Reviewerly/exHarmony huggingface dataset:
From Token to Word: OCR Token Evolution via Contrastive Learning and Semantic Matching for Text-VQA

Text-based Visual Question Answering (Text-VQA) is a question-answering task to understand scene text, where the text is usually recognized by Optical Character Recognition (OCR) systems. However, the text from OCR systems often includes spelling errors, such as "pepsi" being recognized as "peosi". These OCR errors are one of the major challenges for Text-VQA systems. To address this, we propose a novel Text-VQA method to alleviate OCR errors via OCR token evolution. First, we artificially create the misspelled OCR tokens in the training time, and make the system more robust to the OCR errors. To be specific, we propose an OCR Token-Word Contrastive (TWC) learning task, which pre-trains word representation by augmenting OCR tokens via the Levenshtein distance between the OCR tokens and words in a dictionary. Second, by assuming that the majority of characters in misspelled OCR tokens are still correct, a multimodal transformer is proposed and fine-tuned to predict the answer using character-based word embedding. Specifically, we introduce a vocabulary predictor with character-level semantic matching, which enables the model to recover the correct word from the vocabulary even with misspelled OCR tokens. A variety of experimental evaluations show that our method outperforms the state-of-the-art methods on both TextVQA and ST-VQA datasets. The code will be released at https://github.com/xiaojino/TWA.
Levy-Lieb embedding of density-functional theory and its quantum kernel: Illustration for the Hubbard dimer using near-term quantum algorithms

The constrained-search formulation of Levy and Lieb provides a concrete mapping from N-representable densities to the space of N-particle wavefunctions and explicitly defines the universal functional of density functional theory. We numerically implement the Levy-Lieb procedure for a paradigmatic lattice system, the Hubbard dimer, using a modified variational quantum eigensolver approach. We demonstrate density variational minimization using the resulting hybrid quantum-classical scheme featuring real-time computation of the Levy-Lieb functional along the search trajectory. We further illustrate a fidelity based quantum kernel associated with the density to pure-state embedding implied by the Levy-Lieb procedure and employ the kernel for learning observable functionals of the density. We study the kernel's ability to generalize with high accuracy through numerical experiments on the Hubbard dimer.
Comparative Study of Conditional Generative Models for ISL Generation

Deep Generative Models are widely used to generate data that look similar to training data. Synthesis and sampling of data using Deep Generative Models can be useful for certain situations where generating data by hand would be expensive or time consuming. Data-sets for Indian Sign Language often are of small size, which hinders training of Deep Learning models to a good accuracy. In this project we attempt to compare various state-of-the-art Conditional Generative Models for Indian Sign Language Recognition task and evaluate them using various performance metrics.
Virtual Immersive Platforms as a Strategic Innovative Destination Marketing Tool in the COVID-19 Era

The aim of this research was to address the issues in virtual-reality technologies and gamification within the context of immersive technologies. The author drew together, in a hybrid methodology, the principles of virtual touring and online training, as these need to be understood by destination and tourism professionals. The emphasis was on the utility of innovative multimedia and multimodal technological tools for modern content creation, which led to an online training process for travel agents, the role of gamification and how a virtual platform could be developed to promote a tourism destination during the COVID-19 pandemic. The author based the research on a case study of an immersive platform of a Greek destination and on the degree of the user response to the virtual technology and content. The study also included a comparison between two platforms: one using conventional content and one using multimedia content. The author methodologically used VAR models to describe the cogeneration process of multiple variables over time to investigate the relationships between these variables. The results led to a preference for immersive platforms, which can be a guide for corresponding platforms that are concerned with the promotion of destinations.
OpenDesign of Scientific Research in Pandemic Context

Abstract The Coronavirus pandemic and the consequent situation of social isolation have affected research design, especially in fields working directly with people. While online tools enable communication and collaboration, some specific domains involving contemporary technology-based systems, such as socioenactive systems, demand a deeper understanding of human-embodied action in real-world scenarios with ubiquitous technology. This work addresses this issue by presenting a study in which a research team used the OpenDesign Platform (ODP) to conduct a process of ideation and consolidation of a socioenactive-based system solution during pandemics and physical presence limitations. The contributions are twofold: addressing the demands of the research team working remotely and envisaging an interaction scenario for people in a socioenactive system, even without their copresence in the same physical space. Results have shown the strengths and weaknesses of the ODP and lessons learned towards supporting systems for teamwork in such systems’ ideation context.
Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering

