Reviewerly/exHarmony huggingface dataset:
Recognition Of Handwritten English Character Using Convolutional Neural Network

In the domain of computer vision and image processing, one of the most active and difficult study fields is handwritten character recognition. It may be used as a reading tool for bank checks, for identifying characters on forms, and for a slew of other purposes. The optical character recognition of the papers is similar to documents produced by hand by a human. This OCR is put to use to improve the simplification of the process of character translation, which may be obtained from a broad range of file types, such as image and word document files. Researchers have made tremendous progress in HCR by making use of vast amounts of raw data and new breakthroughs in Deep Learning and Machine Learning algorithms. The fundamental purpose of this research paper is to give a solution for several techniques of handwriting recognition. These methods include the usage of touch input through a mobile screen as well as the use of an image file. CNN is used to identify characters in a test dataset in this work. Work on CNNs' capacity to detect characters from a picture dataset and their accuracy of recognition will be examined. Characters are recognized by CNN by comparing and contrasting their shapes and distinguishing characteristics. The dataset A_Z Handwritten was used to test our CNN implementation's handwriting accuracy and model gives the 100% result to recognize the character.
An efficient algebraic representation for graph states for measurement-based quantum computing

Graph states are the main computational building blocks of measurement-based computation and a useful tool for error correction in the gate model architecture. The graph states form a class of quantum states which are eigenvectors for the abelian group of stabilizer operators. They own topological properties, arising from their graph structure, including the presence of highly connected nodes, called hubs. Starting from hub nodes, we demonstrate how to efficiently express a graph state through the generators of the stabilizer group. We provide examples by expressing the ring and the star topology, for which the number of stabilizers reduces from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lceil\frac{n}{2}\rceil$</tex> , and from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> to 1, respectively. We demonstrate that the graph states can be generated by a subgroup of the stabilizer group. Therefore, we provide an algebraic framework to manipulate the graph states with a reduced number of stabilizers.
Implementation Security Digital Signature Using Rivest Shamir Adleman (RSA) Algorithm As A Letter Validation And Distribution Validation System

A digital signature is a type of asymmetric cryptography that is used to ensure that the recipient receives the actual received message from the intended sender. Problems that often arise conventionally when requiring letter approval from the authorized official, and the letter concerned is very important and urgent, often the process of giving the signature is hampered because the official concerned is not in place. With these obstacles, the letter that should be distributed immediately becomes hampered and takes a long time in terms of signing the letter. The purpose of this study is to overcome eavesdropping and data exchange in sending data using Digital Signature as authentication of data authenticity and minimizing fake signatures on letters that are not made and authorized by relevant officials based on digital signatures stored in the database. This research implements the Rivest Shamir Adleman method. (RSA) as outlined in an application to provide authorization or online signature with Digital Signature. The results of the study The application of the Rivest Shamir Adleman (RSA) algorithm can run on applications with the Digital Signature method based on ISO 9126 testing by expert examiners, and the questionnaire distributed to users and application operators obtained good results from an average value of 79.81 based on the scale table ISO 9126 conversion, the next recommendation for encryption does not use MD5 but uses Bcrypt secure database to make it stronger.
Sentimental analysis of amazon customers based on their review comments

Amazon being one of the leading technology giants in the public cloud market is very popular for the services such as amazon shopping and prime video for online shopping and online video streaming. Now a day's customers buy products based on the product reviews given by already purchased customers. If the reviews are good, then that product rating is high. Sentimental analysis is a fastest growing research area where text mining plays a major role in analysing the customer reviews. There are many methods currently existing on sentimental analysis of amazon customers based on their review comments such as using machine learning algorithms and deep learning algorithms. This article proposes a hybrid approach by combining technologies such as Natural language processing, Machine Learning and Deep Learning methods to analyse the customer reviews and ratings. Based on the negative, positive comments and ratings the sentiment of customer will be analysed. The cleaned dataset is pre-processed and sentiment analysis is done using Machine Learning and NLP. The work is proposed to perform sentiment analysis on the data extracted from the pre-processed data of the customer review and ratings and best classifier algorithms such as SVM., NB with natural processing language. The positive and negative classification in done depending on the customer reviews and ratings by the ML techniques. The novelty of the work is that combination of techniques such as NLP., ML and DL are applied.
Quantum Natural Language Processing: A New and Promising Way to Solve NLP Problems

