). The protocol is designed for Proof-of-Work (PoW) blockchains and invokes a thermodynamic approach to move the network towards mining homogeneity, optimising decentralisation. The importance of such a system is underscored by (i) the premise that centralisation should be viewed as an attack on the fundamental requirement of decentralisation that allows a blockchain network to function as a solution to the Byzantine Generals’ Problem (BGP), and (ii) the fact that runaway mining hetereogeneity effects a runaway energy cost that has the potential to cause environmental harm. We introduce the algorithmic balancing protocol as a solution to the allegorical Cheating Athlete’s Problem (CAP).
Enhancing Trust in Industry 4.0 Traceability Data using Confidentiality-Preserving Digital Ledger

Industry 4.0 brings major changes in manufacturing process management. Third parties, such as providers, customers and suppliers are more prone to interact with the traceability system of a factory in order to obtain full transparency on a product manufacturing process. Several solutions, including blockchain-based approaches, have been proposed in order to reinforce trust in such systems. However, many factory owners are still sceptic about deploying such solutions, notably due to confidentiality threats to the factory’s data that could be included in traceability data. This paper investigates a way to include critical data of a factory in a blockchain-based traceability system, and to validate the related transactions with third parties without compromising confidentiality. We describe our proposal for a digital ledger based traceability system using the Multichain platform. In addition to confidentiality-preserving properties, we show how other issues such as energy consumption and storage volume could be controlled through Multichain functionalities.