Abstract Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clustering multivariate continuous data. However, the practical usefulness of these models is jeopardized in high-dimensional spaces, where they tend to be over-parameterized. As a consequence, different solutions have been proposed, often relying on matrix decompositions or variable selection strategies. Recently, a methodological link between Gaussian graphical models and finite mixtures has been established, paving the way for penalized model-based clustering in the presence of large precision matrices. Notwithstanding, current methodologies implicitly assume similar levels of sparsity across the classes, not accounting for different degrees of association between the variables across groups. We overcome this limitation by deriving group-wise penalty factors, which automatically enforce under or over-connectivity in the estimated graphs. The approach is entirely data-driven and does not require additional hyper-parameter specification. Analyses on synthetic and real data showcase the validity of our proposal.
A hybrid combined algorithm based on intelligent water drops and electromagnetism-like algorithms for fuzzy TSP1

This paper presents a new combined algorithm for the fuzzy Travelling Salesman Problem (FTSP) based on a composition of the Intelligent Water Drops (IWD) and the Electromagnetism-like (EM) algorithms. In a FTSP, the time consumed distance between cities i and j can be described by vague knowledge, such as fuzzy quantity. The main goal of FTSP is to achieve the minimum distance of Hamilton circuit of G graph, where the Hamilton circuit is a closed route of cities (i.e., nodes) of G that have been visited only once. The proposed algorithm transfers the generated responses by the IWD to the EM, where the best answer is selected. Importantly, the computed results from both algotithm are compared and the best is accumulated. In other words, in each iteration, the best result is collected by comparison between the current and previous hierarchies until the halt condition is fulfilled. Finally, the results of the genetic algorithm (GA), IWD and EM algorithms are compared, so that the efficiency of the proposed combined IWD-EM algorithm is determined.
Robust self-testing of multipartite Greenberger-Horne-Zeilinger-state measurements in quantum networks

Self-testing is an examination for characterizing unknown quantum devices based on correlations of observed statistics in a black-box scenario. With development of large-scale quantum networks, the requirement for self-testing multipartite entangled measurements has become very demanding. We develop here a general procedure for self-testing arbitrary generalized Greenberger-Horne-Zeilinger-state (GHZ-state) measurements which applies to any number of parties. Moreover, it turns out that the existing result for the three-qubit GHZ-state measurement is recovered as a special case. Our results will motivate operational certification of quantum devices related to device-independent quantum information tasks for various scenarios in complicated quantum networks.
Least Privilege across People, Process, and Technology: Endpoint Security Framework

A common target of cyberattacks today is the vulnerable endpoint device, which can be exploited by hackers to gain access into an organization. This paper presents a theoretical framework for addressing endpoint security by leveraging the principle of least privilege across the overlapping domains of people, process, and technology in organizations. The framework emphasizes nine key elements to endpoint security with associated policy statements designed to promote an organizational culture favorable to least privilege thinking. Leveraging an action design research methodology, we integrated the proposed managerial tool in an organization and incorporated feedback from industry professionals to evaluate it and to generate ideas for the development of a commercial endpoint security application. As a contribution, this framework is one of the first scholarly efforts to apply the principle of least privilege to endpoint security which can be valuable to cybersecurity consultants and academics.
Classification of Hadamard products of one-codimensional subcodes of Reed–Muller codes

Abstract For Reed–Muller codes we consider subcodes of codimension 1. A classification of Hadamard products of such subcodes is obtained. With the use of this classification it has been shown that in most cases the problem of recovery of the secret key of a code-based cryptosystem employing such subcodes is equivalent to the problem of recovery of the secret key of the same cryptosystem based on Reed–Muller codes, which is known to be tractable.
Massive visual long-term memory is largely dependent on meaning

Previous research demonstrated a massive capacity of visual long-term memory (VLTM) for meaningful images. However, the capacity and limits of a "pure" VLTM that is independent of conceptual information still need to be determined. In the encoding phase of three experiments, participants viewed hundreds of images depicting real-world objects, along with visually similar images that were stripped of their semantic meaning. VLTM was evaluated using a four-alternative-forced-choice test including old and new images and their counterpart mirror transformations. The results revealed superior memory for meaningful than for meaningless stimuli and importantly, there was no hint of a massive VLTM for the meaningless items. Furthermore, when examining memory recognition of visual properties per-se (i.e., original/mirror state), memory was overall poor, and practically negligible for the meaningless items. Taken together, our findings suggest that meaning is critical for massive VLTM and for the ability to store visual properties.
Detection and Classification of Various Types of Leukemia Using Image Processing, Transfer Learning and Ensemble Averaging Techniques

