Reviewerly/exHarmony huggingface dataset:
Brain Tumor Segmentation through Level Based Learning Model

Brain tumors are potentially fatal presence of cancer cells over a human brain, and they need to be segmented for accurate and reliable planning of diagnosis.Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived.Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities.The scanner used for sensing the location of tumors cells will be subjected to additional protocols and measures for accuracy, in turn, increasing the time and affecting the performance of the entire model.In this view, Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising results.The previous strategies and models failed to adhere to diversity of sizes and shapes, proving to be a well-established solution for detecting tumors of bigger size.Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network (CNN).This proposal intends to furnish a detailed model for sensing early stages of cancer and hence perform segmentation irrespective of the current size and shape of tumors.The size of networks and layers will lead to a significant weightage when multiple kernel sizes are involved, especially in multiresolution environments.On the other hand, the proposed model is designed with a novel approach including a dilated convolution and level-based learning strategy.When the convolution process is dilated, the process of feature extraction deals with multiscale objective and level-based learning eliminates the shortcoming of previous models, thereby enhancing the quality of smaller tumors cells and shapes.The level-based learning approach also encapsulates the feature reconstruction processes which highlights the sensing of small-scale tumors growth.Inclusively, segmenting the images is performed with better accuracy and hence detection becomes better when compared to that of hierarchical approaches.
An Approach for Unlabeled Tasks Prioritization

Managing a task backlog is a vital and challenging activity, particularly in a large firm with thousands of developers and a massive backlog. It is beneficial to software managers to have some tools at their disposal to prioritize activities from the current backlog so that only important tasks could be accessible for tracking. This study presents a novel approach to the prioritization of software development tasks with the use of a neural network developed in the presence of unlabeled data
YOLO-A2G: An air-to-ground high-precision object detection algorithm based on YOLOv5

Abstract Air-to-ground object detection is playing an increasingly important role in a variety of ground awareness and cognitive missions such as fighter aircraft attempting to assault and defend ground defense barrier fortifications and strike and destroy ground objects. However, air-to-ground object detection becomes very challenging due to the insufficient number of battlefield samples in air-to-ground imaging, many ground background disturbances and large-scale variation. In this paper, an improved air-to-ground object detection algorithm, YOLO-A2G, is proposed to solve this problem based on YOLOv5. In YOLO-A2G, firstly, in response to the insufficient number of samples, we used the direct and inverse Visual Focus (VF) affine a data augmentation algorithm to enrich and expand the samples in addition to the original data augmentation algorithm of YOLOv5. We then introduced the Coordinate Attention (CA) mechanism into the head network of YOLOv5 to autonomously learn explicit and implicit knowledge for the purpose of feature focusing and redundancy removal. Finally, in the post-processing stage after the network prediction, we used Weighted Boxes Fusion (WBF) instead of the traditional NMS to achieve spatial scale fusion. We performed an experimental validation using the Air-to-Ground (A2G) dataset and mAP of YOLO-A2G reached 94%.
Face Shape Classification Based on Bilinear Network with Attention Mechanism

Abstract Face shape is an important information in face recognition, personalized recommendation and other applications. For example, rough face shape filtering before face recognition can effectively improve the recognition accuracy and speed. At the same time, an effective face shape classification can be used to construct a recommendation system for hairstyles and glasses. Therefore, this paper proposes a new face shape classification algorithm. Firstly, the M-RetinaFace network is proposed to align face image. Secondly, by combing the attention mechanism with the EfficientNet bilinear network, the AB-CNN network is proposed to extract feature. Finally, a bilinear pooling layer is used to classify face shape. Experiments on public data sets show that, compared with existing algorithms, the algorithm proposed in this paper is get state-of-the-art results and significantly improves the accuracy of face shape classification.
Analysis of Mobile Robot Control by Reinforcement Learning Algorithm

This work presents a Deep Reinforcement Learning algorithm to control a differentially driven mobile robot. This study seeks to explain the influence of different definitions of the environment with a mobile robot on the learning process. In our study, we focus on the Reinforcement Learning algorithm called Deep Deterministic Policy Gradient, which is applicable to continuous action problems. We investigate the effectiveness of different noises, inputs, and cost functions in the neural network learning process. To examine the feature of the presented algorithm, a number of simulations were run, and their results are presented. In the simulations, the mobile robot had to reach a target position in a way that minimizes distance error. Our goal was to optimize the learning process. By analyzing the results, we wanted to recommend a more efficient choice of input and cost functions for future research.
A Trusted Blockchain Based Traceability System for Fruits and Vegetable Agricultural Products

