Reviewerly/exHarmony huggingface dataset:
Learned Generative Misspecified Lower Bound

The Misspecified Cramér-Rao lower bound (MCRB) provides a lower bound on the performance of any unbiased estimator of parameter vector θ under model misspecification. An approximation of the MCRB can be numerically evaluated using a set of i.i.d samples of the true distribution at θ. However, obtaining a good approximation for multiple values of θ requires collocating an unrealistically large number of samples. In this paper, we present a method for approximating the MCRB using a Generative Model, referred to as a Generative Misspecified Lower Bound (GMLB), in which we train a generative model on data from the true measurement distribution. Then, the generative model can generate as many samples as required for any θ, and therefore the GMLB can use a limited set of training data to achieve an excellent approximation of the MCRB for any parameter. We demonstrate the GMLB on two examples: a misspecified Linear Gaussian model; and a Non-Linear Truncated Gaussian model. In both cases, we empirically show the benefits of the GMLB in accuracy and sample complexity. In addition, we show the ability of the GMLB to approximate the MCRB on unseen parameters.
Weighted Sampling for Masked Language Modeling

Masked Language Modeling (MLM) is widely used to pretrain language models. The standard random masking strategy in MLM causes the pre-trained language models (PLMs) to be biased towards high-frequency tokens. Representation learning of rare tokens is poor and PLMs have limited performance on downstream tasks. To alleviate this frequency bias issue, we propose two simple and effective Weighted Sampling strategies for masking tokens based on token frequency and training loss. We apply these two strategies to BERT and obtain Weighted-Sampled BERT (WSBERT). Experiments on the Semantic Textual Similarity benchmark (STS) show that WSBERT significantly improves sentence embeddings over BERT. Combining WSBERT with calibration methods and prompt learning further improves sentence embeddings. We also investigate fine-tuning WSBERT on the GLUE benchmark and show that Weighted Sampling also improves the transfer learning capability of the backbone PLM. We further analyze and provide insights into how WSBERT improves token embeddings.
Structure-Preserving and Redundancy-Free Features Refinement for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to recognize images from seen and unseen classes. Most models achieve competitive performance but still suffer from two problems: (1) Topological structure neglection; (2) Redundant information interference. In this paper, we propose a Structure-preserving and Redundancy-free Features Refinement model (referred to as SP-RFFR) to address these problems correspondingly in two modules: (1) Structure-preserving, to explicitly incorporate the topological structure into the learning of the latent space and the generator; (2) Redundancy-free features refinement, to remove the redundant information from the visual features and learn class- and semantically-relevant representations. To the best of our knowledge, this is the first work that incorporates topological structure preserving and redundancy-free features refinement into a unified framework for GZSL. Extensive experiments show that SP-RFFR outperforms the state-of-the-art methods on four benchmarks.
Image Tamper Detection and Correction Based on Mean Pixel Value and Logistic Map

This proposed image watermarking scheme works on 4×4 pixel blocks. The least significant bits (LSBs) of all the 16 pixels are set to zeroes. After setting all the LSBs to zeroes, the mean pixel value of the block is calculated. It is expressed in 8 bits. A logistic map (LM) is used to create 8-bit key. The 8-bit mean value and 8-bit key are input into exclusive or (XOR) function to generate watermark bits (WBs). The block is divided into 2 sub-blocks. The first 8 pixels in upper 2 rows of the block are known as upper half block (UHB) and the next 8 pixels in lower 2 rows of the block are known as lower half block (LHB). The 8 bits of block average are concealed in LSBs of UHB pixels, and the 8 WBs are concealed in LSBs of LHB pixels. At the receiver end the 8 bits from the pixels of UHB are extracted and XORed with the 8-bit key generated from LM. These are known as test bits. The LSBs of pixels of LHB are extracted, these are the extracted WBs. If the extracted WBs are same as the test bits, the block is earmarked as not tampered. The quality metrics values are satisfactory and better than the existing techniques.
OMR Reader Info Scanner

