Reviewerly/exHarmony huggingface dataset:
Blockchain: Opportunities in the healthcare sector and its uses in COVID-19

As the world grapples with the COVID-19 pandemic and major populations are getting vaccinated, increasing realization processes healthcare industry needs to be augmented. It includes managing supply chains, healthcare records, and patient care. With a scarcity of time and resources, adaptation of blockchain technology will help mitigate the pressures on existing infrastructure. A blockchain distributed ledger helps to exchange health information securely without complex intermediation of trust with secure access. The organizations and persons in the blockchain network can verify and authorize the data, thus protecting patient identity, privacy, medical information system, and reducing transaction costs. The chapter examines managing and protecting electronic medical records and personal health records data using blockchain. It also analyses issues in healthcare, blockchain implementation, and its uses in the COVID-19 pandemic. The authors put forward the idea that even after the world is free of the Coronavirus, there will be pressures on the healthcare industry and Blockchain technology is the way to alleviate this pressure.
Classification of Histopathologic Images of Breast Cancer by Multi-teacher Small-sample Knowledge Distillation

Model fusion can effectively improve the effect of model prediction, but it will bring about an increase in time. In this paper, the dual-stage progressive knowledge distillation is improved in combination with multi-teacher knowledge distillation technology. A simple and effective multi-teacher&#x0027;s Softtarget integration method is proposed in multi-teacher network knowledge distillation. Improve the guiding role of excellent models in knowledge distillation. Dual-stage progressive knowledge distillation is a method for small sample knowledge distillation. A progressive network grafting method is used to realize knowledge distillation in a small sample environment. In the first step, the student blocks are grafted one by one onto the teacher network and intertwined with other teacher blocks for training, and the training process only updates the parameters of the grafted blocks. In the second step, the trained student blocks are grafted onto the teacher network in turn, so that the learned student blocks adapt to each other and finally replace the teacher network to obtain a lighter network structure. Using Softtarget acquired by this method in Dual-stage progressive knowledge distillation instead of Hardtarget training, excellent results were obtained on BreakHis data sets.
Con-Net: A Consolidated Light-Weight Image Restoration Network

There has been a considerable gap between the recent high-resolution display technologies and the short storage of its content. However, most of the existing restoration methods are restricted by local convolution operations and equal treatment of the diverse information in degraded image. These approaches being degradation-specific employ the same rigid spatial processing across different images ultimately resulting in high memory consumption. For overcoming this limitation we propose Con-Net, a network design capable of exploiting the non-uniformities of the degradations in spatial-domain with limited number of parameters (656k). Our proposed Con-Net comprises of basically two main components, (1) a spatial-degradation aware network for extracting the diverse information inherent in any degraded image, and (2) a holistic attention refinement network for exploiting the knowledge from the degradation aware network to selectively restore the degraded pixels. In a nutshell, our proposed method is generalizable for three applications: image denoising, super-resolution and real-world low-light enhancement. Extensive qualitative and quantitative comparison with prior arts on 8 benchmark datasets demonstrates the efficacy of our proposed Con-Net over existing state-of-the-art degradation-specific architectures, by huge parameter and FLOPs reduction in all the three tasks.
RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation

The segmentation task has traditionally been formulated as a complete-label (We use the term “complete label” to represent the set of all predefined categories in the dataset.) pixel classification task to predict a class for each pixel from a fixed number of predefined semantic categories shared by all images or videos. Yet, following this formulation, standard architectures will inevitably encounter various challenges under more realistic settings where the scope of categories scales up (e.g., beyond the level of $$1\textrm{k}$$ ). On the other hand, in a typical image or video, only a few categories, i.e., a small subset of the complete label are present. Motivated by this intuition, in this paper, we propose to decompose segmentation into two sub-problems: (i) image-level or video-level multi-label classification and (ii) pixel-level rank-adaptive selected-label classification. Given an input image or video, our framework first conducts multi-label classification over the complete label, then sorts the complete label and selects a small subset according to their class confidence scores. We then use a rank-adaptive pixel classifier to perform the pixel-wise classification over only the selected labels, which uses a set of rank-oriented learnable temperature parameters to adjust the pixel classifications scores. Our approach is conceptually general and can be used to improve various existing segmentation frameworks by simply using a lightweight multi-label classification head and rank-adaptive pixel classifier. We demonstrate the effectiveness of our framework with competitive experimental results across four tasks, including image semantic segmentation, image panoptic segmentation, video instance segmentation, and video semantic segmentation. Especially, with our RankSeg, Mask2Former gains + $$0.8\%$$ /+ $$0.7\%$$ /+ $$0.7\%$$ on ADE20K panoptic segmentation/YouTubeVIS 2019 video instance segmentation/VSPW video semantic segmentation benchmarks respectively. Code is available at: https://github.com/openseg-group/RankSeg .
Detecting Multiple Steganography Methods in Speech Streams Using Multi-Encoder Network

