Reviewerly/exHarmony huggingface dataset:
Breast Cancer Prognosis using Machine Learning Applications

The maximum deaths worldwide among women is breast cancer (BC). Breast cancer that mainly develops from breast tissues. Due to current population growth in medical research, early diagnosis of cancer has become a critical issue. The likelihood of death is increasing exponentially due to breast cancer as the world's population grows. Breast cancer is the second leading severe cancers that have already been revealed. Cancer prediction therefore plays an important role in updating treatment aspects and survivability standards. As a result of machine learning methods, breast cancer prediction and early detection have been improved significantly, and these methods have been a center of research for this field. Using the Wisconsin Diagnostic Breast Cancer dataset (WBCD), 5 ML algorithms were compared: Decision tree (C4.5), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and K-Nearest Neighbour (KNN). Machine learning algorithms are being used to identify the most effective algorithms for early detection and identifying breast cancer in its earliest stages.
An Illicit Bitcoin Address Analysis Scheme Based on Subgraph Evolution

Bitcoin, due to its decentralization and anonymity, is more frequently utilized in cybercrimes such as money laundering, darknet market, and blackmail. In order to maintain the development of cryptocurrency communities, it is vital to fight against these cryptocurrency-related (e.g., Bitcoin) cybercrimes. To better monitor cryptocurrency-based cybercrime, it is essential for a deeper understanding of the patterns that exist in Bitcoin transactions. In this paper, we propose a novel framework consisting of empirical analysis and machine learning-based analysis for studying three typical categories of Bitcoin addresses from a dynamic perspective of subgraph evolution. Specifically, we collect 22,001 continuous blocks to build a Bitcoin transaction graph and extract subgraphs from the constructed graph for each address in different periods. In the empirical analysis, we have several important findings from three perspectives. While in the machine learning-based analysis, we propose a 110-dimensional feature set for classification tasks among these three types of Bitcoin addresses, where decision tree, k-nearest neighbor algorithm, and random forest are applied. The experimental results illustrate that metrics of all classifiers can achieve 0.88, and the highest accuracy rate reaches 0.95 in the random forest classifier. In addition, we explore the feature importance in the random forest using the SHAP library and discuss several interesting phenomena that appear in the evolution process of the subgraphs.
SG-Net: A Super-resolution Guided Network for Improving Thyroid Nodule Segmentation

Ultrasound thyroid nodules segmentation is a tough task because of the speckle noise, intensity heterogeneity and low contrast. In order to increase the accuracy of thyroid nodules segmentation, we present a novel framework named SG-Net. Different from other methods, we divide the segmentation problem of thyroid nodules into two steps to solve. First, a super-resolution reconstruction network is utilised to reconstruct a high-resolution version input and suppress the noise. In the second step, our proposed network perform the segmentation task. Our segmentation network includes a multi-scale input layer, several atrous spatial pyramid pooling(ASPP) modules, a U-shape convolutional backbone network with attention blocks and a our designed parallel atrous convolution(PAC) module. The multi-scale input layer paired with the ASPP module sample the input image in parallel with atrous convolutions with different dilation rates, which is beneficial to capture useful contextual information at multiple scales. The attention blocks can propagate information between the high-low level layers so that semantic features can be fully utilized for lesion segmentation. Last but not least, our proposed PAC block is beneficial to further improve the segmentation by extracting high-level semantic features from different receptive fields. Our experiments show that the Dice value of our method reaches 91.7%, and the mIoU value reaches 86.8%, which is better than most popular methods.
Exploring TensorRT to Improve Real-Time Inference for Deep Learning

Deep learning (DL) has dramatically evolved and become one of the most successful machine learning techniques. A variety of DL-enabled applications have been widely integrated into software systems, including embedded ones. Although having achieved very successful results in accuracy, the large size of deep neural networks could require significant runtime and computing resource consumption. To overcome these drawbacks, TensorRT has been developed and may be incorporated into popular DL frameworks such as PyTorch and Open Neural Network Exchange (ONNX). In this paper, focusing on inference, we provide a comprehensive evaluation on the performances of TensorRT. Specifically, we evaluate inference output validation, inference time, inference throughput, and GPU memory usage. Our results demonstrate that TensorRT is able to significantly improve the inference efficiency metrics without compromising inference accuracy. Furthermore, TensorRT may be adopted via several alternative workflows. Our evaluation also shows the pros and cons of these TensorRT workflows. We analyze that for each workflow and discuss the workflow selection for different application scenarios.
Semantic Knowledge Guided Class-Incremental Learning

