Reviewerly/exHarmony huggingface dataset:
Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models

The interpretation of deep neural networks (DNNs) has become a key topic as more and more people apply them to solve various problems and making critical decisions. Concept-based explanations have recently become a popular approach for post-hoc interpretation of DNNs. However, identifying human-understandable visual concepts that affect model decisions is a challenging task that is not easily addressed with automatic approaches. We present a novel human-in-the-Ioop approach to generate user-defined concepts for model interpretation and diagnostics. Central to our proposal is the use of active learning, where human knowledge and feedback are combined to train a concept extractor with very little human labeling effort. We integrate this process into an interactive system, ConceptExtract. Through two case studies, we show how our approach helps analyze model behavior and extract human-friendly concepts for different machine learning tasks and datasets and how to use these concepts to understand the predictions, compare model performance and make suggestions for model refinement. Quantitative experiments show that our active learning approach can accurately extract meaningful visual concepts. More importantly, by identifying visual concepts that negatively affect model performance, we develop the corresponding data augmentation strategy that consistently improves model performance.
Evidence Sensitivity as a Heuristic for Doubt

Sensitivity-type principles are prominent in epistemology. They have the promise to explain our intuitive and considered reactions to a wide range of important cases in everyday life, science and philosophy. Despite this promise, philosophers have raised a number of very serious objections to the principles. Accordingly, I propose a different type of sensitivity account which, I believe, gets around these serious objections. An important feature of the new approach is that the principle I propose need not be true. Rather, it should be understood as a cognitive heuristic that tells us when something is not known—a type of doubt. A second feature is that the principle does not care about what one’s belief would be like in counter-factual situations. Instead, it cares about what the bases or causes for the belief would be like in those situations.
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

Lesion detection is a fundamental problem in the computer-aided diagnosis scheme for mammography. The advance of deep learning techniques have made a remarkable progress for this task, provided that the training data are large and sufficiently diverse in terms of image style and quality. In particular, the diversity of image style may be majorly attributed to the vendor factor. However, the collection of mammograms from vendors as many as possible is very expensive and sometimes impractical for laboratory-scale studies. Accordingly, to further augment the generalization capability of deep learning model to various vendors with limited resources, a new contrastive learning scheme is developed. Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles. Afterward, the backbone network is then recalibrated to the downstream task of lesion detection with the specific supervised learning. The proposed method is evaluated with mammograms from four vendors and one unseen public dataset. The experimental results suggest that our approach can effectively improve detection performance on both seen and unseen domains, and outperforms many state-of-the-art (SOTA) generalization methods.
Cognitive Diagram Understanding and Task Performance in Systems Analysis and Design

Models play an important role in systems analysis and design (SAD). A diagrammatic model is defined as a mapping from a domain to a visual representation in such a way that relevant information is preserved to meet a specific goal. So far, cognitive research on diagram criteria in relation to task performance has been fragmented. The aim of this paper is to (1) consolidate research on the cognitive processing steps involved during understanding and task performance with diagrams, (2) consolidate corresponding criteria for such diagrams to best support cognitive processing, and (3) demonstrate the support effective diagrams provide for performing SAD tasks. Addressing the first aim, we develop a theoretical cognitive framework of task performance with diagrams called CogniDia. It integrates different cognitive theories from research on diagrams in software engineering and information systems. Regarding the second aim, we review the literature to organize criteria for effective cognitive processing of diagrams. We identify research gaps on verbal and task processing. Regarding the third aim, we use the theoretical cognitive framework to investigate how diagrams support the SAD process effectively.
Dual-Constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior

Nonnegativity based matrix factorization is usually powerful for learning the parts-based “shallow” representation, however it fails to discover deep hidden information within both the basis concept and representation spaces. In this paper, we therefore propose a new dual-constrained deep semi-supervised coupled factorization network (DS2CF-Net) for learning hierarchical representations. DS2CF-Net is formulated as the joint partial-label and structure-constrained deep factorization network using multi-layers of linear transformations, which coupled updates the basic concepts and new representations in each layer. An error correction mechanism with feature fusion strategy is also integrated between consecutive layers to improve the representation ability of features. To improve the discriminating abilities of both representation and coefficients in feature space, we clearly consider how to enrich the prior knowledge by the coefficients-based label prediction, and incorporate the enriched prior knowledge as the additional label and structure constraints. To be specific, the label constraint enables the intra-class samples to have the same coordinate in the feature space, while the structure constraint forces the coefficients in each layer to be block-diagonal so that the enriched prior knowledge are more accurate. Besides, we integrate the adaptive dual-graph learning to retain the locality structures of both the data manifold and feature manifold in each layer. Finally, a fine-tuning process is performed to refine the structure-constrained matrix and data weight matrix in each layer using the predicted labels for more accurate representations. Extensive simulations on public databases show that our method can obtain state-of-the-art performance.
RPFA-Net: a 4D RaDAR Pillar Feature Attention Network for 3D Object Detection

3D object detection is a crucial problem in environmental perception for autonomous driving. Currently, most works focused on LiDAR, camera, or their fusion, while very few algorithms involve a RaDAR sensor, especially 4D RaDAR providing 3D position and velocity information. 4D RaDAR can work well in bad weather and has a higher performance than traditional 3D RaDAR, but it also contains lots of noise information and suffers measurement ambiguities. Existing 3D object detection methods can&#x0027;t judge the heading of objects by focusing on local features in sparse point clouds. To better overcome this problem, we propose a new method named RPFA-Net only using a 4D RaDAR, which utilizes a self-attention mechanism instead of PointNet to extract point clouds&#x0027; global features. These global features containing long-distance information can effectively improve the network&#x0027;s ability to regress the heading angle of objects and enhance detection accuracy. Our method&#x0027;s performance is enhanced by 8.13&#x0025; of 3D mAP and 5.52&#x0025; of BEV mAP compared with the baseline. Extensive experiments show that RPFA-Net surpasses state-of-the-art 3D detection methods on Astyx HiRes 2019 dataset. The code and pre-trained models are available at https://github.com/adept-thu/RPFA-Net.git.
DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Six different features—VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models—were extracted. Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature.
Revisiting the Performance of iALS on Item Recommendation Benchmarks

Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications. iALS is known to be one of the most computationally efficient and scalable collaborative filtering methods. However, recent studies suggest that its prediction quality is not competitive with the current state of the art, in particular autoencoders and other item-based collaborative filtering methods. In this work, we revisit four well-studied benchmarks where iALS was reported to perform poorly and show that with proper tuning, iALS is highly competitive and outperforms any method on at least half of the comparisons. We hope that these high quality results together with iALS’s known scalability spark new interest in applying and further improving this decade old technique.
Stationary Diffusion State Neural Estimation for Multiview Clustering

Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, their performance limits to using a predefined graph. We argue that the estimation of the stationary diffusion state can be achieved by gradient descent over neural networks. We specifically design the Stationary Diffusion State Neural Estimation (SDSNE) to exploit multiview structural graph information for co-supervised learning. We explore how to design a graph neural network specially for unsupervised multiview learning and integrate multiple graphs into a unified consensus graph by a shared self-attentional module. The view-shared self-attentional module utilizes the graph structure to learn a view-consistent global graph. Meanwhile, instead of using auto-encoder in most unsupervised learning graph neural networks, SDSNE uses a co-supervised strategy with structure information to supervise the model learning. The co-supervised strategy as the loss function guides SDSNE in achieving the stationary state. With the help of the loss and the self-attentional module, we learn to obtain a graph in which nodes in each connected component fully connect by the same weight. Experiments on several multiview datasets demonstrate effectiveness of SDSNE in terms of six clustering evaluation metrics.
STHarDNet: Swin Transformer with HarDNet for MRI Segmentation