With the development of speech steganography technology, steganographers are more and more inclined to realize more secure covert communication by combining a series of steganography methods. Thus, this letter presents a novel multi-encoder network (MENet) to achieve more efficient detection of multiple steganography methods. Differing from the previous work, MENet utilizes multiple private encoders to individually model the private features of each coding element, introduces a shared encoder based on an attention mechanism to fuse multiple private features for achieving better feature representation, and finally exploits a shared decoder to reduce feature dimensionality as well as give predictions. Taking the existing state-of-the-art steganography methods as the detection targets, the performance of the proposed steganalysis method is evaluated comprehensively and compared with the state-of-the-art ones. The experimental results show that the detection performance of MENet is overall better than the existing steganalysis methods, especially with low embedding rates and short speech sample lengths.
Step by Step to Make Augmented Reality Filters for Molecular Models

Augmented reality (AR) filters are digital interactions applied to users’ faces or environment to add to or change what they are seeing in the real world. By using free software, it is possible to easily make many AR filters for learning chemistry. Here I show how to make AR filters of molecular models and post them on social media in just two steps.
A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records

Headache frequency, defined as the number of days with any headache in a month (or four weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional natural language processing (NLP) algorithms.This was a retrospective cross-sectional study with human subjects identified from three tertiary headache referral centers- Mayo Clinic Arizona, Florida, and Rochester. All neurology consultation notes written by more than 10 headache specialists between 2012 to 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) Model zero-shot (3) GPT-2 QA model few-shot training fine-tuned on Mayo Clinic notes; and (4) GPT-2 generative model few-shot training fine-tuned on Mayo Clinic notes to generate the answer by considering the context of included text.The GPT-2 generative model was the best-performing model with an accuracy of 0.92[0.91 - 0.93] and R2 score of 0.89[0.87, 0.9], and all GPT2-based models outperformed the ClinicalBERT model in terms of the exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy 0.27[0.26 - 0.28], it demonstrated a high R2 score 0.88[0.85, 0.89], suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model.We developed a robust model based on a state-of-the-art large language model (LLM)- a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub.
Mining concise patterns on graph-connected itemsets

The itemset is a basic and usual form of data. People can obtain new insights into their business by discovering its implicit regularities through pattern mining. In some real applications, e.g., network alarm association, the itemsets usually have the following two characteristics: (1) the observed samples come from different entities, with inherent structural relationships implied in their static properties; (2) the samples are scarce, which may lead to incomplete pattern extraction. This paper considers how to efficiently find a concise set of patterns on such kind of data. Firstly, we use a graph to express the entities and their interconnections and propagate every sample to every node with a weight, determined by the pre-defined combination of kernel functions based on the similarities of the nodes and patterns. Next, the weight values can be naturally imported into the MDL-based filtering process and bring a differentiated pattern set for each node. Experiments show that the solution can outperform the global solution (trading all nodes as one) and isolated solution (removing all edges) on simulated and real data, and its effectiveness and scalability can be further verified in the application of large-scale network operation and maintenance.
Two generalizations of Markov blankets.

In a probabilistic graphical model on a set of variables $V$, the Markov blanket of a random vector $B$ is the minimal set of variables conditioned to which $B$ is independent from the remaining of the variables $V \backslash B$. We generalize Markov blankets to study how a set $C$ of variables of interest depends on~$B$. Doing that, we must choose if we authorize vertices of $C$ or vertices of $V \backslash C$ in the blanket. We therefore introduce two generalizations. The Markov blanket of $B$ in $C$ is the minimal subset of $C$ conditionally to which $B$ and $C$ are independent. It is naturally interpreted as the inner boundary through which $C$ depends on $B$, and finds applications in feature selection. The Markov blanket of $B$ in the direction of $C$ is the nearest set to $B$ among the minimal sets conditionally to which ones $B$ and $C$ are independent, and finds applications in causality. It is the outer boundary of $B$ in the direction of $C$. We provide algorithms to compute them that are not slower than the usual algorithms for finding a d-separator in a directed graphical model. All our definitions and algorithms are provided for directed and undirected graphical models.
Self-Supervised Learning via Conditional Motion Propagation