Blockchain is an arising computerized innovation permitting universal monetary exchanges among disseminated untrusted parties, without the need of middle people like banks. This article analyses the effect of blockchain innovation in agribusiness and food production network, presents existing continuous tasks and drives, and examines generally suggestions, difficulties and potential, with a basic view over the development of these activities. Our discoveries show that blockchain is a promising innovation towards a straightforward inventory network of food, with numerous continuous drives in different food items and food-related issues, yet numerous hindrances challenges actually exist, which frustrate its more extensive prevalence among ranchers and frameworks. These difficulties include specialized viewpoints, instruction, strategies and administrative structures.
F $F$‐norm two‐dimensional linear discriminant analysis and its application on face recognition

Two-dimensional linear discriminant analysis (2DLDA) is a widely applied extension of LDA that can cope with matrix input samples directly. However, its construction is based on a squared F $F$ -norm which will lead to sensitivity to noise and outliers. In this paper, a square-free F $F$ -norm 2DLDA is proposed to improve the robustness of 2DLDA. By losing the squared operation, the proposed method weakens the influence of outliers and noise and at the same time keeps the geometric structure of data. It can be solved through an effective nongreedy iterative algorithm, with each subproblem having a closed-form solution. The algorithm is further proved to be convergent. Experiments on several human face image databases demonstrate the effectiveness and robustness of the proposed method.
Fine-Grained Cross-Domain Recommendation via Two-Tier Attention and Three-Channel Learning

Cross-Domain Recommendation (CDR) algorithms, aimed at alleviating the long-standing data sparsity and cold-start problems by transferring information collected from the source domains to the target domains, have attracted increasing attention recently. Other than ratings, existing works on CDR mostly consider side information like tags, reviews, contents etc., yet cannot make full use of texts (i.e. reviews and contents etc.) efficiently or fuse these side information with ratings deeply. Inspired by the advantages shown in review-based recommendations and aspect-based ones, we propose to model fine-grained user preference transfer at aspect level. To achieve this goal, we propose an end2end CDR framework via aspect transfer network with two-tier attention and three-channel learning (named TATCL). TATCL is devised to extract aspects to represent each user or item from their reviews by a review encoder and a subsequent user/item encoder with two-tier attention, and learn accurate aspect correlations across domains with three-channel learning. In addition, we enhance the user and item representation with auxiliary reviews and item contents. Experimental results on datasets demonstrate that, under certain condition, the proposed TATCL has superior predictive performance than existing models in terms of rating prediction accuracy.
Binocular Images Dense Matching considering Image Adaptive Color Weights and Feature Points

When the matching cost function in Semiglobal Matching is unstable, the inaccurate matching cost values will be propagated in the cost aggregation process. It will lead to a serious mismatching phenomenon. To address the problem, a binocular images dense matching method considering image adaptive color weights and feature points was proposed. Firstly, The Color Birchfield Tomasi (CBT) matching cost calculation method was proposed to obtain a stable initial cost volume, which combined image adaptive color weights and gradient information. Secondly, the Scale-invariant Feature Transform matching algorithm was used to extract the a priori feature points from binocular images. Then, the feature points were filtrated. The cost volume was optimized by using their coordinate information and disparity information. Finally, an aggregation path segmentation rectification method was adopted to optimize the SGM aggregation paths and reduce the propagation of incorrect paths. Experimental results demonstrate that the proposed method can effectively improve the stability and accuracy of dense matching, reduce the mismatching phenomenon, and finally produce high-quality disparity maps.
Revisiting instance search: A new benchmark using cycle self-training

Instance search aims at retrieving a particular object instance from a set of scene images. Although studied in previous competitions like TRECVID, there have been limited literature or datasets on this topic. In this paper, to overcome the generalization issue when arbitrary categories are involved in search and to benefit from the large amount of unlabeled data, we propose a cycle self-training framework which trains the instance search pipeline with automatic supervision. Given the two-stage pipeline with a localization and ranking module, the cycle self-training includes a ranker-guided localizer, and a localizer-guided ranker, each carefully designed to handle noisy labels that come with self-supervision. Furthermore, we build and release large-scale groundtruth annotations for instances to facilitate the algorithm evaluation and analysis in this research topic, especially for small objects in complex background. The datasets are publicly available at https://github.com/instance-search/instance-search. Extensive experiments show the effectiveness of the proposed cycle self-training framework and the superior performance compared with other state-of-the-art methods.
Developing sequentially trained robust Punjabi speech recognition system under matched and mismatched conditions