Natural Language Processing (NLP) has known important interest and growth in recent years as may witness the increasing number of publications in different NLP tasks over the world. The primary focus of recent research has been to develop algorithms that process natural language in quantum computers, hence the emergence of new sub-domain called QNLP were proposed. Hence the objective of this paper is to provide to NLP researchers a new vision and way to deal with the NLP problems basing on Quantum computing techniques. The present paper aims to provide a list of existing alternatives and classify them by; implementation on classical or quantum hardware, theoretical or experimental work and representation type of sentence. Our study focuses on the Distributional Compositional Categorical model (DisCoCat), from its mathematical and theoretical demonstration into its experimental results.
Digital Modulation Classification Based on Automatic K-Nearest Neighbors Classifier

This paper presents an algorithm for digital modulation classification based on an automatic k nearest neighbors’ classifier. The proposed AKNN algorithm attempts to improve the classical kNN classifier accuracy by automatically select the k neighbors for each instance using a model associating the average distances values with the appropriate number of neighbors. The classification procedure was performed over 5 classes of modulation: QPSK, 16PSK, QAM, 16QAM and 4FSK, using cumulants features. In addition to the avoidance of the manual selection of K neighbors, the proposed algorithm outperforms the traditional kNN in terms of accuracy rate mainly in the case of low SNR.
Automated Gesture Recognition and Speech Conversion Tool for Speech Impaired

By translating hand gestures into a series of words or speech, sign language recognition (SLR) aims to improve communication between deaf and dumb people and the general public. Despite the fact that this activity has a significant societal impact, it is nevertheless challenging due to the complexity and wide variation of hand behavior. Existing SLR techniques use hand-composed pieces to characterize sign language movement and develop division models based on those aspects. It's difficult to create reliable features that respond to a wide range of hand movements. The goal of this project is to develop a sign language recognition project using CNN and predict the output in the form of text and audio.
Discovering Closed-Loop Failures of Vision-Based Controllers via Reachability Analysis

Machine learning driven image-based controllers allow robotic systems to take intelligent actions based on the visual feedback from their environment. Understanding when these controllers might lead to system safety violations is important for their integration in safety-critical applications and engineering corrective safety measures for the system. Existing methods leverage simulation-based testing (or falsification) to find the failures of vision-based controllers, i.e., the visual inputs that lead to closed-loop safety violations. However, these techniques do not scale well to the scenarios involving high-dimensional and complex visual inputs, such as RGB images. In this work, we cast the problem of finding closed-loop vision failures as a Hamilton-Jacobi (HJ) reachability problem. Our approach blends simulation-based analysis with HJ reachability methods to compute an approximation of the backward reachable tube (BRT) of the system, i.e., the set of unsafe states for the system under vision-based controllers. Utilizing the BRT, we can tractably and systematically find the system states and corresponding visual inputs that lead to closed-loop failures. These visual inputs can be subsequently analyzed to find the input characteristics that might have caused the failure. Besides its scalability to high-dimensional visual inputs, an explicit computation of BRT allows the proposed approach to capture non-trivial system failures that are difficult to expose via random simulations. We demonstrate our framework on two case studies involving an RGB image-based neural network controller for (a) autonomous indoor navigation, and (b) autonomous aircraft taxiing.
HAW: Hardware-Aware Point Selection for Efficient Winograd Convolution

Winograd's minimal filtering algorithm effectively reduces the multiplication arithmetic complexity of Convolutional Neural Networks. However, Winograd convolutions in current implementations are limited to small feature tiles for two reasons:the numerical error and the overhead of the transformations. The performance of Winograd convolutions is determined by the points used to construct transformation matrices, which raises a great challenge to find the optimal points: it requires exploring the vast design space trading off between numerical accuracy and hardware resource consumption. In this letter, we introduce the Hardware-Aware point selection framework for efficient Winograd convolution, which leverages reinforcement learning to determine the point selection policy. We design three reward functions to optimize numerical accuracy and circuit area. Experiments demonstrate thatWinograd convolutions using our policies outperform state-of-the-art methods in circuit area and accuracy.
Beyond Frontal Face Recognition

