Reviewerly/exHarmony huggingface dataset:
A deep residual convolutional neural network for mineral classification

In recent years, the deep learning computing paradigm has revolutionized the way of remote sensing data analysis. The emerging hyperspectral remote sensing has paved the way for the efficient and accurate exploration of the minute features of the earth’s surface due to the increased number of contiguous spectral bands. Hence, hyperspectral images can efficiently impart useful information about mineral resources for precise discrimination and identification in lithological studies. Rapid advancements in computing capabilities and deep learning techniques give the research community a new impulse to develop advanced, robust, and efficient hyperspectral remote sensing-based mineral classification frameworks. The present study aims to introduce two novel deep learning-based mineral classification frameworks: mineral-CNN-LSTM and mineral-ResNet. The architecture of mineral-CNN-LSTM is based on 1D-CNN and LSTM model, whereas the architecture of mineral-ResNet is based on 1D-CNN, LSTM model, and residual connections. The frameworks use raw data as input without feature selection or data augmentation preprocessing steps. The widely used early stop method is also utilized to prevent overfitting of the framework during the training process. The experimental evaluation carried out over the AVIRIS hyperspectral image scene of the Cuprite mining area confirms that the mineral-ResNet can effectively identify most of the minerals such as Alunite, Calcite, Halloysite, Kaolinite, Montmorillonite, Muscovite, Chalcedony with the overall accuracy of 92.16%, and kappa value of 0.89 and mineral-CNN-LSTM achieved the overall accuracy of 91.71% and kappa value of 0.88 for these minerals. Furthermore, a comparative evaluation of the proposed frameworks has been performed with widely used Convolutional Neural Network (CNN) based architectures such as VGG19, VGG16, ResNet-50, and AlexNet; and various machine learning based classifiers. The proposed architectures offer better performance with shorter testing and training time than these existing CNN-based architectures. The proposed framework could be useful for other earth observation-related applications in various fields such as agriculture, forestry, geology, hydrology, ecology, urban planning, military and defense applications, etc.
Developing a universal phenotyping algorithm to identify patients with clinically diagnosed and probable Alzheimer’s disease using electronic health record data

Abstract Background Despite significant advancements in our understanding of Alzheimer’s disease (AD) over the last decade, no disease‐modifying treatments exist for it. Rich clinical information on patients with AD is currently available in electronic health records (EHR) worldwide. Our aim is to develop a portable phenotyping algorithm to mine EHRs and identify patients with clinical and probable preclinical AD. Identified patients can subsequently be used as cohorts in future biomedical research studies. Method We developed an algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This enables other institutes using this standardized EHR data representation to avail the algorithm with minimum effort for local implementation. The algorithm’s backbone is based on ubiquitous information readily derived from EHRs such as ICD‐10 billing codes, medication history, cognitive test scores (Mini‐Mental State Exam), imaging reports and lab results. The model classifies identified patients into three categories based on high (category 1), intermediate (category 2) and low (category 3) probability with AD. The algorithm was applied to a deidentified EHR database at the Vanderbilt University Medical Center (VUMC) with clinical information from over 3 million individuals. We performed an manual chart review on 100 randomly selected samples of each category’s AD cases to assess its predictive value. Result Implementation of the phenotyping algorithm yielded 274,000 patients with probable Alzheimer’s disease. 4169 patients were identified based on category 1 criteria, 13270 patients were identified using category 2 and 256,302 patients were identified in category 3. Independent chart review resulted in a PPV of 94% for category 1, 86% for category 2 and 8% for category 3 patients. Majority of false positive cases occurred because of mild cognitive impairment (MCI) patients in category 3 who were selected through broad billing codes and medication history. Conclusion A portable phenotyping algorithm can help identify large cohorts of patients with diagnosed and probable Alzheimer’s disease for research use by mining EHRs. Based on preliminary results, our algorithm is a promising attempt to achieve this goal. We aim to improve on it till it selects AD patients with an even higher predictive value (&gt;95%).
Data blind: Universities lag in capturing and exploiting data

Study finds a pervasive void of infrastructure thinking.
Honey Badger Algorithm and Chef-based Optimization Algorithm for Multilevel Thresholding Image Segmentation

Image segmentation has an important role in image processing and computer vision and it is widely used in numerous applications, including feature extraction, pattern recognition, scene analysis, object tracking. Due to its simplicity and effectiveness, multilevel thresholding approach to image segmentation has gained increased research attention in recent years. In this paper, the ability of two recently proposed metaheuristic algorithms, Honey badger algorithm and Chef-based optimization algorithm to ascertain the optimal threshold values based on Kapur’s entropy is systematically examined. The performance of the two multilevel thresholding image segmentation methods are assessed on a dataset of nine standard benchmark images. Based on a fixed number of independent runs, for each test image and a given number of thresholds, the multilevel thresholding performance is reported using the mean and standard deviation of Kapur’s entropy as well as the best objective function value and the associated threshold values.
Cervical Cancer Screening on Multi-class Imbalanced Cervigram Dataset using Transfer Learning