In magnetic resonance imaging (MRI) segmentation, conventional approaches utilize U-Net models with encoder–decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder–decoder model structure. However, conventional models have large sizes and slow computation speed and, in vision transformer models, the computation amount sharply increases with the image size. To overcome these problems, this paper proposes a model that combines Swin transformer blocks and a lightweight U-Net type model that has an HarDNet blocks-based encoder–decoder structure. To maintain the features of the hierarchical transformer and shifted-windows approach of the Swin transformer model, the Swin transformer is used in the first skip connection layer of the encoder instead of in the encoder–decoder bottleneck. The proposed model, called STHarDNet, was evaluated by separating the anatomical tracings of lesions after stroke (ATLAS) dataset, which comprises 229 T1-weighted MRI images, into training and validation datasets. It achieved Dice, IoU, precision, and recall values of 0.5547, 0.4185, 0.6764, and 0.5286, respectively, which are better than those of the state-of-the-art models U-Net, SegNet, PSPNet, FCHarDNet, TransHarDNet, Swin Transformer, Swin UNet, X-Net, and D-UNet. Thus, STHarDNet improves the accuracy and speed of MRI image-based stroke diagnosis.
Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

In recent years, research on convolutional neural networks (CNN) and recurrent neural networks (RNN) in deep learning has been actively conducted. In order to provide more personalized and advanced functions in smart home services, studies on deep learning applications are becoming more frequent, and deep learning is acknowledged as an efficient method for recognizing the voices and activities of users. In this context, this study aims to systematically review the smart home studies that apply CNN and RNN/LSTM as their main solution. Of the 632 studies retrieved from the Web of Science, Scopus, IEEE Explore, and PubMed databases, 43 studies were selected and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. In this paper, we examine which smart home applications CNN and RNN/LSTM are applied to and compare how they were implemented and evaluated. The selected studies dealt with a total of 15 application areas for smart homes, where activity recognition was covered the most. This study provides essential data for all researchers who want to apply deep learning for smart homes, identifies the main trends, and can help to guide design and evaluation decisions for particular smart home services.
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection

Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data.Previous research works rarely consider token-level feature fusion, and few works explore learning the common features related to sentiment in multimodal data to help the model fuse multimodal features.In this paper, we propose a Contrastive Learning and Multi-Layer Fusion (CLMLF) method for multimodal sentiment detection.Specifically, we first encode text and image to obtain hidden representations, and then use a multi-layer fusion module to align and fuse the token-level features of text and image.In addition to the sentiment analysis task, we also designed two contrastive learning tasks, label based contrastive learning and data based contrastive learning tasks, which will help the model learn common features related to sentiment in multimodal data.Extensive experiments conducted on three publicly available multimodal datasets demonstrate the effectiveness of our approach for multimodal sentiment detection compared with existing methods.The codes are available for use at https: //github.com/Link-Li/CLMLF
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning With Shapley Values

While Explainable Artificial Intelligence (XAI) is increasingly expanding more areas of application, little has been applied to make deep Reinforcement Learning (RL) more comprehensible.As RL becomes ubiquitous and used in critical and general public applications, it is essential to develop methods that make it better understood and more interpretable.This study proposes a novel approach to explain cooperative strategies in multiagent RL using Shapley values, a game theory concept used in XAI that successfully explains the rationale behind decisions taken by Machine Learning algorithms.Through testing common assumptions of this technique in two cooperation-centered socially challenging multi-agent environments environments, this article argues that Shapley values are a pertinent way to evaluate the contribution of players in a cooperative multi-agent RL context.To palliate the high overhead of this method, Shapley values are approximated using Monte Carlo sampling.Experimental results on Multiagent Particle and Sequential Social Dilemmas show that Shapley values succeed at estimating the contribution of each agent.These results could have implications that go beyond games in economics, (e.g., for non-discriminatory decision making, ethical and responsible AI-derived decisions or policy making under fairness constraints).They also expose how Shapley values only give general explanations about a model and cannot explain a single run, episode nor justify precise actions taken by agents.Future work should focus on addressing these critical aspects.
Phishing URL Detection: A Real-Case Scenario Through Login URLs

