Reviewerly/exHarmony huggingface dataset:
Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks

Early detection remains the backbone of breast cancer control and treatment improvement. However, early detection is difficult since cancer symptoms are absent at onset. Hence, cancer remains one of the health topics that many researchers try to improve from diagnosis, prevention, and treatment perspectives. The main aim of this research is to develop a system using deep learning to classify breast lesions in mammographic images into malignant and nonmalignant based on two approaches, one by using patches of region of interest (ROI) and the other using the whole images. The proposed system comprises two phases, which are the preprocessing phase and the Convolution Neural Network (CNN) building phase. The preprocessing phase prepares the mammogram images for the CNN building phase, it includes format unification, noise removal, image enhancement, ROI extraction, augmentation, and image resizing. The CNN building phase builds the proposed CNN model from scratch to learn features and classify the breast lesions in mammogram images. The proposed system offers good classification rates. It was applied on three benchmark datasets, which are the MIAS, DDSM, and INbreast. Using 5-fold cross validation, experimental results demonstrated high performance and promising results. The sensitivity, specificity, accuracy and AUC reached 96.55%, 96.49%, 96.52%, and 0.98 respectively for the INbreast dataset, and reached 98%, 92.6%, 95.3%, and 0.974 respectively for the MIAS dataset.
Identifying the response factors in the formation of a sense of presence and a destination image from a 360-degree virtual tour

There is a trend for destination marketers to create a 360-degree virtual video to promote their tourist spots. This study examines how the response factors to a 360-degree virtual tour develop a sense of presence and form a destination image. Data were collected from 375 individuals after they had been sightseeing with a 360-degree virtual video of Dinghu Mountain in China. The results of the survey indicate that the viewers' mental imagery and engagement in response to the video influence the formation of a sense of presence, a cognitive image, and an affective image of the destination. Although the immersion in response influences the formation of a sense of presence and a cognitive image, it does not show a significant effect on the affective image. Moreover, the sense of presence, the cognitive image, and the affective image developed through the 360-degree virtual tour affect the viewers' feelings towards the destination. This study contributes to an understanding of the mechanism of the formation of the destination image from the psychological responses to this ‘visiting’ experience. It also provides destination marketers with valuable information regarding the design of 360-degree virtual tours for target marketing. • Immersion, mental imagery, and engagement are response factors of 360 tours. • These three factors influence the generation of a sense of presence. • These three factors influence the formation of the cognitive destination image. • Only mental imagery and engagement influence affective destination image. • Presence, cognitive image, and affective image influence overall destination image.
Triple Adversarial Learning and Multi-View Imaginative Reasoning for Unsupervised Domain Adaptation Person Re-Identification

Due to the importance of practical applications, unsupervised domain adaptation (UDA) person re-identification (re-ID) has attracted increasing attention. However, most of existing methods often lack the multi-view information reasoning and ignore the domain discrepancy of the pedestrian images with the same identity, which constrain the further improvement of recognition performance. So, this paper proposes a triple adversarial learning and multi-view imaginative reasoning network (TAL-MIRN) for UDA person re-ID, which consists of a multi-view imaginative reasoning module (IRM) and a triple adversarial learning module (TALM). IRM makes the classified pedestrian identity features from a single-view image extracted by a feature encoder consistent with the classification results of the aggregated multi-view pedestrian identity features, so the strong multi-view imaginative reasoning ability of the feature encoder is obtained. TALM is composed by the adversarial learning between the camera classifier and feature encoder, adversarial learning of joint distribution alignment, and adversarial learning of the difference between two classifiers used in classification. In particular, the domain-invariant features at camera level are guaranteed by the adversarial learning between the feature extractor and camera classifier. The joint alignment of identity and domain is achieved by the competition between the feature extractor and classifier integrated with identity and domain. The discriminability and robustness of the learned features are enhanced by playing a MinMax game between two different identity classifiers. Furthermore, a simple normalization operation named as cross normalization (CN) is proposed to increase both modeling and generalization capability of the proposed TAL-MIRN across multiple domains. The proposed TAL-MIRN is applied to five benchmark datasets, and the comparative experimental results confirm its superiority over the state-of-the-art methods. The related source codes is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lhf12278/TALM-IRM</uri> .
Un-Compromised Credibility: Social Media Based Multi-Class Hate Speech Classification for Text