Image classification from a multi-class imbalanced dataset is challenging because it is difficult to detect all the minority classes present in the datasets. In this paper, the authors have extended their recently introduced work on a novel deep learning neural network for binary-class imbalanced datasets, called VGGIN-Net, by applying it to classify different grades of cervical cancer from a multi-class imbalanced dataset having a small number of samples. The experimental results prove that the proposed approach along with the data augmentation and rejection resampling is effective for multi-class imbalanced datasets. As analyzed through extensive experiments on a benchmark cervical screening dataset, the proposed approach in comparison to the other state-of-the-art approaches is vividly proven to be a more efficient method.
End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation

We present an end-to-end Reinforcement Learning (RL) framework for robotic manipulation tasks, using a robust and efficient keypoints representation. The proposed method learns keypoints from camera images as the state representation, through a self-supervised autoencoder architecture. The key-points encode the geometric information, as well as the relationship of the tool and target in a compact representation to ensure efficient and robust learning. After keypoints learning, the RL step then learns the robot motion from the extracted keypoints state representation. The keypoints and RL learning processes are entirely done in the simulated environment. We demonstrate the effectiveness of the proposed method on robotic manipulation tasks including grasping and pushing, in different scenarios. We also investigate the generalization capability of the trained model. In addition to the robust keypoints representation, we further apply domain randomization and adversarial training examples to achieve zero-shot sim-to-real transfer in real-world robotic manipulation tasks.
A multi-criteria approach to rating Metaverse games

Non-Fungible Tokens (NFTs) took the already profitable gaming industry to a whole new level. Before that, people would pay to buy games, and there were also some in-game purchases. But now every item in games is an NFT, and different NFTs have different prices. A lot of users consider NFT metaverse games an investment opportunity. Therefore, it is vital to evaluate these metaverse games and rate them to find the most attractive investment opportunities. In this paper, we develop a framework for rating NFT metaverse games by considering their various risks and potential upsides through the PROMETHEE II method. Specially, we design a flip ratio that can take into account both the opportunity to flip another cryptocurrency and the risk of being flipped by another cryptocurrency. Our new flip ratio could be a very useful measure of risk-opportunity analysis. We also analyze the crash risk of NFT game tokens' prices through a non-parametric value at risk analysis, which is compatible with the volatile nature of cryptocurrency prices.
Real-time Dynamic Sign Recognition using MediaPipe

People live in societies, so communication is necessary to express their needs and ideas. Deaf-mute people use sign language as their means of communication, but most people are unaware of its meaning, so creating an effective system to help them communicate is important. This paper proposes an American Sign Language (ASL) recognition system for 12 dynamic signs in real-time using the MediaPipe framework and a Long Short-Term Memory (LSTM) network. To improve system performance, only the relevant features were extracted from the dynamic sign video, and two new useful features were generated (angles and distances). The proposed system achieved a test accuracy of 97,2%.
I2CR: Improving Noise Robustness on Keyword Spotting using Inter-Intra Contrastive Regularization

Noise robustness in keyword spotting remains a challenge as many models fail to overcome the heavy influence of noises, causing the deterioration of the quality of feature embeddings. We proposed a contrastive regularization method called Inter-Intra Contrastive Regularization (I2CR) to improve the feature representations by guiding the model to learn the fundamental speech information specific to the cluster. This involves maximizing the similarity across Intra and Inter samples of the same class. As a result, it pulls the instances closer to more generalized representations that form more prominent clusters and reduces the adverse impact of noises. We show that our method provides consistent improvements in accuracy over different backbone model architectures under different noise environments. We also demonstrate that our proposed framework has improved the accuracy of unseen out-of-domain noises and unseen variant noise SNRs. This indicates the significance of our work with the overall refinement in noise robustness.
Exploring the integration of blockchain technology into supply chain: challenges and performance