Leukemia, often known as blood cancer, is a frequent and deadly illness. The abrupt rise of White Blood Cells in the blood causes a dangerous illness. Leukemia starts in the bone marrow and spreads throughout the body. It is responsible for the development of a huge number of cells that are aberrant. Leukemia can be categorized into four types. They are Acute myeloid leukaemia, Acute lymphoblastic leukaemia, chronic lymphocytic leukaemia, and Chronic myeloid leukemia. The early detection of leukemia plays crucial role. The detecting process will be more accurate if the diagnosis is computer-assisted. This project aims at developing an approach for detecting leukemia using machine learning techniques such as image processing and transfer learning which provides ways to see if it's a case of AML, ALL, CML, or CLL. Leukemic and healthy lymphocytes have markedly different morphological components. The lymphocyte images have been used to extract a variety of characteristics. The leukemia is further classified into its types by using a hybrid model constructed with the ensemble averaging of Inception, Xception convolutional models.
Gender and Age Estimation using Transfer Learning with Multi-tasking Approach

The estimation of Age and Gender of a Human being using facial characteristic is getting lot of attractions these days due to wide variety of its applications in the real-world scenarios. Significant work has been carried out in this regard and various methods and approaches has been published which have offered very good results as far as accuracy are concerned. This paper brings out a comparison of most famous pretrained models for estimating both gender and age of a person in videos using multi-tasking approach enabling a single model to estimate both age as well as gender. The estimation accuracy of EfficientNetV2B1 model using multi-tasking approach provides good accuracy (of 90.31) and MAE (of 0.063) compared to other models. The publicly available dataset UTKFACE has been used for training the multi-tasking CNN.
Business Meeting Summarization System

As the economy expands, so does the quantity of production and sales in a firm; as a result, a substantial number of business meetings are conducted day-to-day to make crucial choices. The importance of business meetings cannot be overstated. It enables you to maintain track of the organization’s processes and operations to achieve the organization’s goals and objectives. The business meeting findings must be kept up to date by a large number of individuals. Conventionally, people had to read long meeting reports or talk to meeting attendees to get the gist of the meeting. This summarization tool helps the user to gain the information shared in a meeting with just one click. It can be used in various domains like education, healthcare, business, etc. Existing summarization systems are limited to only the English language. This work demonstrates summarizing a business meeting held in regional or professional languages with the help of a machine learning model. The summarization is done using the abstractive method wherein words are allocated based on their frequency of occurrence in the text file. The machine learning model is connected to the NodeJS server application with the help of a python connecter. To overcome the current barriers, this system takes audio input of Hindi or English language from the user end, summarizes it using ML techniques which improve overall accuracy and provide the output in any desired language.
Image Captioning Using CNN and RNN

Image captioning is a new task that has recently gained a lot of attention. It has many potential real life applications like virtual assistant, intruder alert in cctv functions and many more. A person can call out and explain an infinite number of elements of a visual situation with a quick glimpse. But automatically describing what’s in a photograph or image has always been a difficult task in Artificial Intelligence. Here the purpose is to summarise whatever is presented in the image in a single phrase, such as the items involved, their attributes, the activities being done, the interaction between the elements, and so on. The implementation of an Automatic Caption Generator employing CNN and RNN-LSTM models is described in this work. It integrates contemporary machine translation and computer vision research. Flickr8k was utilised as a dataset.
Reinforcement Learning for Speech Recognition using Recurrent Neural Networks

This work describes a voice recognition system that does not need an intermediate phonetic representation to convert audio input to text. The system is based on a mix of the the Connectionist Temporal Classification goal function and deep bidirectional LSTM recurrent neural network architecture . A new method is proposed in which the network is taught to reduce the likelihood of an arbitrary transcription loss function being encountered. without the aid of any lexicons or models, this allows for a direct optimization of WER. The system has a WER (word error rate) of 22 percent, 20 percent with simply a lexicon of authorized terms, 9 percent using a trigram language model. The error rate drops to 7 percent when the network is used in conjunction with a baseline system.
A Combined PCA-MLP Network for Early Breast Cancer Detection

