Reviewerly/exHarmony huggingface dataset:
Enhancing the confidence of deep learning classifiers via interpretable saliency maps

This paper quantifies the quality of heatmap-based eXplainable AI (XAI) methods w.r.t image classification problem. Here, a heatmap is considered desirable if it improves the probability of predicting the correct classes. Different XAI heatmap-based methods are empirically shown to improve classification confidence to different extents depending on the datasets, e.g. Saliency works best on ImageNet and Deconvolution on Chest X-ray Pneumonia dataset. The novelty includes a new gap distribution that shows a stark difference between correct and wrong predictions. Finally, the generative augmentative explanation is introduced, a method to generate heatmaps capable of improving predictive confidence to a high level.
DeepSIM: a novel deep learning method for graph similarity computation

Abstract Graphs are widely used to model real-life information, where graph similarity computation is one of the most significant applications, such as inferring the properties of a compound based on similarity to a known group. Definition methods (e.g., graph edit distance and maximum common subgraph) have extremely high computational cost, and the existing efficient deep learning methods suffer from the problem of inadequate feature extraction which would have a bad effect on similarity computation. In this paper, a double-branch model called DeepSIM was raised to deeply mine graph-level and node-level features to address the above problems. On the graph-level branch, a novel embedding relational reasoning network was presented to obtain interaction between pairwise inputs. Meanwhile, a new local-to-global attention mechanism is designed to improve the capability of CNN-based node-level feature extraction module on another path. In DeepSIM, double-branch outputs will be concatenated as the final feature. The experimental results demonstrate that our methods perform well on several datasets compared to the state-of-the-art deep learning models in related fields. Graphical abstract
Handwritten Kannada Character Recognition Using Convolutional Neural Networks and Transfer Learning

Abstract Handwritten Kannada Character Recognition is special field of pattern recognition as different human being will have their own handwritten styles, machine considers even noise as a pattern. In order to classify each character, we present Convolutional Neural Networks and Transfer Learning techniques. Here we use 74k dataset and labelling individual character as one class totally we had made 25 classes. Initially we pre-process each image of character to remove noise, convert to gray-scale, applied gaussian blur and canny edge detection and cropped the images and at last resizing it. These methods help in training neural networks to recognize the characters with good accuracy. Then we apply transfer learning to same dataset, here we resize image into three-dimensional and convert images into gray-scale and using xception we train the model. Even though a few of the kannada language’s characters have similarities, this model can accurately categorise each character to the appropriate class and identify it with better accuracy than CNN.
Time Image De-Noising Method Based on Sparse Regularization

The blurring of texture edges often occurs during image data transmission and acquisition. To ensure the detailed clarity of the drag-time images, we propose a time image de-noising method based on sparse regularization. First, the image pixel sparsity index is set, and then an image de-noising model is established based on sparse regularization processing to obtain the neighborhood weights of similar image blocks. Second, a time image de-noising algorithm is designed to determine whether the coding coefficient reaches the standard value, and a new image de-noising method is obtained. Finally, the images of electronic clocks and mechanical clocks are used as two kinds of time images to compare different image de-noising methods, respectively. The results show that the sparsity regularization method has the highest peak signal-to-noise ratio among the six compared methods for different noise standard deviations and two time images. The image structure similarity is always above which shows that the proposed method is better than the other five image de-noising methods.
SOLMAT: A Neoteric Contextual Model for Odia Language Understanding

Abstract In this era of digital revolution people are expressing their opinion in natural language text. So, people of Odisha staying inside or outside Odisha are also expressing their opinion in Odia language text through different online blogs, review sites, social networking sites etc. Natural Language researchers are now working on these opinions to extract the actual sentiment of people hidden inside these opinions. More than 67% of works are based on machine learning and deep learning. Rarely 29% are only lexicon and context based because the development of lexicon is little bit difficult and time consuming. However, the lexical based gives more accurate result as compared to machine learning approach. As the opinion is much deeper than surface meaning, there is need of contextual analysis of opinion statements. In this paper we have designed a context based model named SOLMAT-A Neoteric Contextual Model for Odia Language Understanding to understand contextual meaning of the object statement for which opinion is called by identifying objects on which the opinion is based on, the action performed by or on the object, type, time of action(Present, Past, Future) and the type of statement( positive, negative or neutral) before forecasting the opinion.
Phase estimation in driven discrete-time quantum walks

