Reviewerly/exHarmony huggingface dataset:
Using Convolutional Neural Networks with Transfer Learning for Action Recognition in Videos

Video classification is a broad field within the machine learning landscape. There are many videos on the internet and being able to classify them allows people to keep track of certain trends. Due to the volume of videos this task is a challenge. In this research we design models on a relatively small dataset to explore the possibilities of separating action recognition from object recognition in hopes of improving the accuracy of previous models. We also explore data augmentation techniques so as to try and expand a limited dataset. The models that we designed are able to identify features from motion data, which allows it to perform better at action recognition.
An Approach to Restoring Missing Data in an Experimental Sample

The paper deals with the problem of restoring missing experimental data in modeling tasks. Real data samples may have missing values for some variables or a study period. All this leads to the risk of building an inaccurate model and, as a result, the experiment failed. In the paper, this problem is considered on data of a real task. A non-linear interpolation model was built for the dependence of the concentration of chlorophyll in algae on the concentration of the pollutant dependent on time. The found model made it possible to restore missing data for the days when measurements were absent, as well as to find out on which day and at what concentration of the pollutant the algae would die.
Process reduction of integrated emergency-defenses system based on heuristic

The complexity of the integrated emergency-defenses (ED) system process is mainly reflected in the various sizes of the process, the lots of activities, the complex interaction and scheduling relationship. To meet the demand of rapid response on the command process, we propose a heuristic of system process reduction based on critical path merging minimum cycles, which lays a foundation for improving the dynamic response capability of system. The heuristic uses the optimization of reinforcement learning to preserve the critical path in the decision-making process, and merges and reduces the repeated minimum cycles in the critical path, which reduces the delay and uncertainty of information transmission and reduces the complexity of decision implementation effectively. Finally, the experimental results show that the heuristic is effective in process reduction and decision dimension reduction.
Secret Image Sharing and Steganography based on Fuzzy Logic and Prediction Error

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.
Region of Interest Localisation of Hematopoietic Stem/Progenitor Cell Images

Image classification using deep learning has been widely implemented, primarily in medical imaging. However, features and focus regions are extracted by the network, becomes a black box mystery in the feature extraction layer during network training, unlike conventional feature extraction approaches, where various methods can extract image features. Regardless, traditional image feature extraction is laborious to find the most suitable algorithm. It takes time to meet the significant image features before classification and final image localisation, especially for the microscopic images. Therefore, a method to localise the region of interest (ROI) in vitro of the colony-formation unit (CFU) of hematopoietic stem/progenitor cell (HSPC) using gradCAM through deep learning, approaches have been proposed. This work comprises three main phases: CFU data preparation, convolutional neural network (CNN) pre-trained networks and localisation of the ROI. The proposed method has successfully localised the ROI of the CFU HSPC using gradCAM through a deep neural network with 87.5% sensitivity performed by DarkNet19. The finding of this work can be used as a baseline for future CFU HSPC classification that focuses on the CFU region.
Reversible Cyclic Codes over F2+uF2+u2F2

In this article, we investigate the formation of reversible cyclic codes (i.e., its codewords forms a symmetry) over the ring S=F2+uF2+u2F2, where u3=0. We find a unique set of generators for cyclic codes over S and classify reversible cyclic codes to their generators. The dual reversible cyclic codes are studied as well. Moreover, we provide some examples of reversible cyclic codes.
Longest k-tuple Common Sub-Strings

We focus on a new problem that is formulated to find a longest k-tuple of common sub-strings (abbr. k-CSSs) of two or more strings. We present a suffix tree based algorithm for this problem, which can find a longest k-CSS of m strings in $O(kmn^{k})$ time and $O(kmn)$ space where n is the length sum of the m strings. This algorithm can be used to approximate the longest k-CSS problem to a performance ratio $\frac{1}{\epsilon}$ in $O(kmn^{\lceil\epsilon k\rceil})$ time for $\epsilon\in(0,1]$. Since the algorithm has the space complexity in linear order of n, it will show advantage in comparing particularly long strings. This algorithm proves that the problem that asks to find a longest gapped pattern of non-constant number of strings is polynomial time solvable if the gap number is restricted constant, although the problem without any restriction on the gap number was proved NP-Hard. Using a C++ tool that is reliant on the algorithm, we performed experiments of finding longest 2-CSSs, 3-CSSs and 5-CSSs of 2 ~ 14 COVID-19 S-proteins. Under the help of longest 2-CSSs and 3-CSSs of COVID-19 S-proteins, we identified the mutation sites in the S-proteins of two COVID-19 variants Delta and Omicron. The algorithm based tool is available for downloading at https://github.com/lytt0/k-CSS.
KSMDB: A classification method in imbalanced COVID dataset based on KmeansSMOTE and DeBERT