Breast cancer is the second most responsible for all cancer types and has been the cause of numerous deaths over the years, especially among women. Any improvisation of the existing diagnosis system for the detection of cancer can contribute to minimizing the death ratio. Moreover, cancer detection at an early stage has recently been a prime research area in the scientific community to enhance the survival rate. Proper choice of machine learning tools can ensure early-stage prognosis with high accuracy. In this paper, we have studied different machine learning algorithms to detect whether a patient is likely to face breast cancer or not. Due to the implicit behavior of early-stage features, we have implemented a multilayer perception model with the integration of PCA and suggested it to be more viable than other detection algorithms. Our 4 layers MLP-PCA network has obtained the best accuracy of 100% with a mean of 90.48% accuracy on the BCCD dataset.
RGB-D Scene Classification using an Ensemble of Convolutional Neural Networks with Softmax Aggregation

Scene classification using a combination of depth images and color images has shown better performance in terms of classification accuracy. Convolutional neural networks are widely used for scene recognition tasks. The fusing of depth information with color information can be done with different convolutional neural networks. Ensemble methods are proven to increase classification accuracy. However, very few studies have been reported on the use of ensembles methods with convolutional neural networks. This study investigates a few architectures to combine depth modality with color modality. We also employ an ensemble method by aggregating outputs of the softmax activation function at the output layer. Our ensemble method provides better scene recognition accuracy compared to standalone architectures.
A Machine Learning-Based Automatic Feedback System to Teach Cybersecurity Principles to K-12 and College Students

Feedback is an essential part of education to help students understand and learn from their mistakes. However, while students learn new content, there is mostly no live person to provide feedback, especially in a virtual environment. Therefore, there are many software for automated code reviews to provide feedback to programming language learners. However, there are no available auto command review tools for security tools except for each tool itself and operating system suggestions. There is also no feedback tool that constructively provides feedback according to learners’ experiences in security subjects while learners practice with commands. Therefore, we developed an automatic feedback system that uses machine learning to create customized student feedback on cybersecurity topics. The foundation of the software was completed and tested in 2 undergraduate introductory computer science courses. Survey results collected from users indicate that the automatic feedback system improved the learning experience of 46% of successful participants and that 77% of successful participants were interested in the continued development of the system. 88% of successful participants felt that the system taught basic command-line skills effectively.
Hybrid Ensemble Machine Learning Approach for URL Phishing Detection

Phishing has been a massive issue on the Internet. It is a sort of Internet fraud. Thus, no antivirus or other type of technological security can eliminate this. But there has been research going on and executed from intellects across the globe to fight this online fraud. Researchers have also come up with additional strategies to deal with this problem. The two main focused techniques taken to tackle phishing are Black Listing and Machine Learning. Machine Learning is a fresh and ingenious way to tackle phishing. For this thesis, I went with the standard and Hybrid ensemble Machine Learning-based approach to dive phishing. This thesis consists of a comparative study of different Machine Learning algorithms like Decision Tree, Naive Bayes, Logistic Regression and Hybrid Ensemble Stacking algorithm in order to know the prediction that these standard machine Learning algorithms make. The results obtained by them show accuracy of 87.04%, 85.59%, 86.18% and 89.25% respectively.
A Distributed Multi-Sensor DCNN &amp; Multivariate Time Series Classification Based technique for Earthquake early warning

An early warning system for earthquakes could save a lot of lives. It can also be used to keep track of critical infrastructure and assets. Recent earthquake prediction research aimed at developing based on the arrival of P waves and seismicity indices, an EEWS can be categorised into two groups.The first method can generate alerts in a matter of seconds. Using Deep convolutional neural networks, a novel seismicity indicator-based EEWS model. In this study, neural EEWS (NEEWS) is described as a method for predicting earthquake size and location before they happen. When an earthquake is discovered early enough, people can flee unsafe areas sooner.EEW system generates an alarm before it generates an earthquake. the magnitude of the earthquake must be determined. The number of people who benefit from EEW systems is determined by how far they are from such powerful incidents. As a result, pinpointing the exact site of these tremors is crucial for inhabitants' peace of mind.As a result, using earthquake Ml magnitudes, this article suggests a magnitude, location, and various parameters involved. Magnitude prediction algorithms have been created and trained for typical magnitude ranges that provide desired values utilising available records as a comprehensive data set. The suggested technique is pinned on a Deep convolutional neural network (DCNN) that can extract significant characteristics from extracted waveforms, allowing the classifier to achieve a reliable performance in the essential frameworks of the earthquake along with multisensor functioning. The suggested approach has enhanced classification and accuracies.