Reviewerly/exHarmony huggingface dataset:
Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the paper. Our evaluation is performed using the Dice or f1 score coefficient and the Jaccard index. Our architecture may simply be extended to ischemia segmentation and computed tomography image identification by selecting relevant hyperparameters. The Dice/f1 score similarity coefficient of our model shown 58% and results close to ground truth which is higher than the standard 3D UNet model, demonstrating that our model can accurately segment ischemic stroke. The modified 3D UNet model proposed by us uses an efficient averaging method inside a neural network. Since this set of ISLES is limited in number, using the data augmentation method and neural network regularization methods to prevent overfitting gave the best result. In addition, one of the advantages is the use of the Intersection over Union loss function, which is based on the assessment of the coincidence of the shapes of the recognized zones.
MetaLearning with Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.
Visual enhancement of single-view 3D point cloud reconstruction

3D reconstruction from a single image is one of the core computer vision problems. Thanks to the development of deep learning, 3D reconstruction of a single image has demonstrated impressive progress in recent years. Existing researches use Chamfer distance as a loss function to guard the training of the neural network. However, the Chamfer loss will give equal weights to all points inside the 3D point clouds. It tends to sacrifice fine-grained and thin structures to avoid incurring a high loss, which will lead to visually unsatisfactory results. This paper proposes a framework that can recover a detailed three-dimensional point cloud from a single image by focusing more on boundaries (edge and corner points). Experimental results demonstrate that the proposed method outperforms existing techniques significantly, both qualitatively and quantitatively, and has fewer training parameters.
Multi-label video classification via coupling attentional multiple instance learning with label relation graph

Multi-label video classification is a challenging problem in pattern recognition field, as it is difficult to grasp the occurring localizations of a huge number of labels in videos. To solve this problem, we propose a general framework named MALL-CNN, i.e., Multi-Attention Label Relation Learning Convolutional Neural Network. MALL-CNN not only builds the correspondences between labels and videos by an attention mechanism, but also captures label co-occurrence by a graph learning approach. Specifically, we introduce multiple instance learning to composite a set of frame-level features into a video-level feature. Then, video-level feature is mapped into the content-aware category representations in an improved attentional manner. Further, these representations are enhanced by a series of label relation graphs, which transform global label relationships to the label relationships of current video. With the three processes, frame feature aggregation, video feature mapping, and label relationship construction can be achieved in MALL-CNN for multi-label video classification. Extensive experiments on real-world scene benchmark Youtube-8M verify that MALL-CNN with only frame feature surpasses the state of the arts with multi-modal features as well as ensemble models.
Conditioned Image Retrieval for Fashion using Contrastive Learning and CLIP-based Features

Building on the recent advances in multimodal zero-shot representation learning, in this paper we explore the use of features obtained from the recent CLIP model to perform conditioned image retrieval. Starting from a reference image and an additive textual description of what the user wants with respect to the reference image, we learn a Combiner network that is able to understand the image content, integrate the textual description and provide combined feature used to perform the conditioned image retrieval. Starting from the bare CLIP features and a simple baseline, we show that a carefully crafted Combiner network, based on such multimodal features, is extremely effective and outperforms more complex state of the art approaches on the popular FashionIQ dataset.
Traffic control hand signal recognition using convolution and recurrent neural networks

Abstract Gesture understanding is one of the most challenging problems in computer vision. Among them, traffic hand signal recognition requires the consideration of speed and the validity of the commanding signal. The lack of available datasets is also a serious problem. Most classifiers approach these problems using the skeletons of target actors in an image. Extracting the three-dimensional coordinates of skeletons is simplified when depth information accompanies the images. However, depth cameras cost significantly more than RGB cameras. Furthermore, the extraction of the skeleton needs to be performed in prior. Here, we show a hand signal detection algorithm without skeletons. Instead of skeletons, we use simple object detectors trained to acquire hand directions. The variance in the time length of gestures mixed with random pauses and noise is handled with a recurrent neural network (RNN). Furthermore, we have developed a flag sequence algorithm to assess the validity of the commanding signal. In whole, the computed hand directions are sent to the RNN, which identifies six types of hand signals given by traffic controllers with the ability to distinguish time variations and intermittent randomly appearing noises. We constructed a hand signal dataset composed of 100 thousand RGB images that is made publicly available. We achieved correct recognition of the hand signals with various backgrounds at 91% accuracy. A processing speed of 30 FPS in FHD video streams, which is a 52% improvement over the best among previous works, was achieved. Despite the extra burden of deciding the validity of the hand signals, this method surpasses methods that solely use RGB video streams. Our work is capable of performing with nonstationary viewpoints, such as those taken from moving vehicles. To accomplish this goal, we set a higher priority for the speed and validity assessment of the recognized commanding signals. The collected dataset is made publicly available through the Korean government portal under the URL “data.go.kr/data/15075814/fileData.do.”
Improving Tax Audit Efficiency Using Machine Learning: The Role of Taxpayer’s Network Data in Fraud Detection