There is an enormous growth of social media which fully promotes freedom of expression through its anonymity feature. Freedom of expression is a human right but hate speech towards a person or group based on race, caste, religion, ethnic or national origin, sex, disability, gender identity, etc. is an abuse of this sovereignty. It seriously promotes violence or hate crimes and creates an imbalance in society by damaging peace, credibility, and human rights, etc. Detecting hate speech in social media discourse is quite essential but a complex task. There are different challenges related to appropriate and social media-specific dataset availability and its high-performing supervised classifier for text-based hate speech detection. These issues are addressed in this study, which includes the availability of social media-specific broad and balanced dataset, with multi-class labels and its respective automatic classifier, a dataset with language subtleties, dataset labeled under a comprehensive definition and well-defined rules, dataset labeled with the strong agreement of annotators, etc. Addressing different categories of hate separately, this paper aims to accurately predict their different forms, by exploring a group of text mining features. Two distinct groups of features are explored for problem suitability. These are baseline features and self-discovered/new features. Baseline features include the most commonly used effective features of related studies. Exploration found a few of them, like character and word n-grams, dependency tuples, sentiment scores, and count of 1st, 2nd person pronouns are more efficient than others. Due to the application of latent semantic analysis (LSA) for dimensionality reduction, this problem is benefited from the utilization of many complex and non-linear models and CAT Boost performed best. The proposed model is compared with related studies in addition to system baseline models. The results produced by the proposed model were much appreciating.
Secure biometric authentication with de-duplication on distributed cloud storage

Cloud computing is one of the evolving fields of technology, which allows storage, access of data, programs, and their execution over the internet with offering a variety of information related services. With cloud information services, it is essential for information to be saved securely and to be distributed safely across numerous users. Cloud information storage has suffered from issues related to information integrity, data security, and information access by unauthenticated users. The distribution and storage of data among several users are highly scalable and cost-efficient but results in data redundancy and security issues. In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security. The proposed method is compared with existing algorithms, such as convergent encryption (CE), leakage resilient (LR), randomized convergent encryption (RCE), secure de-duplication scheme (SDS), to evaluate the de-duplication performance. Our comparative analysis shows that our proposed scheme results in smaller computation and communication costs than existing schemes.
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance. Demonstration videos are available at https://caipeide.github.io/autorace-dirl/.
Transformer3D-Det: Improving 3D Object Detection by Vote Refinement

Voting-based methods (e.g., VoteNet) have achieved promising results for 3D object detection. However, the simple voting operation in VoteNet may lead to less accurate voting results that are far away from the true object centers. In this work, we propose a simple but effective 3D object detection method called Transformer3D-Det (T3D), in which we additionally introduce a transformer based vote refinement module to refine the voting results of VoteNet and can thus significantly improve the 3D object detection performance. Specifically, our T3D framework consists of three modules: a vote generation module, a vote refinement module, and a bounding box generation module. Given an input point cloud, we first utilize the vote generation module to generate multiple coarse vote clusters. Then, the clustered coarse votes will be refined by using our transformer based vote refinement module to produce more accurate and meaningful votes. Finally, the bounding box generation module takes the refined vote clusters as the input and generates the final detection result for the input point cloud. To alleviate the impact of inaccurate votes, we also propose a new non-vote loss function to train our T3D. As a result, our T3D framework can achieve better 3D object detection performance. Comprehensive experiments on two benchmark datasets ScanNetV2 and SUN RGB-D demonstrate the effectiveness of our T3D framework for 3D object detection.
Machine learning based approach to exam cheating detection

The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students’ continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments.
Optimal deep learning based fusion model for biomedical image classification

Abstract Automated examination of biomedical signals plays a vital role to diagnose diseases and offers useful data to several applications in the areas of physiology, sports medicine, and human–computer interface. The latest advancements in Artificial Intelligence (AI) have the ability to manage and analyse enormous biomedical datasets resulting in clinical decision making and real time applications. At the same time, Colorectal cancer (CRC) is the third most deadly disease affecting people over the globe. The utilization of AI techniques for the earlier identification of CRC has gained significant interest among the research communities. Therefore, this paper presents a novel AI based fusion model for CRC disease diagnosis and classification, named AIFM‐CRC. The presented AIFM‐CRC model primarily undergoes Gaussian filtering based noise removal and contrast enhancement as a preprocessing stage. In addition, a fusion based feature extraction process takes place where the SIFT based handcrafted features and Inception v4 based deep features are fused together. Besides, whale optimization algorithm tuned deep support vector machine model is employed as a classification technique to determine the existence of CRC. In order to highlight the proficient results analysis of the AIFM‐CRC model, a comprehensive simulation analysis takes place. The resultant experimental values pointed out the betterment of the AIFM‐CRC model by accomplishing a maximum accuracy of 96.18%.
FD-SSD: An improved SSD object detection algorithm based on feature fusion and dilated convolution

