Reviewerly/exHarmony huggingface dataset:
Design and Developmement of Offline Handwritten Text Recognition leveraging Machine Learning Techniques

With the rise of Convolutional neural networks in recent times, deep learning became much easier and powerful in image classifications. This project started with the intention of creating a neural network that can detect numbers from 0 to 9. This project has taken the input from a famous dataset called MNIST. To interact with the neural network, created a good-looking Graphical User Interface [GUI] using Tkinter and some other python packages. As an upgrade to our project to be more useful and more powerful MLkit will be used, it is a machine learning framework from Google. Using android studio and Java as the primary language, this android application was created. Using a mobile camera to give input images, can detect so much more than text from images.
Video Summarization using Deep Convolutional Neural Networks and Mutual Probability-based K-Nearest Neighbour

The video summarisation is an advanced mechanism for enabling users to handle and browse large videos in an effective manner. Various video summarisation methods are developed in recent days, in which handling of synchronisation and timing issues remain as the important challenge. The proposed video summarisation technique produces a short summary from the huge video stream. Initially, from an input database, the cricket videos containing number of frames are fed to keyframe extraction unit. Here, the keyframe extraction is done by the Euclidean distance and discrete cosine transform, and the best keyframes are selected based on the Euclidean distance. The residual frame is obtained by passing the input frames through deep convolutional neural network. Then, the similarity is calculated by Bhattacharyya distance. For video summarisation process, the optimal frameset is evaluated by matching residual keyframe with obtained keyframes. Here, input queries consisting of face object are subjected to object matching process, which is performed using the proposed mutual probability-based k-nearest neighbour (MP-KNN) to obtain relevant frames based on texture features. The performance of the proposed MP-KNN is superior based on precision, recall, and F-measure with values 0.963, 0.960, and 0.909, respectively.
Auxiliary tasks to boost Biaffine Semantic Dependency Parsing

The biaffine parser of Dozat and Manning (2017) was successfully extended to semantic dependency parsing (SDP) (Dozat and Manning, 2018).Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of tokens).To circumvent such an independence of decision, while retaining the O(n 2 ) complexity and highly parallelizable architecture, we propose to use simple auxiliary tasks that introduce some form of interdependence between arcs.Experiments on the three English acyclic datasets of SemEval 2015 task 18 (Oepen et al., 2015), and on French deep syntactic cyclic graphs (Ribeyre et al., 2014) show modest but systematic performance gains on a near state-ofthe-art baseline using transformer-based contextualized representations.This provides a simple and robust method to boost SDP performance.
3D scanning of objects by projection of three incoherent RGB lines with Gaussian profile

The projection of one light line is a widely used technique for the 3D scanning of objects; however, its main disadvantage is the large number of stored images and the time it takes to captured them. In this work, it is proposed 3D scanning of objects with colored surface by projecting of three incoherent RGB lines with Gaussian profile which is generated numerically using a multimedia projector. Each color line (red, green and blue) simultaneously scans a third of the object to be reconstructed thus reducing the number of stored images, favoring the optimization of the capture time compared to the projection technique of one light line. To carry out our proposal, algorithms of light line segmentation by color and algorithms for the extraction of the skeleton with sub-pixel resolution based on Bezier polynomials were implemented. The height measurements obtained on a test object were compared with a coordinate measuring machine (CMM); an average accuracy error of 0.2192 mm was achieved. The good results obtained in the 3D scanning of different objects support our conclusions.
A fast encryption method for enterprise financial data based on blockchain

Aiming at the problems of poor encryption security and long encryption time in the existing enterprise financial data encryption, a fast encryption method of enterprise financial data based on blockchain is proposed. Firstly, the enterprise financial data is collected from the enterprise source database with the help of ETL Technology, and the collection is completed with the help of full extraction; Then, the interference data in the data is filtered with the help of Bloom filter, and the data index position is determined with the help of hash function mapping to complete the preprocessing. Finally, with the help of the data layer, network layer, consensus layer, motivation layer and application layer in the blockchain, the data and related encrypted data and timestamp are encrypted in each node, and the data encryption key is generated to complete the rapid encryption of enterprise financial data. The experimental results show that the security coefficient of enterprise financial data encrypted by the proposed method is high, and the encryption time is short.
On Learning Hierarchical Embeddings from Encrypted Network Traffic