In order to put OMR (Optical Mark Recognition) into practice using a common scanner, we have created a special application for this project. Our project's aim is to provide an OMR-based grading system to academic institutions. Each student will be given a sheet with blank circles for grading. To automatically grade student response sheets on a mobile device, we use an image processing technique. The program will automatically tally student grades and save the results to a database. In addition, with the push of a button, a list of defaulters will be generated.The present project was inspired by the need to automate multiple-choice evaluations in school contests at a low cost. This was done by combining optical mark recognition (OMR) technology with artificial vision algorithms for multiple choice questions. It was able to assess the amount of precision using common scanning technologies based on four critical variables, including economic images, the identification and repair of images with changes, student coding, and time reading. The recognition success has been calculated as 97.6%. OMR is useful for applications in which large numbers of hand-filled forms need to be processed quickly and with great accuracy, such as surveys, questionnaires. OMR allows for processing of hundreds or thousands of physical documents per hour. The existing system requires special hardware which turns out to be very costly for any organization. So using such a system may be cost inefficient or not feasible by organizations. The error rate for OMR technology is less than 1.5%However, the OMR technique does have its limitations, such as its inability to deal with inaccurate printing or thin materials. In this research, a fresh strategy for improving OMR is explored. After scanning papers that have been structured and marked according to a template, software may examine the resultant image to identify which answers were circled. We also detail how to fix mistakes introduced by modifying the scanned copy using techniques like resizing, rotating, and transposing.
Dictionary Learning on Graph Data with Weisfieler-Lehman Sub-Tree Kernel and Ksvd

Graph representation has gained wide popularity as a data representation method in many applications. Graph embedding methods convert graphs to a vector representation and are an important part of a data processing pipeline. In this paper, we utilize sparse dictionary learning techniques as a graph embedding solution. Sparse representation has notable applications in signal image processing. Inspired by the Graph2Vec algorithm, we aim to modify the Doc2Vec model training portion of the Graph2Vec by incorporating unsupervised dictionary learning. We investigate the viability of using the sparse dictionary learning technique KSVD for graph data. We train the dictionary on Weisfeiler-Lehman graph sub-tree kernel features. Furthermore, we use graph-based labeled data sets to compare classification results with several existing graph embedding methods. Findings show that using the learned sparse coefficients as features for a supervised machine learning algorithm provides on-par classification results when compared to other graph embedding methods.
Footprint Counting using Face Recognition

Face detection is one of the most advanced technologies in the current era. Based on face feature detection and recognition, this research provided a system for counting individuals. The live feed is recorded in video format. Face detection, face recognition, feature extraction, and counting are the four parts of this system. The viola Jones approach for facial recognition was used to recognise faces in images. This approach is quicker than others and produces more accurate results when the facial traits of a person are examined using the Adaboost algorithm. It was used to distinguish between the face and non-facial areas. If a face is recognised, it is known. If it is unknown, this individual will be counted. The results of the experiments reveal that the system successfully counts people with great efficiency and accuracy.
E-Commerce Product Review Classification based on Supervised Machine Learning Techniques

To become a successful organization it is the most important aspect to consider is the customer satisfaction. A business can grow only when the customers enjoy their product/service and how much they recommend it. For the same, companies collect reviews and ratings from its customers to understand their perspective better. Amazon is also such a giant, which values its customers’ opinions. It is very difficult or nearly impossible to have a detailed manual scan of each and every review for each and every product available on the website. This is where NLP (Natural language processing) comes into picture. It processes human language from text to arrays of numbers in order to find sense in the text so that the machine learning algorithms can process them further and classify the texts. This project focuses on processing the amazon reviews of various categories and products and then classify them into 5 ratings, from 1 to 5, worst to best respectively. The supervised machine learning algorithms used to process these reviews are Support Vector machines (SVM), Naïve Bayes classifier and Decision Tree classifier. The analysis of comparison of these algorithms has also been discussed using accuracy score bar charts.
A Dynamic Cross-Scale Transformer with Dual-Compound Representation for 3D Medical Image Segmentation

Transformer models exploit multi-head self-attention to capture long-range information. Further, window-based self-attention solves the problem of quadratic computational complexity and provides a new solution for dense prediction of 3D images. However, Transformers miss structural information due to the naive tokenization scheme. Furthermore, single-scale attention fails to achieve a balance between feature representation and semantic information. Aiming at the above problems, we propose a window-based dynamic crossscale cross-attention transformer (DCS-Former) for precise representation of the diversity features. DCS-former first constructs dual-compound feature representations through Antehoc Structure-aware Module and Post-hoc Class-aware Module. Then, the bidirectional attention structure is designed to interactively fuse structural features with class representations. The experimental results show that our method outperforms various competing segmentation models on three different public datasets.
Improving Fairness and Robustness in End-to-End Speech Recognition Through Unsupervised Clustering

The challenge of fairness arises when Automatic Speech Recognition (ASR) systems do not perform equally well for all sub-groups of the population. In the past few years there have been many improvements in overall speech recognition quality, but without any particular focus on advancing Equality and Equity for all user groups for whom systems do not perform well. ASR fairness is therefore also a robustness issue. Meanwhile, data privacy also takes priority in production systems. In this paper, we present a privacy preserving approach to improve fairness and robustness of end-to-end ASR without using metadata, zip codes, or even speaker or utterance embeddings directly in training. We extract utterance level embeddings using a speaker ID model trained on a public dataset, which we then use in an unsupervised fashion to create acoustic clusters. We use cluster IDs instead of speaker utterance embeddings as extra features during model training, which shows improvements for all demographic groups and in particular for different accents.
Learning Supervised Covariation Projection Through General Covariance