Objects that occupy a small portion of an image or a frame contain fewer pixels and contains less information. This makes small object detection a challenging task in computer vision. In this paper, an improved Single Shot multi-box Detector based on feature fusion and dilated convolution (FD-SSD) is proposed to solve the problem that small objects are difficult to detect. The proposed network uses VGG-16 as the backbone network, which mainly includes a multi-layer feature fusion module and a multi-branch residual dilated convolution module. In the multi-layer feature fusion module, the last two layers of the feature map are up-sampled, and then they are concatenated at the channel level with the shallow feature map to enhance the semantic information of the shallow feature map. In the multi-branch residual dilated convolution module, three dilated convolutions with different dilated ratios based on the residual network are combined to obtain the multi-scale context information of the feature without losing the original resolution of the feature map. In addition, deformable convolution is added to each detection layer to better adapt to the shape of small objects. The proposed FD-SSD achieved 79.1% mAP and 29.7% mAP on PASCAL VOC2007 dataset and MS COCO dataset respectively. Experimental results show that FD-SSD can effectively improve the utilization of multi-scale information of small objects, thus significantly improve the effect of the small object detection.
Performances of K-Means Clustering Algorithm with Different Distance Metrics

Clustering is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where... | Find, read and cite all the research you need on Tech Science Press
Planning with Learned Entity Prompts for Abstractive Summarization

Abstract We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries with entity chains—ordered sequences of entities mentioned in the summary. Transformer-based sequence-to-sequence models are then trained to generate the entity chain and then continue generating the summary conditioned on the entity chain and the input. We experimented with both pretraining and finetuning with this content planning objective. When evaluated on CNN/DailyMail, XSum, SAMSum, and BillSum, we demonstrate empirically that the grounded generation with the planning objective improves entity specificity and planning in summaries for all datasets, and achieves state-of-the-art performance on XSum and SAMSum in terms of rouge. Moreover, we demonstrate empirically that planning with entity chains provides a mechanism to control hallucinations in abstractive summaries. By prompting the decoder with a modified content plan that drops hallucinated entities, we outperform state-of-the-art approaches for faithfulness when evaluated automatically and by humans.
Binocular Mutual Learning for Improving Few-shot Classification

Most of the few-shot learning methods learn to transfer knowledge from datasets with abundant labeled data (i.e., the base set). From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic manner to train meta-tasks within few classes in a local view. However, the interaction of the two views is rarely explored. As the two views capture complementary information, we naturally think of the compatibility of them for achieving further performance gains. Inspired by the mutual learning paradigm and binocular parallax, we propose a unified framework, namely Binocular Mutual Learning (BML), which achieves the compatibility of the global view and the local view through both intraview and cross-view modeling. Concretely, the global view learns in the whole class space to capture rich inter-class relationships. Meanwhile, the local view learns in the local class space within each episode, focusing on matching positive pairs correctly. In addition, cross-view mutual interaction further promotes the collaborative learning and the implicit exploration of useful knowledge from each other. During meta-test, binocular embeddings are aggregated together to support decision-making, which greatly improve the accuracy of classification. Extensive experiments conducted on multiple benchmarks including cross-domain validation confirm the effectiveness of our method <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
COMPLEX DYNAMIC SYSTEMS THEORY IN LANGUAGE LEARNING

Abstract A quarter of a century has passed since complex dynamic systems theory was proposed as an alternative paradigm to rethink and reexamine some of the main questions and phenomena in applied linguistics and language learning. In this article, we report a scoping review of the heterogenous body of research adopting this framework. We analyzed 158 reports satisfying our inclusion criteria (89 journal articles and 69 dissertations) for methodological characteristics and substantive contributions. We first highlight methodological trends in the report pool using a framework for dynamic method integration at the levels of study aim, unit of analysis, and choice of method. We then survey the main substantive contribution this body of research has made to the field. Finally, examination of study quality in these reports revealed a number of potential areas of improvement. We synthesize these insights in what we call the “nine tenets” of complex dynamic systems theory research, which we hope will help enhance the methodological rigor and the substantive contribution of future research.
Classification of White blood cell using Convolution Neural Network

The human immune system consists of White Blood Cells that are responsible for fighting of disease pathogens. In the field of medical imagining, white blood cells is of great importance. Analysis of white blood cells can be helpful to medical experts in many of the cases such as viral infection or cancer infection. In this paper, the classification of White Blood Cell using a Convolution Neural Network (CNN) is proposed. The proposed approach is able to classify the type of cell in much less epochs/time than other approaches. The performance of the proposed approach is evaluated on Kaggle dataset. The overall accuracy obtained from the proposed approach is 98.55%.
Internet of Things Security: Challenges and Key Issues

Internet of Things (IoT) refers to a vast network that provides an interconnection between various objects and intelligent devices. The three important components of IoT are sensing, processing, and transmission of data. Nowadays, the new IoT technology is used in many different sectors, including the domestic, healthcare, telecommunications, environment, industry, construction, water management, and energy. IoT technology, involving the usage of embedded devices, differs from computers, laptops, and mobile devices. Due to exchanging personal data generated by sensors and the possibility of combining both real and virtual worlds, security is becoming crucial for IoT systems. Furthermore, IoT requires lightweight encryption techniques. Therefore, the goal of this paper is to identify the security challenges and key issues that are likely to arise in the IoT environment in order to guide authentication techniques to achieve a secure IoT service.