Development of a native language robust ASR framework is very challenging as well as an active area of research. Although an urge for investigation of effective front-end as well as back-end approaches are required for tackling environment differences, large training complexity and inter-speaker variability in achieving success of a recognition system. In this paper, four front-end approaches: mel-frequency cepstral coefficients (MFCC), Gammatone frequency cepstral coefficients (GFCC), relative spectral-perceptual linear prediction (RASTA-PLP) and power-normalized cepstral coefficients (PNCC) have been investigated to generate unique and robust feature vectors at different SNR values. Furthermore, to handle the large training data complexity, parameter optimization has been performed with sequence-discriminative training techniques: maximum mutual information (MMI), minimum phone error (MPE), boosted-MMI (bMMI), and state-level minimum Bayes risk (sMBR). It has been demonstrated by selection of an optimal value of parameters using lattice generation, and adjustments of learning rates. In proposed framework, four different systems have been tested by analyzing various feature extraction approaches (with or without speaker normalization through Vocal Tract Length Normalization (VTLN) approach in test set) and classification strategy on with or without artificial extension of train dataset. To compare each system performance, true matched (adult train and test-S1, child train and test-S2) and mismatched (adult train and child test-S3, adult + child train and child test-S4) systems on large adult and very small Punjabi clean speech corpus have been demonstrated. Consequently, gender-based in-domain data augmented is used to moderate acoustic and phonetic variations throughout adult and children's speech under mismatched conditions. The experiment result shows that an effective framework developed on PNCC + VTLN front-end approach using TDNN-sMBR-based model through parameter optimization technique yields a relative improvement (RI) of 40.18%, 47.51%, and 49.87% in matched, mismatched and gender-based in-domain augmented system under typical clean and noisy conditions, respectively.
A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care

The annual incidence of head and neck cancers (HNC) worldwide is more than 550,000 cases, with around 300,000 deaths each year. However, the incidence rates and disease-characteristics of HNC differ between treatment centers and different populations, due to undetermined reasons, which may or not include socioeconomic factors. The multi-faceted and multi-variate nature of the data in the context of the emerging field of health disparities research makes automated analysis impractical. Hence, we present a visual analysis approach to explore the health disparities in the data of HNC patients from two different cohorts at two cancer care centers. Our approach integrates data from multiple sources, including census data and city data, with custom visual encodings and with a nearest neighbor approach. Our design, created in collaboration with oncology experts, makes it possible to analyze the patients' demographic, disease characteristics, treatments and outcomes, and to make significant comparisons of these two cohorts and of individual patients. We evaluate this approach through two case studies performed with domain experts. The results demonstrate that this visual analysis approach successfully accomplishes the goal of comparing two cohorts in terms of different significant factors, and can provide insights into the main source of health disparities between the two centers.
Patterns of Code-to-Test Co-evolution for Automated Test Suite Maintenance

Software systems are characterized by continual change which often occurs concurrently across various artifact types. While prior work has focused on the evolution of individual artifacts, this paper studies the patterns of co-evolution between source and test code. In this research, with a reference to the literature, as well as our manual analysis of several open-source software systems we first, patternize and document common patterns of co-evolution between source code and test suites. Leveraging the proposed patterns, we further infer the necessary remedies in the test suite in response to source code changes. Our approach enables to add missing test cases to the current version of a system (augmentation), but additionally allows to reuse and evolve the existing test suite for a modified version of the system (evolution). Furthermore, identifying patterns of concurrent evolution provides opportunities for a bi-directional change detection and remediation for both artifacts, source code and test cases, and additionally automates the process of maintaining code-to-test trace links. The evaluation of the patterns and remedies in five large open-source applications indicated the patterns contained up to 42% of the source code changes and the remediation recovered up to 100% of the impacted test cases in certain cases.
The cybersecurity risk estimation engine: A tool for possibility based risk analysis