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Proxemic maps for immersive visualization

In human computer interaction, proxemics describes the ways that people use space to interact with other people or objects. We focus on proxemic maps, which are virtual maps in immersive environments that react to proxemic interaction. Proxemic maps take advantage of new opportunities brought about by immersive visualization, where virtual maps can be freely positioned in virtual or physical space and adapt themselves relative to the spatial position of the viewer. We discuss proxemic interactions that alter the content and type of maps, including changing scale, symbolization, type of visualization and geometry. We propose a novel transformation that changes the geometry of maps based on their proximity to users. Users move the map back and forth and the map transitions between ring, horizontal, vertical and cylindrical geometries. The ring geometry surrounds the user and aligns features on the map with features in the real world. We implemented the map transformation in virtual reality and conducted a user study to evaluate it. The results of the user study indicate that participants preferred the ring and horizontal geometries. The ring geometry is useful because it simplifies connecting virtual features on the map with real features in the landscape, while the horizontal geometry provides an overall view of the landscape. We further found that combination of different geometries helped the study participants to overcome the limitations of each geometry.
Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (Re-ID) has received increasing research attention for its great practical value in night-time surveillance scenarios. Due to the large variations in person pose, viewpoint, and occlusion in the same modality, as well as the domain gap brought by heterogeneous modality, this hybrid modality person matching task is quite challenging. Different from the metric learning methods for visible person re-ID, which only pose similarity constraints on class level, an efficient metric learning approach for visible-infrared person Re-ID should take both the class-level and modality-level similarity constraints into full consideration to learn sufficiently discriminative and robust features. In this article, the hybrid modality is divided into two types, within modality and cross modality. We first fully explore the variations that hinder the ranking results of visible-infrared person re-ID and roughly summarize them into three types: within-modality variation, cross-modality modality-related variation, and cross-modality modality-unrelated variation. Then, we propose a comprehensive metric learning framework based on four kinds of paired-based similarity constraints to address all the variations within and cross modality. This framework focuses on both class-level and modality-level similarity relationships between person images. Furthermore, we demonstrate the compatibility of our framework with any paired-based loss functions by giving detailed implementation of combing it with triplet loss and contrastive loss separately. Finally, extensive experiments of our approach on SYSU-MM01 and RegDB demonstrate the effectiveness and superiority of our proposed metric learning framework for visible-infrared person Re-ID.
Imposing Semantic Consistency of Local Descriptors for Few-Shot Learning

Few-shot learning suffers from the scarcity of labeled training data. Regarding local descriptors of an image as representations for the image could greatly augment existing labeled training data. Existing local descriptor based few-shot learning methods have taken advantage of this fact but ignore that the semantics exhibited by local descriptors may not be relevant to the image semantic. In this paper, we deal with this issue from a new perspective of imposing semantic consistency of local descriptors of an image. Our proposed method consists of three modules. The first one is a local descriptor extractor module, which can extract a large number of local descriptors in a single forward pass. The second one is a local descriptor compensator module, which compensates the local descriptors with the image-level representation, in order to align the semantics between local descriptors and the image semantic. The third one is a local descriptor based contrastive loss function, which supervises the learning of the whole pipeline, with the aim of making the semantics carried by the local descriptors of an image relevant and consistent with the image semantic. Theoretical analysis demonstrates the generalization ability of our proposed method. Comprehensive experiments conducted on benchmark datasets indicate that our proposed method achieves the semantic consistency of local descriptors and the state-of-the-art performance.
Balancing Accuracy and Interpretability through Neuro-Fuzzy Models for Cardiovascular Risk Assessment

Machine learning algorithms have been proven to be effective in supporting medical decisions. However, often the resulting models are difficult to understand by clinicians. Fuzzy Inference Systems are effective in returning interpretable models that can be easily described by IF-THEN rules expressed in natural language. Yet, conventional fuzzy systems require human expertise to design the fuzzy rule base. Neuro-fuzzy systems are data-driven algorithms, that are able to learn fuzzy rule-based models from data, thus avoiding the time consuming task of manually designing the rules. In this work we propose the use of neuro-fuzzy systems to learn predictive models from data in form of fuzzy rules that are suitable to support decision making for cardiovascular risk assessment. Our results show the effectiveness of these models in returning accurate predictions while preserving the interpretability. Furthermore, our results indicate that the models derived automatically are more accurate than the models defined manually, with the best accuracy of 0.91.
DevOps for Ethereum Blockchain Smart Contracts