2022 is already the third year of the COVID-19 outbreak, and public opinion information about the outbreak has always been at the forefront of hot searches. The imbalance problem prevalent in many reviews of COVID-19 causes classification models to favor most categories in training and prediction process, resulting in low accuracy of small sample classification data generated by imbalanced data sets. Therefore, it is suggested here that the text classification model is based on the combination of the KMeansSMOTE method combined with DeBERT. First of all, during data processing, the KmeansSMOTE algorithm is utilized to oversample the imbalance of the COVID dataset, which increases the classification accuracy of the model. Besides, we put a stacked denoising bidirectional transformer encoder (DeBERT) to use, a more abstract and richer hidden feature vector is extracted by adding an embedded layer after the input tag, and the noise data is reconstructed to solve the noise problem in the process of raw data existence and oversampling. Furthermore, on the basis of model training, overfitting can be alleviated by adopting an early stopping strategy. A world of experiments using the COVID dataset demonstrates the effectiveness of the proposed method for solving simple imbalance and noise problems. With an overall accuracy of 87%, which improves the classification effect of minority samples and provides a new feasible method for the war of epidemic prevention.
Finding the Most Transferable Tasks for Brain Image Segmentation

Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this paper, we propose a prior knowledge guided and transferability based framework to select the best source tasks among a collection of brain image segmentation tasks, to improve the transfer learning performance on the given target task. The framework consists of modality analysis, RoI (region of interest) analysis, and transferability estimation, such that the source task selection can be refined step by step. Specifically, we adapt the state-of-the-art analytical transferability estimation metrics to medical image segmentation tasks and further show that their performance can be significantly boosted by filtering candidate source tasks based on modality and RoI characteristics. Our experiments on brain matter, brain tumor, and white matter hyperintensities segmentation datasets reveal that transferring from different tasks under the same modality is often more successful than transferring from the same task under different modalities. Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance. And such similarity can be captured using the Structural Similarity index in the label space.
Improved Gradient-Based Optimizer for solving real-world engineering problems

Gradient-based optimizer (GBO) is one of the most promising metaheuristic algorithms, where it proved its efficiency in various fields. GBO combine two major search mechanisms population-based and gradient-based Newton. Thus, it has a strong ability in global search. However, it suffers from dealing with local search problems. In this paper, a new version introduces which integrates the feature of Simulating annealing method (SA) with the GBO (GBOSA) to enhance the local search technique. The proposed GBOSA has been compared with various popular algorithms and improved variants on a set of real-world engineering problems. The experiment results show that GBOSA outperformed the other algorithms in the literature.
TsERL: Two-stage Enhancement of Radical and Lexicon for Chinese Medical Named Entity Recognition

With the large-scale growth of medical data, Chinese medical named entity recognition (NER) has attracted extensive attention, which plays a crucial prerequisite role in extracting high-quality medical information. Many studies have confirmed that additional lexical and radical information can improve the Chinese medical NER model effectively. Still, a method that can effectively utilize both types of information has been lacking. In this paper, various forms of introducing lexical and radical information into the NER task are deeply investigated. On this basis, TsERL: Two-stage Enhancement of Radical and Lexicon is proposed for Chinese medical NER, which constructs the relation graph among radicals, characters, and words. In terms of lexical enhancement, TsERL utilizes word graph to match characters with dynamic word counts from three aspects according to the match position. TsERL follows adaptive embedding (AE) paradigm that is transferable, while maintaining better performance than dynamic architecture (DA) paradigm. In terms of radical enhancement, TsERL constructs a global radical graph according to the role of radicals in the Chinese system. On the graph, character nodes can perceive the information of other character nodes and radical nodes through multi-hop relationships. With the power of GAT, TsERL can obtain character type information and lexical boundary information from the radical graph and the word graph, respectively. Experiments on two public datasets show that TsERL outperforms other models in performance.
Prompt Enhanced Generative MRC Framework for Pancreatic Cancer NER