Quantum walks have been shown to be important for quantum metrological tasks, in particular for the estimation of the evolution parameters of the walk. In this work, we address the enhancement of this parameter estimation using the driven discrete-time quantum walk (DDTQW), which is a variant of the discrete-time quantum walk with multiple walkers. DDTQW has two regimes based on the interference between the walker number, i.e., phase matched and phase mismatched. We derive an expression for the quantum Fisher information (QFI) in the phase-matched regime of DDTQW driven using the squeezing operator, demonstrating an exponential increase in QFI. In the phase-mismatched regime, QFI varies as ${t}^{2}$, consistent with previous studies. Our analysis shows that parameter estimation can be improved by driving the walk using squeezing operators.
Design of Path Planning Algorithm for Intelligent Robot Based on Chaos Genetic Algorithm

Path planning is one of the core problems in the field of intelligent robot research, and also an important aspect of artificial intelligence in robotics. This article combines GA (genetic algorithm) with chaotic algorithms. By studying chaos theory, the randomness of chaos is used. This article designs a path planning algorithm for intelligent robots based on chaotic genetic algorithm. By selecting a geographic information encoding method for chromosome encoding, the serial number is converted into coordinate form when evaluating the path. The coordinate method is more convenient for representing the relative positions between grids, calculating the path length, and verifying the feasibility of the path. Finally, the simulation results show that when planning according to the obtained path, because there are many path nodes, the robot’s direction and angle are adjusted more times, which strengthens the effect of positive feedback, increases the diversity of understanding, reduces the possibility of GA falling into local minima, and speeds up the convergence speed. At the same time, the algorithm is also conducive to parallel execution and application. Simulation experiments verify the effectiveness of the proposed algorithm. The chaotic genetic algorithm has achieved good results in solving the path planning problem of robots in unknown environments.
Deep learning approach for detection of Dengue fever from the microscopic images of blood smear

Abstract Dengue virus (DENV), known to cause dengue fever is a global public health concern. A safe and effective anti-viral drug or vaccine that can protect humans from dengue fever currently does not exist. Today, severe dengue has become a leading cause of serious illness in most Asian and Latin American countries. This digital pathology-related research focuses on the automatic detection of dengue by utilizing digital microscopic peripheral blood smears (PBS). This paper explored pre-trained convolution neural network (CNN) architectures for automatic dengue fever detection. Transfer learning (TL) was performed on two widely used pre-trained CNNs - SqueezeNet and GoogleNet, and employed to differentiate the dengue-infected and normal blood smears. The last few layers were replaced and retrained to customize the architectures for this task. Leishman’s stained dengue-infected and normal control 100x magnified PBS images were included in the study. The best performance was rendered by GoogleNet (Learn Rate, 0.0001; Batch Size, 8) with an Accuracy 91.30%, Sensitivity 84.62%, Specificity 100%, Precision 100%, and F1 score 91.67%. Promising results show that this approach can be an essential adjunct to other clinical methods, namely CBC test &amp; NS1 antigen capture, and can significantly support dengue diagnosis in low-resource setups.
Personalized Music Recommendation Model Based on Collaborative Filtering Algorithm and K-Means Clustering

Personalized music recommendation technology provides personalized service for users, so that users can find the products they are interested in in in the shortest time. At present, there are many recommended technologies. The traditional usage information filtering technology is mostly content-based filtering technology, in addition to collaborative filtering technology, clustering, Bayesian network, association rules and so on. Among them, CF (Collaborative Filtering) technology is the most mature and successful technology. Therefore, this paper establishes a personalized music recommendation model based on CF algorithm and K-means clustering, and verifies that the accuracy of the test results of this model is better than that of the model established in reference [5] through simulation experiments. Interested in; The average accuracy of great interest is 54.16%, 70.77% and 81.83% respectively. It is better than the test results of the model in reference [5]. It can be seen that this model is more accurate in measuring the interests of these professional users, which makes the recommendation results more in line with the needs of users.
Construction of Music Classification and Detection Model Based on Big Data Analysis and Genetic Algorithm

How to discover and recommend the most valuable songs from users’ actual needs is an urgent problem in the current music search and recommendation field. On this basis, this project plans to use a combination of big data analysis and genetic algorithm to study a new music recognition method. Firstly, feature extraction is performed on the music signal to obtain feature vectors that comprehensively reflect the sound, rhythm, emotion, and other information of the music; On this basis, the established neural network model is used to classify different tracks and obtain the segmentation probability of different tracks; Finally, a test was conducted on the tested tracks, and based on the probability of their occurrence, the test tracks were classified and the test accuracy was calculated. Experiments have shown that the algorithm proposed in this article has high recognition accuracy and can achieve unsupervised extraction of multidimensional information, laying the foundation for personalized music search.
Simulation Experiment of DPCM Compression System for High Resolution Multi-spectral Remote Sensing Images