This work presents a novel concept for learning embeddings from encrypted network traffic. In contrast to existing approaches, we evaluate the feasibility of hierarchical embeddings by iteratively aggregating packet embeddings to flow embeddings, and flow embeddings to trace embeddings. The hierarchical embedding concept was designed to especially consider complex dependencies of Internet traffic on different time scales. We describe this novel embedding concept for the domain of network traffic in full detail, and evaluate its performance for the downstream task of website fingerprinting, i.e., identifying websites from encrypted traffic, which is relevant for network management, e.g., as a prerequisite for QoE monitoring or for intrusion detection. Our evaluation reveals that embeddings are a promising solution for website fingerprinting as our model correctly labels up to 99.8% of traces from 500 target websites.
Explainable Artificial Intelligence: Requirements for Explainability

To date, many reasons have been suggested for making explainable artificial intelligence (XAI) models. However, it is unclear when the XAI suggested content is considered an explanation. This paper conducts a survey to determine the requirements for the information to be considered an explanation. Four minimum requirements have been prioritized based on the impact of the change they present to distinguishing between information and explainability.
KunlunTVM: A Compilation Framework for Kunlun Chip Supporting Both Training and Inference

With the rapid development of deep learning, training big neural network models demands huge amount of computing power.Therefore, many accelerators are designed to meet the performance requirements. Recently, series of Kunlun chips have been released, which claim comparable performance over GPUs. However, there lacks an end-to-end compiler to support both training and inference on Kunlun chip,leaving large performance optimization space to be explored. This paper presents KunlunTVM, the first end-to-end compiler based on TVM, supporting both training and inference tasks on Kunlun Chip. Experimental results show that KunlunTVM achieves up to 5x training performance improvement over the existing framework PaddlePaddle supporting Kunlun chip. It is noteworthy that the proposed methods are general and extensible for the TVM framework targeting different backends.
An Access Control Mechanism for Geospatial Image Security Using Encrypted Pyramid Structure

Geospatial Image Data have grown immensely both in quantity and quality due to rapid progress in remote sensing technologies. Due to low cost and easy availability, geospatial data are core component of many applications such as disaster management, Town planning, environmental observations, military systems, etc. Enterprise level Geographical information system (GIS) applications have been the mainstream way of accessing the geospatial data, making data to be operated in a distributed environment. This poses a serious threat to spatial and non-spatial information carried by these data, especially for security and privacy critical applications. Also, data has to be replicated with different copies based on the role and usage of end user. This not only increases the storage but, also requires more effort to distribute and maintain for different users. So, there is always a need for an efficient security mechanism to protect these high-resolution satellite images. In this paper we propose a novel access control mechanism which solves two essential problems of geospatial image sharing in a distributed network viz. restricted usage of image content to authorized persons in the network and transmission of highvolume image content in a band width hungry network so as to make the image available to the user within a permissible delay. A resolution access control mechanism is designed where each user can access different resolution for different user categories from a single image file.
Machine Learning Approach for Credit Card fraud Detection

Abstract: Finance fraud is a growing problem with far consequences in the financial industry and while many techniques have been discovered. Data analysis is to be applied to finance databases to automate analysis of huge volumes of complex data. Data analysis has also played a salient role in the detection of credit card fraud in online transactions. Fraud detection in credit card is a data analysis problem, It becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviors change frequently and secondly due to reason that credit card fraud data sets are highly skewed. This project propose investigation and to check the performance of various algorithms on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 284,786 transactions. These techniques are applied on the raw and preprocessed data. The performance of the techniques is evaluated based on accuracy, sensitivity, and specificity, precision.
Analysis of automatically generated embedding guides for cell classification

Automated cell classification in human bone marrow microscopy images could lead to faster acquisition and, therefore, to a considerably larger number of cells for the statistical cell count analysis. As basis for the diagnosis of hematopoietic dis-eases such as leukemia, this would be a significant improvement of clinical workflows. The classification of such cells, however, is challenging, partially due to dependencies between different cell types. In 2021, guided representation learning was introduced as an approach to include this domain knowledge into training by providing “embedding guides” as an optimization target for individual cell types. In this work, we propose improvements to guided repre-sentation learning by automatically generating guides based on graph optimization algorithms. We incorporate information about the visual similarity and the impact on diagnosis of mis-classifications. We show that this reduces critical false predictions and improves the overall classification F-score by up to 2.5 percentage points.
Performance Analysis of Class Imbalance Handling Techniques for Early Sepsis Prediction using Machine Learning Algorithms