Medical Named Entity Recognition (NER) is a fundamental but challenging task due to the lack of specialized entity datasets like tumor entities, which are often overlapped and discontinuous. In this paper, we propose a novel Prompt Enhanced Generative Machine Reading Comprehension Framework (PGMRC) to improve the overlapped and discontinuous NER performance. Specifically, we formulate NER as a Machine Reading Comprehension (MRC) task and employ a pre-trained encoder-decoder module to generate entity span sequences according to their entity query. In this way, we adopt query to guide the model to focus on answer entities in context, which can naturally solve entity overlap and alleviate the exposure bias of the generative model. Then, we introduce continuous prompts to the self-attention mechanism in Transformer to reduce the dependence on manually constructed queries. In addition, we annotate 875 pathological documents of pancreatic cancer and construct a Chinese pathological NER dataset (PAN) containing overlapped and discontinuous entities. Finally, we conduct our experiments on three widely used benchmarks (GENIA, ACE04, ACE05) and our dataset PAN. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.
A Knowledge-Enhanced Model with Dual-Channel Encoder for Joint Entity and Relation Extraction from Biomedical Literature

Biomedical entity and relation extraction has attracted increasing attention recently, whereas it remains challenging due to its domain-specific features for the biomedical corpus. Hence, many researchers consider utilizing external knowledge from large-scale databases to enhance the semantic understanding of models. However, these knowledge-enhanced methods usually enrich context information by incorporating the context-independent knowledge into entity representations and lack effective interaction. Actually, inspired by pre-trained language models, we argue that knowledge representations need to be trainable and adapted for different contexts. Therefore, we propose Knowledge-enhanced Dual-channel Iterative Model (KeDcIM), a novel end-to-end joint model for biomedical entity and relation extraction. Experiments show that KeDcIM achieves new state-of-the-art results on two benchmark datasets.
Unified Fine-Grained Biomedical Entity Recognition as a Combination of Boundary Detection and Sequence Generation

Biomedical Named Entity Recognition (BioNER) is a critical component of biomedical information extraction. NER is more challenging in the biomedical domain because of fine-grained entity types and more common nested and discontinuous entity forms. However, none of the BioNER datasets contains a large amount of all three entity forms, including flat, nested, and discontinuous. Not to mention that there is a unified BioNER model for dealing with the above three entity forms simultaneously. Methods in the public domain only focus on identifying text spans and ignore distinguishing fine-grained entity types. To address these issues, we propose a unified framework based on our own BioNER dataset CCNER, which innovatively models the BioNER task as a combination of boundary recognition and sequence generation. CCNER is a comprehensive and fine-grained BioNER dataset, where the proportion of discontinuous, nested, and flat entities in the dataset is 8.9%, 52.6%, and 38.5%, respectively. Meanwhile, it includes five fine-grained entity types. Our proposed framework includes two modules which are boundary detection and entity generation. In the boundary detection module, we propose a sample-based span representation method to determine fine-grained entity boundaries better. Finally, we conduct experiments on four datasets and achieve competitive results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://github.com/lx-hit/BioNER.
Quantum Angle Generator for Image Generation

The Quantum Angle Generator (QAG) is a new generative model for quantum computers. It consists of a parameterized quantum circuit trained with an objective function. The QAG model utilizes angle encoding for the conversion between the generated quantum data and classical data. Therefore, it requires one qubit per feature or pixel, while the output resolution is adjusted by the number of shots performing the image generation. This approach allows the generation of highly precise images on recent quantum computers. In this paper, the model is optimised for a High Energy Physics (HEP) use case generating simplified one-dimensional images measured by a specific particle detector, a calorimeter. With a reasonable number of shots, the QAG model achieves an elevated level of accuracy. The advantages of the QAG model are lined out - such as simple and stable training, a reasonable amount of qubits, circuit calls, circuit size and computation time compared to other quantum generative models, e.g. quantum GANs (qGANs) and Quantum Circuit Born Machines.
A Random Feature Augmentation for Domain Generalization in Medical Image Segmentation