Driven by practical needs, research on Class-Incremental Learning (CIL) has received more and more attentions in recent years. A technical challenge to be conquered by CIL methods is the catastrophic forgetting problem, where the model’s performance improves rapidly on new classes while deteriorates drastically on old ones. The main causes behind catastrophic forgetting include network drifts, inter-class confusions, etc. In this paper, we propose a novel CIL method that solves the catastrophic forgetting problem from two aspects. First, to solve the inter-class confusion problem, we propose a novel Semantic knOwledge gUided ciL framework (SOUL) that consists of a CNN feature extractor and a Bi-GCN (Graph Convolutional Network) classifier. In each CIL session, we use the semantic knowledge extracted from the class labels to build two inter-class relation graphs among all the encountered old and new classes. Using these two relation graphs, we develop a Bi-GCN classifier to fuse two kinds of semantic relations in a balanced way, and then to transfer the inter-class relations from semantic modality to image classification weights. The entire SOUL framework is trained end-to-end by the standard BP algorithm, which optimizes the Bi-GCN classifier and the CNN feature extractor jointly. Second, to prevent the network drift, we develop the local topology preserving strategy that divides the global topological structure of the learned feature space into a set of local topological relations, and maintains these local relations at CIL session. Experimental evaluations demonstrate the state-of-the-art performance accuracies on benchmark image classification datasets.
Animal-centered design needs dignity: a critical essay on ACI’s core concept

Despite a massive acceptance of ‘animal-centered design’ being at the very heart of Animal-Computer Interaction, exactly what it means to be animal-centered often remains vague. In this position paper, I question and critique what animal-centered design really means as it is used. I argue that even though the ACI manifesto and subsequent foundational works clearly set out a focus on animal user-centered design, much work since has adopted ‘animal-centered’ as being a synonym for ‘animal user-centered’. However, I argue, the fundamental essence of ACI’s intellectual origins in human-centered design’s preoccupation with human values, and in turn, human dignity – which set it apart from mere user-centered design, is lost in such a straightforward adoption of the term. I then analyze what it might mean to actually adopt a value-driven approach akin to human-centered design for animal-centered design, and how this might force us to move beyond the typical welfarist position dominant across most of ACI. Rather than consider the prevention of unnecessary suffering as a central goal of technologies developed by ACI researchers, I argue that technologies that preserve animal dignity as a core value is a more appropriate understanding of the term ‘animal-centered’.
Evaluating the Design of an Art Student Framework Supporting XR Exhibitions

A successful system design may lead to a successfully implemented system. This paper demonstrates the followed evaluation procedure for the creation of a system dedicated to art schools. Specifically, we focus on meeting the needs of art teachers, art students, and visitors who are the stakeholders of such a system. Art teachers need to be able to initiate art exhibitions for their students and assess them, art students to create virtual exhibitions, and art exhibition visitors to have immersive and interactive experiences. This evaluation is a part of an EU-funded research project, namely CREAMS which aims to design, develop end evaluate a framework and open-source tools based on virtual, augmented, and mixed reality for the creation of virtual exhibitions for art students. Here, we present the followed user-centered design methodology that we followed to reach the first system design. The methodology begins with the literature review and continues with the filtering of review findings by experts, the creation of the corresponding mockups, the evaluation of the mockups by the end users, the specification of quantitative and qualitative metrics on employing XR technologies in Higher Education Institutions and concludes with the identification of a set of specifications that drives the development of a specific conceptual system design.
A Comparative Evaluation of Chatbot Development Platforms

Chatbots and virtual assistants have become part of people's everyday life. The need for mass production of these services rapidly and efficiently has created an explosion of software-related services focused on developing chatbots. Big companies like Google, Microsoft, Amazon, and IBM offer complete Chatbot Development Platforms and compete with each other. Our effort is to help people interested in using these platforms decide which is the best CDP for their case. Similar attempts have happened but are now outdated as CDPs have introduced breaking changes. We study each CDP, define criteria and calculate scores based on requirement assumptions. In parallel, we observe how innovations in NLP are presented in the market through CDPs.
Cascading CNNs with S-DQN: A Parameter-Parsimonious Strategy for 3D Hand Pose Estimation

This paper proposes a cascaded parameter-parsimonious 3D hand pose estimation strategy to improve real-time performance without sacrificing accuracy. The estimation process is first decomposed into feature extraction and feature exploitation. The feature extraction is seen as a dimension reduction process, where convolutional neural networks (CNNs) are used to ensure accuracy. Feature exploitation is considered as a policy optimization process, and a shallow reinforcement learning (RL)-based feature exploitation module is proposed to improve running rapidity. Ablation studies and experiments are carried out on NYU and ICVL datasets to evaluate the performance of the strategy, and multiple baselines are used to evaluate generalization. The results show that the improvement on testing time reaches 8.1 $$\%$$ and 14.6 $$\%$$ by the proposed strategy. Note that the overall accuracy also reaches state-of-the-art, which further shows the effectiveness of the proposed strategy.
VERGE in VBS 2023