Canonical correlation analysis (CCA) is a classical yet powerful tool for learning two-view feature representation in various fields. But, most CCA approaches are based on the conventional covariance measure, which makes them difficult to uncover the complicatedly nonlinear relationship between distinct features. In this paper, we address the preceding problem and propose two novel CCA approaches in a supervised manner by using a general covariance metric. The proposed approaches not only consider the label information of training data, but also the nonlinear relationship between different features rather than samples, which leads to greater flexibility in many practical applications. A series of experimental results on five benchmark datasets demonstrate the effectiveness of our proposed methods in terms of classification accuracy.
The Role of Initial Entanglement in Adaptive Gibbs State Preparation on Quantum Computers

Sampling from distributions is an important capability for a range of tasks in science and engineering, including in machine learning applications. Quantum computers hold promise for the capability of sampling from distributions that are hard to sample from with usual (classical) computers. We have recently introduced an adaptive quantum variational algorithm for Gibbs state preparation, a state that can be sampled to yield a thermal (or Gibbs) distribution. This has applications in statistical physics, stochastic processes, and optimization. Here we explore the role of the initial quantum correlations (i.e., entanglement) built into the ‘data’ quantum bits (those that will be sampled) and the ancilla quantum bits (those that assist in preparing the Gibbs state) in the run-time of the adaptive state-preparation algorithm. Based on our simulations, we conclude that certain types of entangled states perform better for Ising Hamiltonians, while others are more appropriate for XY Hamiltonians. We also show that both perform well if multiple operators are added at each iteration through the ‘TETRIS-ADAPT’ technique.
Frame-Wise and Overlap-Robust Speaker Embeddings for Meeting Diarization

Using a Teacher-Student training approach we developed a speaker embedding extraction system that outputs embeddings at frame rate. Given this high temporal resolution and the fact that the student produces sensible speaker embeddings even for segments with speech overlap, the frame-wise embeddings serve as an appropriate representation of the input speech signal for an end-to-end neural meeting diarization (EEND) system. We show in experiments that this representation helps mitigate a well-known problem of EEND systems: when increasing the number of speakers the diarization performance drop is significantly reduced. We also introduce block-wise processing to be able to diarize arbitrarily long meetings.
A Closer Look At Scoring Functions And Generalization Prediction

Generalization error predictors (GEPs) aim to predict model performance on unseen distributions by deriving dataset-level error estimates from sample-level scores. However, GEPs often utilize disparate mechanisms (e.g., regressors, thresholding functions, calibration datasets, etc), to derive such error estimates, which can obfuscate the benefits of a particular scoring function. Therefore, in this work, we rigorously study the effectiveness of popular scoring functions (confidence, local manifold smoothness, model agreement), independent of mechanism choice. We find, absent complex mechanisms, that state-of-the-art confidence- and smoothness- based scores fail to outperform simple model-agreement scores when estimating error under distribution shifts and corruptions. Furthermore, on realistic settings where the training data has been compromised (e.g., label noise, measurement noise, under-sampling), we find that model-agreement scores continue to perform well and that ensemble diversity is important for improving its performance. Finally, to better understand the limitations of scoring functions, we demonstrate that simplicity bias, or the propensity of deep neural networks to rely upon simple but brittle features, can adversely affect GEP performance. Overall, our work carefully studies the effectiveness of popular scoring functions in realistic settings and helps to better understand their limitations.
Multiscale Facial Expression Based Face Recognition using Normalized Segment Classification Detection in Face Images

Face recognition has always been one of the most interesting and important study areas. This is based on Convolution neural network (CNN) on automated recognition and surveillance systems, interest in facial recognition and face vision systems, and in the drawing up of human-machine interfaces, facial recognition, etc. It contracts the edge detection of each layer, and there is a need for the extraction of traditional features of the facial recognition system. Information for the node of an image obtained by edge detection of an existing CNN and Harris edge detection method is a very complex process for finds accurate detection. Harris’s edge detection technology does not find accurate edges for the faces. To overcome the face Expression detection using the Multiscale Facial Action Regression System (MFARS) algorithm. In these algorithms, segmentation, pixel fusion, and absolute link point’s analysis are performed. Image feature segmentation and analyzing the image pixel values and then extracting the face features functions are also selected and compared with images in the training data set. Facial recognition system enables the retrieval of information, which comes from which can assist in the recognition of images are improving the sharpness, color range, pixel values, and image contrast level. The process has several stages, including image completion, feature extraction, and finally expression classification. Our simulated result shows the improved accuracy during face Expression detection and fast process to collect information about the face from the database.
Some 3-designs and shortened codes from binary cyclic codes with three zeros