Face recognition is one of the most researched subjects in computer vision. The attention it receives is due to the complexity of the problem. Face recognition models have to deal with a wide variety of intraclass variations such as pose variations, facial expressions, the effect of aging, and natural occlusion due to different illumination. This challenge is often referred to as pose-illumination-expression. Our brain performs face recognition efficiently because we process it holistically. Since achieving human-level accuracy in face recognition is the ultimate goal, we should ascertain whether a computational model mimicking this approach would better tackle this problem. In this research, we developed a computational learning model that closely mimics the way the human visual cortex performs face recognition. It decomposes the recognition task into two specialized sub-tasks. A generator performs the holistic step, followed by a classifier for the recognition step, together referred to as the "holistic model." To deal with the pose variations problem, we introduced the use of calculated distance features known as configural information, which correlate the frontal face with profile face. We compared the holistic model against two baseline models and the current state-of-the-art (or classical models). The experimental results show the holistic model outperforming the current state-of-the-art for the Multi-PIE dataset, by 2.14% and performed as expected for the Labeled Face in the Wild dataset. The ability of the holistic model to recognize a face in any orientation with high accuracy will have a tremendous impact on biometric authentication with liveness detection.
Cross-Modal Text Steganography Against Synonym Substitution-Based Text Attack

Steganography has received massive attention from the information-hiding community due to its excellent security for covert communication systems. Existing work focuses on improving security on single-modal media while cross-modal media is less explored. However, cross-modal interaction has become a prevalent social manner on current social networks, which arises potential behavioral security issues of single-modal steganography. In this letter, we propose a novel text steganography to explore the practicability of cross-modal steganography. The proposed scheme is composed with image encoder, message encoder, language model, and message extractor networks, where the generated stego texts are semantically consistent with the input reference image. In addition, current generative text steganography schemes are vulnerable to text attack based on synonym substitution since these heuristic algorithms embed information by constructing a mapping between secret messages and candidate tokens. Thus, we design a text attack layer based on synonym substitution to further improve the robustness of generated stego text. Experiments illustrate the superior performance of the proposed cross-modal steganography scheme in terms of security and robustness.
Learning for Feature Matching via Graph Context Attention

Establishing reliable correspondences via a deep learning network is an important task in remote sensing, photogrammetry and other computer vision fields. It usually requires mining the relationship among correspondences to aggregate both local and global context. However, current methods are insufficient to effectively acquire context information with high reliability. In this paper, we propose a graph context attention based network (called GCA-Net) to capture and leverage abundant contextual information for feature matching. Specifically, we design a graph context attention block which generates multi-path graph contexts and softly fuses them to combine respective advantages. In addition, for building the graph context containing stronger representation ability and outlier resistance ability, we further design a local-global channel mining block to gather context information by focusing on the significant part as well as to mine dependencies among channels of correspondences in both local and global aspects. The proposed GCA-Net is able to effectively infer the probability of correspondences being inliers or outliers and estimate the essential matrix meanwhile. Extensive experimental results for outlier removal and relative pose estimation demonstrate that GCA-Net outperforms the state-of-art methods on both outdoor and indoor datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> .,YFCC100M and SUN3D). In addition, experiments extended to remote sensing and point cloud scenes also demonstrate the powerful generalization capability of our network.
Spatial Hard Attention Modeling via Deep Reinforcement Learning for Skeleton-Based Human Activity Recognition

Deep learning-based algorithms have been very successful in skeleton-based human activity recognition. Skeleton data contains 2-D or 3-D coordinates of human body joints. The main focus of most of the existing skeleton-based activity recognition methods is on designing new deep architectures to learn discriminative features, where all body joints are considered equally important in recognition. However, the importance of joints varies as an activity proceeds within a video and across different activities. In this work, we hypothesize that selecting relevant joints, prior to recognition, can enhance performance of the existing deep learning-based recognition models. We propose a spatial hard attention finding method that aims to remove the uninformative and/or misleading joints at each frame. We formulate the joint selection problem as a Markov decision process and employ deep reinforcement learning to train the proposed spatial-attention-aware agent. No extra labels are needed for the agent’s training. The agent takes a sequence of features extracted from skeleton video as input and outputs a sequence of probabilities for joints. The proposed method can be considered as a general framework that can be integrated with the existing skeleton-based activity recognition methods for performance improvement purposes. We obtain very competitive activity recognition results on three commonly used human activity recognition datasets.
Amharic Sentence-Level Word Sense Disambiguation Using Transfer Learning