This paper describes VERGE, an interactive video retrieval system for browsing a collection of images from videos and searching for specific content. The system utilizes many retrieval techniques as well as fusion and reranking capabilities. A Web Application is also part of VERGE, where a user can create queries, view the top results and submit the appropriate data, all in a user-friendly way.
QIVISE: A Quantum-Inspired Interactive Video Search Engine in VBS2023

In this paper, we present a quantum-inspired interactive video search engine (QIVISE), which will be tested in VBS2023. QIVISE aims at assisting the user in dealing with Known-Item Search and Ad-hoc Video Search tasks with high efficiency and accuracy. QIVISE is based on a text-image encoder to achieve multi-modal embedding and introduces multiple interaction possibilities, including a novel quantum-inspired interaction on paradigm, label search, and multi-modal search to refine the retrieval results via user’s interaction and feedback.
Cloth-Changing Person Re-Identification With Noisy Patch Filtering

Cloth-changing person re-identification (ReID) aims to recognize the identities of persons even when changing cloth. Even with the same person, changes in clothing can cause significant visual variations, making it challenging to identify them. Therefore, there is a need for a technique that can extract their inherent characteristics while being invariant to changes in clothing. Recent studies have utilized additional information such as gait, body parsing maps, and 3D shape to address cloth variance. However, these methods require additional processing and their effectiveness is dependent on the quality of the information provided. In this letter, we propose a two-stream model for ReID based on a single RGB image, consisting of a baseline ReID network and cloth-unrelated ReID networks. The baseline network takes the full RGB image as input, while the cloth-unrelated networks only receive cropped patches of the RGB image that include only cloth-independent identity cues such as the face and legs. We also propose a noisy patch filtering module (NPFM) to remove interference from noise patches during training. Finally, we combine features from baseline and cloth-unrelated ReID networks to perform cloth-changing person ReID. Our approach achieves a significant improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+7.4\%$</tex-math></inline-formula> for Rank-1 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+1.7\%$</tex-math></inline-formula> for mAP in cloth-changing environments compared to the recent state-of-the-art method. The effectiveness of our model for the cloth-changing person ReID is verified on the benchmark dataset with various ablation studies. In summary, our method can extract the unique features of an individual person in a cloth-changing environment and is robustly trained even with occluded or poorly captured data. Therefore, it can be more versatile in practical environments than existing ReID methods.
Reinforcement Learning Enhanced PicHunter for Interactive Search

With the tremendous increase in video data size, search performance could be impacted significantly. Specifically, in an interactive system, a real-time system allows a user to browse, search and refine a query. Without a speedy system quickly, the main ingredient to engage a user to stay focused, an interactive system becomes less effective even with a sophisticated deep learning system. This paper addresses this challenge by leveraging approximate search, Bayesian inference, and reinforcement learning. For approximate search, we apply a hierarchical navigable small world, which is an efficient approximate nearest neighbor search algorithm. To quickly prune the search scope, we integrate PicHunter, one of the most popular engines in Video Browser Showdown, with reinforcement learning. The integration enhances PicHunter with the ability of systematic planning. Specifically, PicHunter performs a Bayesian update with a greedy strategy to select a small number of candidates for display. With reinforcement learning, the greedy strategy is replaced with a policy network that learns to select candidates that will result in the minimum number of user iterations, which is analytically defined by a reward function. With these improvements, the interactive system only searches a subset of video datasets relevant to a query while being able to quickly perform Bayesian updates with systematic planning to recommend the most probable candidates that can potentially lead to minimum iteration rounds.
Faster Convergence with Lexicase Selection in Tree-Based Automated Machine Learning

In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution. In this paper, we present a study comparing the role of two commonly used parent selection methods in evolving machine learning pipelines in an automated machine learning system called Tree-based Pipeline Optimization Tool (TPOT). Specifically, we demonstrate, using experiments on multiple datasets, that lexicase selection leads to significantly faster convergence as compared to NSGA-II in TPOT. We also compare the exploration of parts of the search space by these selection methods using a trie data structure that contains information about the pipelines explored in a particular run.
Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming

Denoising Autoencoder Genetic Programming (DAE-GP) is a model-based evolutionary algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of genetic programming (GP). In this paper, we use the DAE-GP to solve a set of nine standard real-world symbolic regression tasks. We compare the prediction quality of the DAE-GP to standard GP, geometric semantic GP (GSGP), and the gene-pool optimal mixing evolutionary algorithm for GP (GOMEA-GP), and find that the DAE-GP shows similar prediction quality using a much lower number of fitness evaluations than GSGP or GOMEA-GP. In addition, the DAE-GP consistently finds small solutions. The best candidate solutions of the DAE-GP are 69% smaller (median number of nodes) than the best candidate solutions found by standard GP. An analysis of the bias of the selection and variation step for both the DAE-GP and standard GP gives insight into why differences in solution size exist: the strong increase in solution size for standard GP is a result of both selection and variation bias. The results highlight that learning and sampling from a probabilistic model is a promising alternative to classic GP variation operators where the DAE-GP is able to generate small solutions for real-world symbolic regression tasks.
A Spatial–Spectral Transformer Network With Total Variation Loss for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising has an essential effect on HSI analysis and interpretation. In recent years, denoising methods based on convolutional neural networks (CNNs) have made great progress. However, the convolution kernel in the CNN model is content-independent, and the ability to capture long-distance correlation is weak, which leads to spectral distortion and edge blurring. To address this problem, we propose a spatial–spectral transformer network for HSI denoising, which introduces the shifted window-based transformer method to denoise HSIs by modeling image content correlation while preserving the local inductive bias. In detail, to jointly explore the spatial–spectral features, we first formulate the spatial–spectral cubes as network input, which are composed of the current band and its adjacent fixed K-bands. Second, these spatial–spectral cubes are forwarded to cascaded hyperspectral transformer blocks (HTBs) with skip connection for deep feature extraction. The HTB contains multiple transformer layers based on different window partitioning, which not only reduces memory cost, but also enhances the feature extraction capability. Finally, for the denoised <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> spatial–spectral cubes, we average the pixels of overlapping spectral bands to generate a complete HSI. Furthermore, we introduce the total variation (TV) to preserve the smoothness structures of HSIs. The experimental results on simulated and real data indicate that the proposed spatial-spectral transformer denoising (SSTD) is superior to other mainstream learning-based HSI denoising algorithms.
A Boosting Approach to Constructing an Ensemble Stack

An approach to evolutionary ensemble learning for classification is proposed using genetic programming in which boosting is used to construct a stack of programs. Each application of boosting identifies a single champion and a residual dataset, i.e. the training records that thus far were not correctly classified. The next program is only trained against the residual, with the process iterating until some maximum ensemble size or no further residual remains. Training against a residual dataset actively reduces the cost of training. Deploying the ensemble as a stack also means that only one classifier might be necessary to make a prediction, so improving interpretability. Benchmarking studies are conducted to illustrate competitiveness with the prediction accuracy of current state-of-the-art evolutionary ensemble learning algorithms, while providing solutions that are orders of magnitude simpler. Further benchmarking with a high cardinality dataset indicates that the proposed method is also more accurate and efficient than XGBoost.
Social Media Safety Practices and Flagging Sensitive Posts

Today, social media has uniquely become a force for positive change, community building, and sharing ideas. But with millions of people, of all ages, tuned into social media many hazards and mishaps can arise due to inadequate content monitoring. Therefore, our research problem deals with social media safety practices, flagging sensitive posts, and effective content monitoring. In this paper, an examination will be made of the most impactful hazards and mishaps that arise from poor social media content monitoring, incorrect flagging, public safety, and the spread of misinformation. In addition, this paper will discuss the shortcomings of content monitoring tools currently on the market and how they can be improved. Finally, this paper goes over the findings and areas of research related to the University of Texas Dallas 2022 Software Safety REU program. Through the synthesis and culmination of exploring social media safety practices, we have curated an application to manually label social media posts according to a pre-established corpus of violent phrases.
EVMFFR: Electronic Voting Machine with Fingerprint and Facial Recognition

An Internet of Things (IoT) based Electronic Voting Machine with Fingerprint and Facial Recognition (EVMFFR) with duplicate voting avoidance is proposed, designed and implemented. The designed EVMFFR system uses dual verification techniques with better results comparing to the existing single verification systems. Using raspberry, fingerprint and facial recognition methods are integrated and used for the identification of voter in the process of election. A biometric template is developed using the rectangular Haar transform during the registration of a new voter which is then used for verification and validation. The features in the developed biometric template is compared with the Eigen vectors of captured biometric of the voters using cascaded machine learning techniques such as Generalized Principal Component Analysis (GPCA) and C-Nearest Neighbor (CNN). The results of the proposed EVMFFR system shows that the integrated design performs better than the existing EVMs. The proposed EVMFFR system performs with an accuracy of more than 90% under day light for facial recognition at comparatively low cost per EVMFFR unit.