Sepsis is a life-threatening condition that may cause mortality in ICU patients. Researchers have formerly developed Machine Learning (ML) models to predict Sepsis and other medical conditions. Performance of ML models dependent on the quality of the data set used for training. Class imbalance is one of the problems observed in medical data sets. In this paper, we have presented a comparative study of three class imbalance handling techniques to identify the best among the three. We made a comparison on performance metrics of predictive models developed using ML classification algorithms. We proposed a four-phase approach for predictive model development. Consequently, we used healthcare parameters available at any peripheral basic health facility and do not require laboratory investigations. Further, we proposed a novel machine learning predictive model, &#x201C;Sepsis Prediction Model for Peripheral Hospitals (SPMPH), &#x201D; for sepsis prediction in ICU patients. SPMPH provides better accuracy (.95), precision (.98), recall (.91) and AUROC (.978) as compared to other available models.
Blockchain Based Electronic Voting Protocol

SQL injection attack is now the most common server-side attack in web applications whereby malicious codes are injected into the database through user input fields by unauthorized users and this could lead to data loss or in the worst case, to database hijacking. The utilization of Blockchain technology in e-voting applications is not a new thing. Many systems have been proposed using cryptography and other security techniques. In those systems, minimal involvement of third party is observed, a problem of coercion resistance and transparency maintenance at the same time is observed and most processes have not been implemented to evaluate the systems further. This paper applies the cryptographic signatures to validate the origin and integrity of the votes by preserving the voter’s choices during the election process. Furthermore, authors provide several possible extensions and improvements that can be made as an addition to the scope of this research.
Text Augmentation for Compressed Image Captioning Models

The field of compressing image captioning models to be suitable for real-time mobile devices usage remains under-explored despite its high practical value. Recent researches showed a huge progress in this topic compressing classical state-of - the-art models using basic models size reduction methods. However, some more sophisticated approaches that showed great results for ordinary image captioning models quality improve-ments are often left behind. One of such techniques is captions augmentation. It appeared to help for big uncompressed models but its influence on smaller models metrics wasn&#x0027;t clear. In this paper we show that along with the other image captioning models compression techniques text augmentations could help to improve quality leaving models size small enough to fit mobile devices.
Cluster Computing

A computer cluster is a collection of loosely linked computers that operate together so closely that they appear to be one computer in many ways. Clusters are frequently connected via fast local area networks. Clusters are frequently used to increase the performance and/or dependability of a single computer while also being significantly less expensive than single machines of equivalent speed and reliability. Cluster computing arose from the convergence of numerous developments, including the affordable availability of high- performance microprocessors and high-speed networks, as well as the creation of standard software tools for high-performance distributed calculate. Clusters have grown to accommodate a wide range of applications, including ecommerce and high-performance database applications. The fundamental goal of a cluster computer is to perform a task in a shorter amount of time by employing processing node groups. This is accomplished by moving loads from busy to idle nodes. Transferring excess loads from active nodes to idle nodes is one approach for achieving this goal.
Development of operational and demand-oriented firefighting equipment

Due to the increasing demands and the constantly changing tasks, it is significant that the rescue tools of rescue and emergency organizations are optimally adapted to the situation. At the same time, rescue tools should be usable by as many members of the organizations as possible. That means that people should not be excluded from using the tool, for example, because they are too heavy or complicated to handle. At the same time, rescue techniques must be adapted to new challenges such as new materials or power sources in vehicles. The research aims to produce a guide to counteract this problem.
Hierarchical Multifocus Navigation in Text Annotation Data

We present a new hierarchical multifocus representation- and interaction-technique for the analysis of text annotations. The comparative analysis of annotation data on multiple, distant passages (focus regions) of a long source text raises several scalability problems. In particular, one soon can be confronted with many nested foci on text ranges with sizes of different orders of magnitude. Our solution suggests to delegate the detailed data representation to other views and to concentrate in the presented overview on the organization of the focus regions. The approach consists of three parts: a collection of strips where the annotations are represented in a condensed form and where sibling- and child-foci are manipulated, a map of the resulting tree of foci for direct access, and a work bench that allows to compare deliberate nodes of the tree on a flat layer. We discuss our approach by comparing it with other state-of-the-art multifocus techniques and describe two use cases that deal with relational annotation and with the analysis of annotations on hierarchical text structures respectively.
Generative Rendering Network based on U-shape Discriminator