Despite dramatic changes in the constellation of cybersecurity risks, the basic approach to risk calculation has been anchored to probability theory. The probability approach is widely known and conceptually simple. But it is disadvantageous in its grounding on expert estimates of frequency data which is often publicly unavailable. This study proposes the use of possibility theory as a complementary grounding for cybersecurity risk calculations. Using a design science research approach, we use possibility theory as the kernel theory in developing and evaluating a practical possibility-based risk estimation prototype. The results offer an expanded grounding to improve cybersecurity risk analysis.
Construction of English and American Literature Corpus Based on Machine Learning Algorithm

In China, the application of corpus in language teaching, especially in English and American literature teaching, is still in the preliminary research stage, and there are various shortcomings, which have not been paid due attention by front-line educators. Constructing English and American literature corpus according to certain principles can effectively promote English and American literature teaching. The research of this paper is devoted to how to automatically build a corpus of English and American literature. In the process of keyword extraction, key phrases and keywords are effectively combined. The similarity between atomic events is calculated by the TextRank algorithm, and then the first N sentences with high similarity are selected and sorted. Based on ML (machine learning) text classification method, a combined classifier based on SVM (support vector machine) and NB (Naive Bayes) is proposed. The experimental results show that, from the point of view of accuracy and recall, the classification effect of the combined algorithm proposed in this paper is the best among the three methods. The best classification results of accuracy, recall, and F value are 0.87, 0.9, and 0.89, respectively. Experimental results show that this method can quickly, accurately, and persistently obtain high-quality bilingual mixed web pages.
Network Alignment enhanced via modeling heterogeneity of anchor nodes

Network Alignment (NA), which aims to find the nodes that represent the same entity (i.e., anchor nodes) across different networks, is a fundamental problem in many cross-network researches. Recent advances in network embedding have inspired various auspicious approaches for addressing the NA task, and embedding-based NA technology has become the main research trend. Most embedding-based NA methods follow the consistency assumption explicitly or implicitly, where anchor nodes across different networks tend to have similar local structures/neighbors. However, through the detailed statistical analysis across networks, we observe that anchor nodes have high heterogeneity, i.e., they have different local structures across different networks. Hence, in this paper, we present the formal definition of the heterogeneity of anchor nodes and propose a network alignment framework that combines heterogeneity, which can simultaneously consider the case of heterogeneity and consistency of anchor nodes. In our approach, we propose to use a variational autoencoder to learn node embeddings, and design an effective dual constraint mechanism–Laplacian regularization and heterogeneity constraint to balance the consistency and heterogeneity for network alignment across different networks respectively. Finally, to verify the effectiveness of our proposed method, we conduct extensive experiments on several real-world datasets. Experimental results show that the proposed model achieves better performance than state-of-the-art methods.
Designing and Implementing a Terminal Platform to Manage and Detect the Health of Exercise Parameters Based on Internet Objects

In order to realize the research on vital signs monitoring based on the Internet of things, in view of the urgent needs for mobile health and universal medical treatment in China, this paper puts forward the platform design and implementation of exercise parameter health management and detection terminal based on the Internet of things. Firstly, the overall architecture of the exercise heart rate monitoring system is designed, and the design of the heart rate acquisition module is also put forward, in order to monitor human heart rate data conveniently, quickly, and in real time, considering the portability and low power consumption of the system, a wearable heart rate real-time monitoring system is designed in this paper. The experimental results show that the maximum individual average heart rate difference is 4.11 bpm and the overall average heart rate difference is 2.24 bpm. After processing with the adaptive hybrid filtering algorithm, the maximum individual average heart rate difference is 0.69 bpm, and the overall average heart rate difference is 0.42 bpm. The average standard deviation of the difference between the 10 groups of measured values and reference values with and without the filtering algorithm is 0.304 and 2.788, respectively, which is about 9 times higher. The accuracy and stability of the filtering algorithm are greatly improved. The whole system can run well and can be applied to practical sports training guidance.
Automatic Scoring of English Essays Based on Machine Learning Technology in a Wireless Network Environment

The English composition is an important indicator of English learners’ overall language skills and is asked in large-scale English examinations, both in China’s college entrance examinations and graduate examinations and in the TOEFL, GRE, and IELTS examinations in Europe and the United States. Some automatic scoring systems for English writing have been created in the United States and internationally, however the systems still have issues with generalization, accuracy, and error correction. In this paper, we present a method to improve the accuracy of existing automatic composition scoring systems through deep learning techniques in a wireless network environment. Experiments reveal that the method can accurately assess the quality of English learners’ writings, paving the way for the creation of an automated composition scoring system for large-scale machine testing and web-based self-learning platforms.