Linear codes and t-designs are interactive with each other. It is well known that some t-designs have been constructed by using certain linear codes in recent years. However, only a small number of infinite families of the extended codes of linear codes holding an infinite family of t-designs with t≥3 are reported in the literature. In this paper, we study the extended codes of the augmented codes of a class of binary cyclic codes with three zeros and their dual codes, and show that those codes hold 3-designs. Furthermore, we obtain some shortened codes from the studied cyclic codes and explicitly determine their parameters. Some of those shortened codes are optimal or almost optimal.
Identification of Breast Cancer using Jelly fish Based Deep Wavelet Autoencoder

Mammography, an imaging technology used to detect breast cancer, has recently received more interest from scientists. Screening mammography is the best way to detect breast cancer and other abnormalities. Breast tumours often have two distinct characteristics: mass lesions and microcalcification. A gathering of appropriate picture pre-processing, proceduresare essential for accurate cancer detection and classification in mammograms. Image processing is widely employed in the medical field to improve the diagnostic and treatment processes. When it comes to classification and segmentation, deep neural networks (DNN) have excelled so far. With this in mind, the authors of this paper suggest a deep wavelet autoencoder (DWA) method for image compression by fusing the autoencoder's basic feature reduction function with the picture decomposition capabilities of the wavelet transform. The best attributes for identifying breast cancer are chosen in this study by using the Jelly Fish Search Algorithm (JFSA). When used together, they have a dramatic impact on reducing the size of the feature set required to persist through a subsequent classification job using DNN. The suggested DWA-DNN image classifier was evaluated on two public datasets of breast cancer pictures. The DWA-DNN classifier's performance criteria was compared to that of other existing classifiers, and it was found that the suggested technique excels in comparison across a range of measures.
Analyzation of Sentiments of Product Reviews using Natural Language Processing

In order to analyze opinion mining from e-commerce websites, this paper discusses sentence-level sentiment analysis. To save time, many consumers use e-commerce websites like Amazon and eBay to purchase millions of things online. The product may already have a lot of reviews or comments from other customers who have already purchased it. In this paper, a Bayesian-network based framework is presented that makes use of product reviews imported from e-commerce websites for a specific product. The shipped product reviews are then automatically analyzed to produce untrained rules. After that, a Bayesian Network is constructed utilizing a set of untrained rules, evidence set and random variables. The newly generated reviews from e-commerce websites can subsequently be evaluated by the trained network. Additionally, the built-in model can be incrementally retrained to increase its resilience. Last but not the least, the framework is easily scalable to opinion mining from social networking sites on a variety of topics, and the model standards that are produced by this process are developed instantly and automatically.
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification

Data augmentation is vital to the generalization ability and robustness of deep neural networks (DNNs) models. Existing augmentation methods for speaker verification manipulate the raw signal, which are time-consuming and the augmented samples lack diversity. In this paper, we present a novel difficulty-aware semantic augmentation (DASA) approach for speaker verification, which can generate diversified training samples in speaker embedding space with negligible extra computing cost. Firstly, we augment training samples by perturbing speaker embeddings along semantic directions, which are obtained from speaker-wise covariance matrices. Secondly, accurate covariance matrices are estimated from robust speaker embeddings during training, so we introduce difficulty-aware additive margin softmax (DAAM-Softmax) to obtain optimal speaker embeddings. Finally, we assume the number of augmented samples goes to infinity and derive a closed-form upper bound of the expected loss with DASA, which achieves compatibility and efficiency. Extensive experiments demonstrate the proposed approach can achieve a remarkable performance improvement. The best result achieves a 14.6% relative reduction in EER metric on CN-Celeb evaluation set.
Few but Informative Local Hash Code Matching for Image Retrieval

Content-based image retrieval (CBIR) aims to search for the most similar images from an extensive database to a given query content. Existing CBIR works either represent each image with a compact global feature vector or extract a large number of highly compressed low-dimensional local features, where each contains limited information. In this research study, we propose an expressive local feature extraction pipeline and a many-to-many local feature matching method for large-scale CBIR. Unlike existing local feature methods, which tend to extract large amounts of low-dimensional local features from each image, the proposed method models characteristic feature representations for each image, aiming to employ fewer but more expressive local features. For further improving the results, an end-to-end trainable hash encoding layer is used for extracting compact but informative codes from images. The proposed many-to-many local feature matching is then directly performed on the hash feature vectors from input images, leading to new state-of-the-art performance on several benchmark datasets.