Intelligent agent naturally learns from motion. Various self-supervised algorithms have leveraged motion cues to learn effective visual representations. The hurdle here is that motion is both ambiguous and complex, rendering previous works either suffer from degraded learning efficacy, or resort to strong assumptions on object motions. In this work, we design a new learning-from-motion paradigm to bridge these gaps. Instead of explicitly modeling the motion probabilities, we design the pretext task as a conditional motion propagation problem. Given an input image and several sparse flow guidance vectors on it, our framework seeks to recover the full-image motion. Compared to other alternatives, our framework has several appealing properties: (1) Using sparse flow guidance during training resolves the inherent motion ambiguity, and thus easing feature learning. (2) Solving the pretext task of conditional motion propagation encourages the emergence of kinematically-sound representations that poss greater expressive power. Extensive experiments demonstrate that our framework learns structural and coherent features; and achieves state-of-the-art self-supervision performance on several downstream tasks including semantic segmentation, instance segmentation, and human parsing. Furthermore, our framework is successfully extended to several useful applications such as semi-automatic pixel-level annotation. Project page: "http://mmlab.ie.cuhk.edu.hk/projects/CMP/".
Cloud Storage–Optimization of Initial Phase for Privacy-Preserving Public Auditing

The integrity of data confirms that an unapproved user cannot temper the outsourced data in cloud storage. The cloud storage needs to be secure from unapproved tempering. For integrity verification process, data owner generates the signature which is sent to the third-party auditor. It is a one-time process, but it increases the overhead of the data owner. We have analyzed the existing data integrity auditing schemes along with their distinctions. The proposed system supports privacy-preserving integrity auditing by using homomorphic linear authentication and employing Boneh–Lynn–Shacham-based signature technique. We extend our research to enable the data owner to speed up the initial phase through detailed experiments and comparisons between single-thread and multi-thread models using different core of CPUs. The proposed scheme demonstrated by using multithreading architecture on multi-core CPU for getting better performance.
Data-Driven Neuron Allocation for Scale Aggregation Networks

Successful visual recognition networks benefit from aggregating information spanning from a wide range of scales. Previous research has investigated information fusion of connected layers or multiple branches in a block, seeking to strengthen the power of multi-scale representations. Despite their great successes, existing practices often allocate the neurons for each scale manually, and keep the same ratio in all aggregation blocks of an entire network, rendering suboptimal performance. In this paper, we propose to learn the neuron allocation for aggregating multi-scale information in different building blocks of a deep network. The most informative output neurons in each block are preserved while others are discarded, and thus neurons for multiple scales are competitively and adaptively allocated. Our scale aggregation network (ScaleNet) is constructed by repeating a scale aggregation (SA) block that concatenates feature maps at a wide range of scales. Feature maps for each scale are generated by a stack of downsampling, convolution and upsampling operations. The data-driven neuron allocation and SA block achieve strong representational power at the cost of considerably low computational complexity. The proposed ScaleNet, by replacing all 3x3 convolutions in ResNet with our SA blocks, achieves better performance than ResNet and its outstanding variants like ResNeXt and SE-ResNet, in the same computational complexity. On ImageNet classification, ScaleNets absolutely reduce the top-1 error rate of ResNets by 1.12 (101 layers) and 1.82 (50 layers). On COCO object detection, ScaleNets absolutely improve the mmAP with backbone of ResNets by 3.6 (101 layers) and 4.6 (50 layers) on Faster RCNN, respectively. Code and models are released at this https URL.
Indexes to Find the Optimal Number of Clusters in a Hierarchical Clustering

Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mining. Most clustering methods require establishing the number of clusters beforehand. However, due to the size of the data currently used, predicting that value is at a high computational cost task in most cases. In this article, we present a clustering technique that avoids this requirement, using hierarchical clustering. There are many examples of this procedure in the literature, most of them focusing on the dissociative or descending subtype, while in this article we cover the agglomerative or ascending subtype. Being more expensive in computational and temporal cost, it nevertheless allows us to obtain very valuable information, regarding elements membership to clusters and their groupings, that is to say, their dendrogram. Finally, several sets of data have been used, varying their dimensionality. For each of them, we provide the calculations of internal validation indexes to test the algorithm developed, studying which of them provides better results to obtain the best possible clustering.
A Dominant Point-Based Algorithm for Finding Multiple Longest Common Subsequences in Comparative Genomics

Finding the longest common subsequence is a classic and well-studied problem in the field of computer science and considered as an NP-hard problem. There are many application of LCS in the field of bioinformatics, computational genomics, image processing, file comparison, etc. There are many algorithms are present to find the similarity between the given strings and its special cases. As there is a tremendous increase in the biological data and it requires an efficient mechanism to deal with them, many efforts have been taken to reduce the time and space complexity of the given problem. In this paper, we presented a novel algorithm for the general case of multiple LCS problems, i.e., finding a longest common subsequence in the given two strings. Our algorithm works on dominant point approach to compute the LCS of the given string. When applied to multiple strings of length each 1000, 2000, 3000, 4000, and 5000, characters, it is found that our algorithm works two or three magnitude faster than existing algorithm and it requires less space compared to existing algorithms.
Towards a 5c theory of communication for scrum-based distributed projects

Scrum-based distributed projects face numerous challenges and failures due geographical, dispersed, times and cultural differences. To improve the success of such software development projects, there is a need to identify factors crucial to achieving the required level of communication between the distributed stakeholders. In this paper, we reviewed the issues and challenges associated with communication in scrum-based distributed projects and explored factors that could improve communication for scrum-based distributed projects. A total number of 25 interviews were conducted in various geographical regions which helped to identify five major factors, i.e. competency, correlation, comprehension, contentment and commitment, crucial to the success of scrum-based distributed projects. Our identification of these factors presents a research and practice approach to developing a theory of communication that would help project managers, scrum masters and practitioners to improve practices while undertaking scrum-based distributed projects.
The Effects of Using Realia Media on Increasing Science Learning Outcomes of Elementary School Students

The aimed of this study was to examine how much are the effects of using realia media in increasing science learning outcomes of elementary school students with meta-analysis design. Meta-analysis is a secondary analysis after other researchers have done their own analyses, allowing the meta-analyzer to go beyond what had been accomplished in the past. Steps of meta-analysis are formulating a problem for meta-analysis, searching literature, evaluating literature, statistically analyzing effect size and reporting the result(s) of the meta-analysis. To perform a meta-analysis, the researcher computes an effect size and variance for each study and then computes a weighted mean of these effect sizes. Effect size is a measure of strength in the meta-analysis. Data collection employed documentation techniques. The population/sample in this research was all written documents about the Effects of using Realia Media on increasing science learning outcomes of elementary school students. The written documents were articles. Data or information obtained from each sample was determined based on its compatibility with the theme of this research. Through the utilization of realia media around the student learning environment, students are able to create an effective and efficient learning atmosphere. From the results by using the comparative descriptive analysis with percentage showed a significant effects of using realia media to the students' classical outcomes in science learning.
Adaptive Nonparametric Variational Autoencoder.

Clustering is used to find structure in unlabeled data by grouping similar objects together. Cluster analysis depends on the definition of similarity in the feature space. In this paper, we propose an Adaptive Nonparametric Variational Autoencoder (AdapVAE) to perform end-to-end feature learning from raw data jointly with cluster membership learning through a Nonparametric Bayesian modeling framework with deep neural networks. It has the advantage of avoiding pre-definition of similarity or feature engineering. Our model relaxes the constraint of fixing the number of clusters in advance by assigning a Dirichlet Process prior on the latent representation in a low-dimensional feature space. It can adaptively detect novel clusters when new data arrives based on a learned model from historical data in an online unsupervised learning setting. We develop a joint online variational inference algorithm to learn feature representations and cluster assignments via iteratively optimizing the evidence lower bound (ELBO). Our experimental results demonstrate the capacity of our modelling framework to learn the number of clusters automatically using data, the flexibility to detect novel clusters with emerging data adaptively, the ability of high quality reconstruction and generation of samples without supervised information and the improvement over state-of-the-art end-to-end clustering methods in terms of accuracy on both image and text corpora benchmark datasets.