Compared with natural images, remote sensing images are rich in texture information and have poor spatial correlation, so it is difficult to obtain high compression ratio only by removing spatial redundancy in the spectrum. In order not to lose the original information obtained by detectors, remote sensing data usually require lossless compression. In this paper, the simulation experiment of DPCM compression system for high resolution (HR) multispectral remote sensing image is carried out, and a new algorithm for HR multispectral remote sensing image compression using wavelet transformation (WT) and piecewise DPCM hybrid coding is introduced. Firstly, the algorithm uses DPCM method to remove the inter-spectral redundancy of HR multi-spectral remote sensing images, and then uses WT to encode the residual image after removing the inter-spectral redundancy. This algorithm can not only realize near lossless compression of HR multispectral remote sensing images, but also realize lossy compression with different accuracy conveniently. The simulation results show that the entropy value of the image is reduced after the compression of this algorithm. The algorithm in this paper solves the problems existing in calculating the basis vector by covariance estimation and feature decomposition, and it only needs a few bits, but the calculation is very effective. It shows that the algorithm in this paper can achieve ideal compression effect.
Investigation of Cervical Cancer Detection from Whole Slide Imaging

Abstract Early cancer detection is critical in enhancing a patient’s clinical results. Cervical cancer detection from a large number of whole slide images generated regularly in a clinical setting is a complex and time-consuming task. As a result, we require an efficient and accurate model for early cancer diagnosis, especially cervical cancer as it can be fully prevented if detected in an early stage. This study focuses on in-depth writing on current methodologies for cervical cancer segmentation and characterization from the whole cervical slide. It combines the state of their specialty’s performance measurement with the quantitative evaluation of cutting-edge techniques. Numerous publications over the last eleven years (2011-2022) clearly outline various cervical imaging methods over multiple blocks. And this review shows different types of algorithms used in each processing stage of detection. The study clearly indicates the advancements in the automation field and the necessity of the same.
Multimode non-Gaussian secure communication under mode mismatch

In this paper we analyze entanglement-based (EB) continuous-variable (CV) quantum-key distribution (QKD) with bright multimode non-Gaussian light. Our analysis is centered around the role of non-Gaussianity in mitigating the excess noise arising due to the mismatch between the signal modes and the local oscillators used for measurements. To be specific, we consider the non-Gaussian resources generated by single-photon subtraction and zero-photon catalysis applied on a two-mode squeezed vacuum (TMSV) state. We show that, at a given strength of the mode-mismatch noise, zero-photon catalysis leads to the maximum transmission distance, compared to the TMSV. However, considering the unavoidable issue of photon loss in the linear optical scheme for implementing zero-photon catalysis, our results hints at the single-photon-subtracted TMSV being the optimal choice for maximizing the transmission distance in EB CV QKD.
The Roots of the Reliability Polynomials of Circular Consecutive-k-out-of-n:F Systems

The zeros of the reliability polynomials of circular consecutive-k-out-of-n:F systems are studied. We prove that, for any fixed k≥2, the set of the roots of all the reliability polynomials (for all n≥k) is unbounded in the complex plane. In the particular case k=2, we show that all the nonzero roots are real, distinct numbers and find the closure of the set of roots. For every n≥k, the expressions of the minimum root and the maximum root are given, both for circular as well as for linear systems.
Unpacking the Art of Customer Complaint Handling in Spanish and British Telecom Emails: A Cross-Cultural Webcare Study With a Human Touch

In spite of the rise of new media in a B2C context, companies still prefer to handle complaints privately. As such, many complaints are handled via email resulting in a professional communication genre of its own. In this study we performed a cross-cultural genre analysis to understand the specific discourse structure of the moves within response mails to complaints, on the one hand, and the importance of the communicative function of Conversational Human Voice within this genre, on the other. With this aim, we collected authentic organizational email replies to complaints from telecom companies active in the UK and Spain (36 and 44 emails respectively). The results indicate that the British and Spanish data sets show a similar discourse structure in terms of move frequency. The submoves that are prototypical for all data sets are Greeting, Explanation, Conclusion, and the closing submoves Sign-off and Signature. The data sets differ mainly in their frequency for the interpersonal submoves Empathy, Gratitude, and Apology, which are more prevalent in the English corpus, and the more business-oriented moves, such as Contact reason, Marketing, and Future contact, which are mainly present in the Spanish corpus. This suggests that organizational email replies to complaints are a rather conventionalized genre, with some linguacultures putting more effort in company-customer interactions by using more interpersonal submoves. Regarding the cross-cultural analysis of the expression of Conversational Human Voice we observed an influence of the respective linguacultures in the sense that the Spanish data are less personal and less invitational than the English mails, although they present more empathetic intensifiers. Furthermore, both data sets show only a limited extent of informal language. We evaluate these findings in the light of previous work.
Robust tissue differentiation in head and neck cancer: ex-vivo study with hyperspectral imaging and convolutional neural networks