Phishing is a social engineering cyberattack where criminals deceive users to obtain their credentials through a login form that submits the data to a malicious server. In this paper, we compare machine learning and deep learning techniques to present a method capable of detecting phishing websites through URL analysis. In most current state-of-the-art solutions dealing with phishing detection, the legitimate class is made up of homepages without including login forms. On the contrary, we use URLs from the login page in both classes because we consider it is much more representative of a real case scenario and we demonstrate that existing techniques obtain a high false-positive rate when tested with URLs from legitimate login pages. Additionally, we use datasets from different years to show how models decrease their accuracy over time by training a base model with old datasets and testing it with recent URLs. Also, we perform a frequency analysis over current phishing domains to identify different techniques carried out by phishers in their campaigns. To prove these statements, we have created a new dataset named Phishing Index Login URL (PILU-90K), which is composed of 60K legitimate URLs, including index and login websites, and 30K phishing URLs. Finally, we present a Logistic Regression model which, combined with Term Frequency - Inverse Document Frequency (TF-IDF) feature extraction, obtains 96.50% accuracy on the introduced login URL dataset.
Handwritten Character Recognition using Neural Network and Tensor Flow

The paper will describe the best approach to get more than 90% accuracy in the field of Handwritten Character Recognition (HCR). There have been plenty of research done in the field of HCR but still it is an open problem as we are still lacking in getting the best accuracy. In this paper, the offline handwritten character recognition will be done using Convolutional neural network and Tensorflow. A method called Soft Max Regression is used for assigning the probabilities to handwritten characters being one of the several characters as it gives the values between 0 and 1 summing up to 1. The purpose is to develop the software with a very high accuracy rate and with minimal time and space complexity and also optimal.
Multisensory Integration Dominates Hypnotisability and Expectations in the Rubber Hand Illusion

Some recent papers by P. Lush and colleagues have argued that the rubber hand illusion (RHI), where participants can feel a rubber hand as their own under appropriate multisensory stimulation, may be caused mainly by hypnotic suggestibility and expectations (demand characteristics). These papers rely primarily on a study with 353 participants who took part in a RHI experiment carried out in a classical way with brush stroking. Participants experienced a synchronous condition where the rubber hand was seen to be touched in synchrony with touch felt on their corresponding hidden real hand, or the touches were applied asynchronously as a control. Each participant had a related measure of their hypnotisability on a scale known as the Sussex-Waterloo Scale of Hypnotisability (SWASH). The authors found a correlation between the questionnaire ratings of the RHI in the synchronous condition and the SWASH score. From this, they concluded that the RHI is largely driven by suggestibility and further proposed that suggestibility and expectations may even entirely explain the RHI. Here we examine their claims in a series of extensive new analyses of their data. We find that at every level of SWASH, the synchronous stimulation results in greater levels of the illusion than the asynchronous condition; moreover, proprioceptive drift is greater in the synchronous case at every level of SWASH. Thus, while the level of hypnotisability does modestly influence the subjective reports (higher SWASH is associated with somewhat higher illusion ratings), the major difference between the synchronous and asynchronous stimulation is always present. Furthermore, by including in the model the participants’ expectancy ratings of how strongly they initially believed they would experience the RHI in the two conditions, we show that expectations had a very small effect on the illusion ratings; model comparisons further demonstrate that the multisensory condition is two-to-three-times as dominant as the other factors, with hypnotisability contributing modestly and expectations negligibly. Thus, although the results indicate that trait suggestibility may modulate the RHI, presumably through intersubject variations in top-down factors, the findings also suggest that the primary explanation for the RHI is as a multisensory bodily illusion.
A literature review of financial losses statistics for cyber security and future trend

Cybercrime directs to any criminal activity taken out utilizing computers or the internet. Attackers have chosen strategies such as social engineering, phishing, and malware as part of their cyber-attacks. A cyber-attack can lead to various effects, ranging from stealing individual data to extortion money or losing helpful information. Society and systems depend on critical infrastructures like power plants, hospitals, and financial services companies. This paper analyzes financial losses statistics for cyber security and future trends. The cost of cybercrime prevention is increasing day by day. Financial losses refer to damages to the wealth of an organization. This includes organizational losses, compensation, and legal fees. By financial loss, we mean increased costs or reduced income caused by the threat. We collect data from various datasets and information from sources. After collecting data, we analyze the data and create a different chart to identify the growth of cyber-attacks, cyber security, and cybercrime costs. We analyze global and worldwide cybercrime status. We also investigate state-wise cybercrime and the cyber security status of the United States of America. Our main objective of the analysis is to find out the financial losses and future trends of cybercrime and cyber security. From our study, we noticed that the number of cybercrimes and their management and prevention costs are rapidly increasing in the USA and worldwide.