Word sense disambiguation (WSD) plays an important role, in increasing the performance of NLP applications such as information extraction, information retrieval, and machine translation. The manual disambiguation process by humans is tedious, prone to errors, and expensive. Recent research in Amharic WSD used mostly handcrafted rules. Such works do not help to learn different representations of the target word from data automatically. Moreover, such a manual disambiguation approach looks at a limited length of surrounding words from the sentence. The main drawback of previous works is that the sense of the word will not be detected from the synset list unless the word is explicitly mentioned. Our study explores and designs the Amharic WSD model by employing transformer-based contextual embeddings, namely AmRoBERTa. As there is no standard sense-tagged Amharic text dataset for the Amharic WSD task, we first compiled 800 ambiguous words. Furthermore, we collect more than 33k sentences that contain those ambiguous words. The 33k sentences are used to finetune our transformer based AmRoBERTa model. We conduct two types of annotation for our WSD experiments. First, using linguistic experts, we annotate 10k sentences for 7 types of word relations (synonymy, hyponymy, hypernymy, meronomy, holonomy, toponymy, and homonymy). For the WSD disambiguation experiment, we first choose 10 target words and annotate a total of 1000 sentences with their correct sense using the WebAnno annotation tool. For the classification task, the CNN, Bi-LSTM, and BERT-based classification models achieve an accuracy of 90%, 88%, and 93% respectively. For the WSD task, we have employed two experiments. When we use the masking technique of the pre-trained contextual embedding to find the correct sense, it attains 70% accuracy. However, when we use the FLAIR document embedding framework to embed the target sentences and glosses separately and compute the similarities, our model was able to achieve 71% accuracy to correctly disambiguate target words.
一种用于位移场分析的应变模式频域数字图像相关方法

Abstract A spectral image correlation method considering the effect of displacement gradients upon displacement analysis in the Fourier frequency domain is proposed. A spectral image correlation criterion is studied and established to match the target subset image with the reference subset image in the frequency domain. An iterative algorithm for calculating displacement and strain is then derived, and the Hessian matrix remains unchanged within an iterative process. During the formation of the Hessian matrix, the greyscale gradients of the reference subset image can be calculated using the fast Fourier transform with high computational efficiency and accuracy. An accurate Fourier transform resampling technique, instead of the traditional interpolation method, is used to reconstruct or up-date the target image at a subpixel position in the deformed subset image. To verify the validity and accuracy of the proposed method, a series of images (2D-Challenge 1.0, sample 6) provided by the International DIC Challenge Committee (IDCC) was used for displacement analysis. The analyzed error results show that the proposed method is better than the published literature in accuracy. The second simulation experimental result with a strain of 50% is performed and the corresponding displacement accuracy is about 0.0032 pixel under the condition of 41 × 41 subset. Finally, a 14.85% uniaxial tensile test of a silicone rubber specimen is performed, and the analyzed displacement and strain distribution are given.
Emerging field or passing fashion? A case study of Agile-Stage-Gate model in innovation processes

Purpose Agile methods are increasingly being applied in the contexts of innovation beyond traditional information technology (IT) and physical product development projects, such as when process improvements are being implemented. Nevertheless, this phenomenon is still recent and little addressed in the literature, with few descriptions of empirical cases. This study aims to address this gap. Design/methodology/approach This multiple case study aims to present and discuss the application of Agile practices embedded in large companies’ innovation value chains, focusing on improvements of business processes. The following research question is pursued: How are large companies applying elements of Agile methods to their innovation processes when implementing incremental improvements in their operational processes? Based on the idea that the Agile-Stage-Gate model is an alternative to this challenge, this study investigates the application of this hybrid model in two large Brazilian companies by presenting their idiosyncrasies, lessons learned, adaptations, challenges and benefits. Findings Overall, it was observed that the experience with the application of the Agile-Stage-Gate model is positive for these companies, with better customer engagement, easier project control and increased productivity of the project team. Originality/value For those aiming to implement the Agile-Stage-Gate model, this paper identifies the main adaptations made in order to combine the purist approaches and critical success factors for its implementation.