Abstract This paper introduces a generative rendering network(GRN) based on a U-shape discriminator for novel view image synthesis. Recently, some generative adversarial networks start to explore 3D space and synthesize new images by rendering methods. Compared with 2D-only networks, they have achieved impressive results in synthesizing novel view images. However, they still have a common shortcoming: the discriminator cannot provide more detailed information for training a strong generator. For this problem, we design an U-shape based discriminator to extract both global information and pixel-wise information. This innovative discriminator will feedback more instructive clues to train a better generator. Benefit from this strategy, our synthesis results on ShapeNet v2 “cars” and “chairs” and celebA have achieved better performance than comparative methods.
Voice recognition bot for internet banking

Chat bots are systems or software that behave like a help desk in order to face problems and deal with such a problems that people find themselves in. This bot helps the users to stay at one place and gives the solution for any of the queries that has raised by users. However, this bank chat bot will assist the user rather than a person in resolving his or her problem, allowing us to benefit while remaining at home. People who are unable to leave due to the COVID-19 can use this initiative to resolve issues with their banks. This chat bot system can help the users in knowing about the bank queries. So it will be a solution and useful system in our day to day life.
Sentiment Analysis on Book Reviews Using Machine Learning Techniques

Among the most essential NLP applications is sentiment analysis, often known as opinion mining. In recent years, sentiment analysis has received a lot of attention. It is concerned with text classification in order to establish the intent of the text's final user. End-user feedback becomes the most important factor in determining the quality of a book's content. Online book reviews are regarded as one of the most important sources of customer feedback. In the current environment, people may discover on books and make selections based on online review sites. Hence, extracting the precise book reviews from the dataset wherein we have preprocessed the dataset using sentimental analysis, counter vectorizer are used for feature extraction in bag of words and the classification are done through SVM, KNN, RF, DT, NB to predict the review status whether it is positive, negative or neutral. The model is tuned with grid search and the confusion matrix are plot for the above mentioned algorithms. The final aim of the work is to provide users with their wanted books. Thus, we have tested our categorization approach through amazon reviews.
On the Use of Mouse Actions at the Character Level

Neural Machine Translation (NMT) has improved performance in several tasks up to human parity. However, many companies still use Computer-Assisted Translation (CAT) tools to achieve perfect translation, as well as other tools. Among these tools, we find Interactive-Predictive Neural Machine Translation (IPNMT) systems, whose main feature is facilitating machine–human interactions. In the most conventional systems, the human user fixes a translation error by typing the correct word, sending this feedback to the machine which generates a new translation that satisfies it. In this article, we remove the necessity of typing to correct translations by using the bandit feedback obtained from the cursor position when the user performs a Mouse Action (MA). Our system generates a new translation that fixes the error using only the error position. The user can perform multiple MAs at the same position if the error is not fixed, each of which increases the correction probability. One of the main objectives in the IPNMT field is reducing the required human effort, in order to optimize the translation time. With the proposed technique, an 84% reduction in the number of keystrokes performed can be achieved, while still generating perfect translations. For this reason, we recommend the use of this technique in IPNMT systems.
SPECIFICS OF DISTANCE LEARNING IN THE SYSTEM OF POSTGRADUATE PEDAGOGICAL EDUCATION

The purpose of the study is to reveal the features of the application of innovative pedagogical technologies of distance learning of teachers in the system of postgraduate education in crisis conditions. The applied research methods, such as theoretical analysis and systematization of scientific sources, allowed to identify the state of the problem of application of innovative technologies of distance learning in the educational process carried out in crisis conditions; pedagogical observation and diagnostic research provided an opportunity to obtain research results on the active introduction of innovative pedagogical technologies for distance learning of teachers in the system of postgraduate education, including gaining practical experience in integrating information and communication technologies with interactive technologies and technologies of critical and creative thinking; systematization, generalization and structuring in order to record and present the results of the study. The article outlines the problem of finding a modern format of distance learning for teachers in the system of postgraduate education, which will contribute to the continuous development of their professional competence and soft skills, which are vital for their life and professional activities in today's crisis. The practical experience of using innovative pedagogical technologies, methods, techniques and forms of organization of the educational process in a distance format, which contribute to the active continuous professional development of teachers in the system of postgraduate education, is highlighted. A qualitative analysis of online tools use for distance learning of teachers in the system of postgraduate education.