Deep convolutional neural networks (DCNNs) significantly improve the performance of medical image segmentation. Nevertheless, medical images frequently experience distribution discrepancies, which fails to maintain their robustness when applying trained models to unseen clinical data. To address this problem, domain generalization methods were proposed to enhance the generalization ability of DCNNs. Feature space-based data augmentation methods have proven their effectiveness to improve domain generalization. However, existing methods still mainly rely on certain prior knowledge or assumption, which has limitations in enriching the diversity of source domain data. In this paper, we propose a random feature augmentation (RFA) method to diversify source domain data at the feature level without prior knowledge. Specifically, we explore the effectiveness of random convolution at the feature level for the first time and prove experimentallyt hat itc an adequately preserve domain-invariant information while perturbing domainspecific information. Furthermore, tocapture the same domain-invariant information from the augmented features of RFA, we present a domain-invariant consistent learning strategy to enable DCNNs to learn a more generalized representation. Our proposed method achieves state-of-the-art performance on two medical image segmentation tasks, including optic cup/disc segmentation on fundus images and prostate segmentation on MRI images.
Quantum-enhanced Regression Analysis Using State-of-the-art QLSAs and QIPMs

Quantum computing has the potential to speed up machine learning methods. One major direction is using quantum linear algebra to solve linear system problems or optimization problems in the machine learning area. Quantum approaches in the literature for different types of least squares problems demonstrate speedups w.r.t. dimension but have disadvantages w.r.t. precision and condition number. In this paper, we discuss how an iterative refinement scheme can deliver an accurate solution without the excessive cost of Quantum Linear System Algorithms (QLSAs). In addition, we propose an adaptive regularization approach that can mitigate the effect of condition number on solution time. In the second part of this paper, we investigate how state-of-the-art Quantum Interior Point Methods (QIPMs) can solve more sophisticated regression problems such as Lasso regression and support vector machine problems, which can be reformulated as quadratic optimization problems.
Privacy-Preserving and Secure Divide-and-Conquer Learning

The computation need of neural networks has out-paced the capabilities of many individual users whose computers, mobile devices, and other devices are relatively limited in computation power. To solve this problem, currently users need to offload the model training task to the cloud that has many computing resources. On the other hand, many devices on the edge have idling CPU cycles not used. Inspired by the successes of crowdsourcing and decentralized computing platforms such as blockchain and Web3, we propose to outsource an individual's neural network training task to edge devices, such that individuals can train their own neural network models without relying on the centralized cloud. Specifically, we design a divide-and-conquer learning framework in the edge computing environment. A user can divide the training computation of its neural network into neuron-sized computation tasks and distribute them to devices in the edge based on their available resources. The results will be returned to the user and aggregated in an iterative process to obtain the final neural network model. To protect the privacy of the user's data and model, shuffling is done to both the data and the neural network model before the computation task is distributed to edge nodes. Security against misbehaving edge nodes can also be provisioned by redundancy in task assignment.
Dection of Dense Crowed based on Optomized YOLOv3 Algorithm

High density crowd gathering area is the high incidence of accidents. It has significantly important application value in monitoring and analyzing the crowd to prevent the occurrence of accident. YOLOv3 is an excellent framework for speed and accuracy, however, it still suffers from a lack of recall rate and a lack of accurate targeting of dense crowed. This paper proposes the optimized YOLOv3 algorithm based on YOLOv3 to precisely locate and detect pedestrians in dense crowd. The experiments in this paper are based on the WiderPerson dataset, and the optimized network has a significant improvement in mAP values compared with the base YOLOv3, so the model in this paper has a strong practicality and accuracy for pedestrian detection in dense crowds.