Purpose In the era of digitization, blockchain technology (BCT) has gained huge attraction in a variety of business processes including operations and supply chains (SC) of different industrial sectors. This paper aims that the adoption of BCT in SC enables the industries to integrate the stakeholders and its distinct features enhance the performance of SC. Design/methodology/approach An attempt is made to develop a BCT platform a pilot case of bearing-SC and identify the challenges and their solutions in the integration of BCT with SC. Moreover, the impact of BCT on measures of SC performance is also studied. Findings The results show enhanced transparency in the transactions, better security of the data, diminished latency period for transfer of information, improved traceability of the bearing, reduced time for payment transfer process and decreased nonvalue-added activities. Ultimately, overall trust of the customer can be improved by the integration of BCT in bearing-SC. Originality/value In the same way, BCT can be applied to SC of other components to assure the product quality to the end-users.
Fast and robust video-based exercise classification via body pose tracking and scalable multivariate time series classifiers

Recent technological advancements have spurred the usage of machine learning based applications in sports science and healthcare. Using wearable sensors and video cameras to analyze and improve the performance of athletes, has become widely popular. Physiotherapists, sports coaches and athletes actively look to incorporate the latest technologies in order to further improve performance and avoid injuries. While wearable sensors are very popular, their use is hindered by constraints on battery power and sensor calibration, especially for use cases which require multiple sensors to be placed on the body. Hence, there is renewed interest in video-based data capture and analysis for sports science. In this paper, we present the application of classifying strength and conditioning exercises using video. We focus on the popular Military Press exercise, where the execution is captured with a video-camera using a mobile device, such as a mobile phone, and the goal is to classify the execution into different types. Since video recordings need a lot of storage and computation, this use case requires data reduction, while preserving the classification accuracy and enabling fast prediction. To this end, we propose an approach named BodyMTS to turn video into time series by employing body pose tracking, followed by training and prediction using multivariate time series classifiers. We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors. We compare BodyMTS to state-of-the-art deep learning methods which classify human activity directly from videos and show that BodyMTS achieves similar accuracy, but with reduced running time and model engineering effort. Finally, we discuss some of the practical aspects of employing BodyMTS in this application in terms of accuracy and robustness under reduced data quality and size. We show that BodyMTS achieves an average accuracy of 87%, which is significantly higher than the accuracy of human domain experts.
Anomaly detection in univariate time series incorporating active learning

In this research. we study anomaly detection in univariate time series and optimize according to a business objective using a novel active learning approach. The motivation is to detect anomalies while monitoring systems within an IT infrastructure, known as intrusion detection, specifically for hotel organizations. The proposed detector is based on moving averages in combination with a prediction interval, where parameters are optimized via an active learning component. By using prediction intervals, the results are easily interpretable for domain experts due to the white-box nature of the detector. Annotations originating from domain experts serve as input to acquire oracle parameters, which are obtained via Bayesian optimization using Gaussian process. The detector is tested on the Numenta Anomaly Benchmark (NAB) and is compared to commonly used black-box models.
Improvement of the PBFT Algorithm Based on Grouping and Reputation Value Voting

An improved practical Byzantine fault tolerance (Practical Byzantine Fault Tolerant consensus algorithm based on reputation, RPBFT) algorithm based on grouping and reputation value voting is proposed for the problems of high communication complexity, poor scalability, and random selection of master nodes of the practical Byzantine fault tolerance (PBFT) consensus algorithm of the consortium chain. First, the consistency process is optimized to take the response speed of nodes to each group leader as the basis of grouping, and the intragroup consensus is performed. The group leader then takes the result of intragroup consensus and participates in extra-group consensus to reduce the frequency and time of internode communication. Second, the reputation model and voting mechanism are proposed, and the group leader is generated by node reputation value voting, which enhances the initiative and reliability of trusted nodes and reduces the abnormal nodes as group leader. Finally, a simulation and performance testing system based on this improved scheme is built to prove the effectiveness as well as the usability of the scheme through simulation experiments. The experimental results show that when the number of network nodes is 36, the throughput of the RPBFT algorithm is six times that of PBFT. Therefore, the consensus delay is reduced by 91.7%, and the communication overhead is reduced by 37.8%.
Sentiment Analysis of Tweets During the COVID-19 Pandemic Using Multinomial Logistic Regression

Recently, the research on sentimental analysis has been growing rapidly. The tweets of social media are extracted to analyze the user sentiments. Many of the studies prefer to apply machine learning algorithms for performing sentiment analysis. In the current pandemic, there is an utmost importance to analyze the sentiments or behavior of a person to make the decisions as the whole world is facing lockdowns in multiple phases. The lockdown is psychologically affecting the human behavior. This study performs a sentimental analysis of Twitter tweets during lockdown using multinomial logistic regression algorithm. The proposed system framework follows the pre-processing, polarity and scoring, and feature extracting before applying the machine learning model. For validating the performance of proposed framework, other three majorly used machine learning based models-- namely decision tree, naïve Bayes, and K-nearest neighbors-- are implemented. Experimental results prove that the proposed framework provides improved accuracy over other models.
A question-guided multi-hop reasoning graph network for visual question answering