Tissue classification in surgical workflows between healthy and tumoral regions remains challenging both during and post-surgery. The current standard practice consists of taking small biopsies directly after tumor resection and sending them to pathologists for an intraoperative margin assessment, which is time-consuming and error prone due to the necessarily limited size and number of samples. Then, after the surgery is completed, the resected tumor is sent to the pathology lab, where its type and grading are further confirmed. The present workflow is prone to inaccuracies and particularly difficult when the sample is resected in several pieces. Therefore, an intraoperative tissue classification technology is highly sought-after for a simplified surgical workflow and better patient outcome. Our work aims at using hyperspectral images (HSI) for contact- and tracer-free tissue differentiation. We introduce a deep learning-based algorithm for the classification of tissue type that is based on spectral information and can be applied simultaneously to the whole sample. We illustrate the performance of our method on ex vivo head and neck squamous cell cancer samples. The proposed algorithm can differentiate between three main classes: background, tumor, and healthy tissues. Our experiments first assess the generalization of the neural network on data from unseen cases. We then determine the minimal number of training examples needed to cover the variety of tissue spectral appearances seen in the clinical dataset. We evaluate the influence of the delay between resection and start of image acquisition on the quality of the recorded HSI and the prediction. Qualitative and quantitative evaluations support the applicability of hyperspectral imaging for tissue classification and demonstrate an agreement between surgeon annotations and neural network predictions in most test cases.
Investigating the Role of Semantic Analysis in Automated Answer Scoring

The technology underlying automatic scoring is still in its infancy. The study's primary goal is to provide a new method that can be utilized for computerized response grading. Research is considering semantic analysis to fulfill this objective. Research work has considered the deep learning approach and considered the long short-term memory technique. We did this by joining the semitone-filtered spectrogram's first-order difference to the original input. With this improvement, we saw a dramatic boost in transcribing efficiency. Recurrent neural networks (RNNs) have the advantage over feed-forward neural networks (FNN) in music response scoring because they can learn the temporal dependence of sequential input. The long short-term memory unit's memory block is updated only while an input or forgets gate is open, and gradients may move across memory cells without being multiplied at each time step. Together, the networks’ backward and forward layers provide access to the past and future of the specified period. The suggested method outperforms the state-of-the-art, according to the studies conducted. When compared to an unfiltered dataset, accuracy in the filtered dataset ranges from 95 to 97%, precision ranges from 0.95 to 0.90 for filtered datasets, compared to 0.84–0.90 for unprocessed datasets. According to simulation findings, the proposed long short-term memory approach is performing better than the traditional system in terms of f1 score, precision, and recall.
MathKnowTopic: Creation of a Unified Knowledge Graph-Based Topic Modeling from Mathematical Text Books

Most of the mathematical text documents are complex to understand and so it is very difficult to derive meaningful information from the text. In order to overcome this problem, MathKnowTopic: a unified knowledge graph-based topic modeling approach has been proposed to understand complex mathematical text documents and derive meaningful insights. A knowledge graph is a directed graph-based structure which depicts the semantic relations between the nodes. Topic modeling technique discovers the hidden semantic topics from the unstructured texts. The creation of a Unified Knowledge Graph Topic Modeling (UKGTM) consists of the following steps such as text preprocessing, entity and entity pairs extraction, relation or predicate extraction, construction of directed knowledge graph, topic model generation using Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic and optimum number of topics identification using evaluation. The dataset used in this paper is the term based on mathematical text books of class 1 to class 6. The proposed system is evaluated using performance metrics such as topic coherence score, and cognitive metrics such as knowledge capacity and incrementality. The results indicate that the KG+BERTopic (all-MiniLM-L6-v2) renders meaningful topics based on topic coherence score (0.6387). In terms of knowledge capacity cognitive score (0.7680), KG+BERTopic (all-MiniLM-L6-v2) performs well and Kg+BERTopic (Universal Sentence Encoder) performs well based on incrementality cognitive score (0.9925).