Visual Question Answering (VQA) requires reasoning about the visually-grounded relations in the image and question context. A crucial aspect of solving complex questions is reliable multi-hop reasoning, i.e., dynamically learning the interplay between visual entities in each step. In this paper, we investigate the potential of the reasoning graph network on multi-hop reasoning questions, especially over 3 “hops.” We call this model QMRGT: A Question-Guided Multi-hop Reasoning Graph Network. It constructs a cross-modal interaction module (CIM) and a multi-hop reasoning graph network (MRGT) and infers an answer by dynamically updating the inter-associated instruction between two modalities. Our graph reasoning module can apply to any multi-modal model. The experiments on VQA 2.0 and GQA (in fully supervised and O.O.D settings) datasets show that both QMRGT and pre-training V&L models+MRGT lead to improvement on visual question answering tasks. Graph-based multi-hop reasoning provides an effective signal for the visual question answering challenge, both for the O.O.D and high-level reasoning questions.
Segmentation and classification of lymphoblastic leukaemia using quantum neural network

Abstract In lymphoblastic leukaemia (ALL), the bone marrow naturally produces immature cells. Each year ALL is diagnosed with over 6500 instances, and the trend is still going upward. Technological advancements in AI and big data analytics help doctors and radiologists make accurate and efficient clinical decisions. The proposed method consists of two core steps: segmentation and classification based on the quantum convolutional networks. A three‐dimensional U‐network is proposed having 70 layers that are trained on the optimal hyperparameters, which provides 0.98 dice scores. The four‐qubit quantum transfer learning model is proposed for classifying different types of blood cells. The accuracies achieved are 0.99 on blast cells, 0.99 on Basophils, 0.98 on Eosinophils, 0.97 on Neutrophils, 0.99 on Lymphocytes, and 0.96 on Monocytes. The proposed classification model provides 0.99 average accuracy.
Accelerating N-Bit Operations over TFHE on Commodity CPU-FPGA

TFHE is a fully homomorphic encryption (FHE) scheme that evaluates Boolean gates, which we will hereafter call Tgates, over encrypted data. TFHE is considered to have higher expressive power than many existing schemes in that it is able to compute not only N-bit Arithmetic operations but also Logical/Relational ones as arbitrary ALR operations can be represented by Tgate circuits. Despite such strength, TFHE has a weakness that like all other schemes, it suffers from colossal computational overhead. Incessant efforts to reduce the overhead have been made by exploiting the inherent parallelism of FHE operations on ciphertexts. Unlike other FHE schemes, the parallelism of TFHE can be decomposed into multilayers: one inside each FHE operation (equivalent to a single Tgate) and the other between Tgates. Unfortunately, previous works focused only on exploiting the parallelism inside Tgate. However, as each N-bit operation over TFHE corresponds to a Tgate circuit constructed from multiple Tgates, it is also necessary to utilize the parallelism between Tgates for optimizing an entire operation. This paper proposes an acceleration technique to maximize performance of a TFHE N-bit operation by simultaneously utilizing both parallelism comprising the operation. To fully profit from both layers of parallelism, we have implemented our technique on a commodity CPU-FPGA hybrid machine with parallel execution capabilities in hardware. Our implementation outperforms prior ones by 2.43× in throughput and 12.19× in throughput per watt when performing N-bit operations under the 128-bit quantum security parameters.
LayouTransformer

Generating legal and diverse layout patterns to establish large pattern libraries is fundamental for many lithography design applications. Existing pattern generation models typically regard the pattern generation problem as image generation of layout maps and learn to model the patterns via capturing pixel-level coherence, which is insufficient to achieve polygon-level modeling, e.g., shape and layout of patterns, thus leading to poor generation quality. In this paper, we regard the pattern generation problem as an unsupervised sequence generation problem, in order to learn the pattern design rules by explicitly modeling the shapes of polygons and the layouts among polygons. Specifically, we first propose a sequential pattern representation scheme that fully describes the geometric information of polygons by encoding the 2D layout patterns as sequences of tokens, i.e., vertexes and edges. Then we train a sequential generative model to capture the long-term dependency among tokens and thus learn the design rules from training examples. To generate a new pattern in sequence, each token is generated conditioned on the previously generated tokens that are from the same polygon or different polygons in the same layout map. Our framework, termed LayouTransformer, is based on the Transformer architecture due to its remarkable ability in sequence modeling. Comprehensive experiments show that our LayouTransformer not only generates a large amount of legal patterns but also maintains high generation diversity, demonstrating its superiority over existing pattern generative models.