With the evolution and proliferation of blockchain, the technology is becoming more prevalent in enterprise software development. Using the already proven DevOps approach in this setting makes sense, as it can accelerate the general pace of software development and delivery, improve software quality, and increase overall productivity. However, there is currently a lack of guidance on a structured DevOps approach and a breakdown of the specifics in the context of blockchain-based software development. Therefore, we combined gray literature and DevOps application studies from pertinent GitHub projects to systematically investigate current practices and solution approaches for an efficient blockchain-oriented DevOps procedure. In this process, we elaborated procedural steps and related activities according to the main stages of Continuous Integration and Continuous Delivery. Our research shows that core DevOps concepts and activities are similar to other areas and are entirely possible with already established CI/CD solutions that orchestrate the right tools, with the difference that more rigorous testing and differentiated deployment practices are required due to the inherent immutability of blockchain.
Evaluation of Cyber Security Threats in Banking Systems

The financial sector is the part of the economy that consists of companies and institutions that provide financial services to commercial and retail customers. It covers a wide range of industries, including banks, investment or insurance companies, and real estate companies. Financial institutions are exposed to cyber-attacks. Banks are places where money is available, and for cybercriminals, attacking banks offers a variety of ways to make a profit through extortion, robbery, and fraud, while national governments also provide financial support for hackers, they target political and ideological influence. According to Global Findex, approximately 1.2 billion adults have had bank accounts since 2011. The purpose of this study is to evaluate the effectiveness of existing cyber security methods in reducing or mitigating financial crime and achieving the CIA business, as well as creating security recommendations for potential improvements.
Fast data reduction by space partitioning via convex hull and MBR computation

Large volumes of training data introduce high computational cost in instance-based classification. Data reduction algorithms select or generate a small (condensing) set of representative training prototypes from the available training data. The Reduction by Space Partitioning algorithm is one of the most well-known prototype generation algorithms that repetitively divides the original training data into subsets. This partitioning process needs to identify the diameter of each subset, i.e., its two farthest instances. This is a costly process since it requires the calculation of all distances between the instances in each subset. The paper introduces two new very fast variations that, instead of computing the actual diameter of a subset, choose a pair of distant-enough instances. The first variation uses instances belonging to an exact 3d convex hull of the subset, while the second one uses instances belonging to the minimum bounding rectangle of the subset. Our experimental study shows that the new variations vastly outperform the original algorithm without a penalty in classification accuracy and reduction rate.
Face Direction Estimation based on Mediapipe Landmarks

Many studies had been presented to estimate face direction that relied on training images. A small portion of these studies relied on mathematical calculations with poor results. This paper proposed implementing of trigonometric functions to estimate face direction by using detected landmarks. These landmarks had been detected using the 3D model of (Mideapipe) method. Among there detected landmarks, several points are selected as predicted landmarks. The angles among extended lines from these selected landmarks have been measured to calculate the directions of the human head. The experimental results showed that proposed method achieved high performance with very low error ratio (0.055) for Yaw angle, (0.046) for Pith angle and (0.025) for Roll angle. The difference was approximately ±0.5 degree between real and predicted measures.
Dynamic graph computing: A method of finding companion vehicles from traffic streaming data

For intelligent transportation research, the detection of companion vehicle patterns aids in excavating behavioral relation between vehicles. This paper builds a dynamic license plate corpus based on streaming automatic number plate recognition data and combines the monitoring cameras character division to mine companion vehicle groups in real time. First, we establish the traffic flow graph based on vehicle trajectory data, and the improved PageRank algorithm is used to obtain the influence of the monitoring cameras. Second, the time-sliding window mechanism in Spark Streaming is applied to the streaming data, and the creation and updating of vehicle dynamic license plate corpus are completed according to the driving trajectory data. Finally, the plate-number dynamic graph computing algorithm is proposed to establish the dynamic relation graph between vehicles based on dynamic corpus and character identification of monitoring cameras. The camera characters are used as the influencing factor to correlate with the graph formed by vehicle nodes. The companion groups are obtained by real-time calculation of the trim and weight in the graph. Experimental analysis on the real license plate recognition dataset shows that the proposed model can effectively reduce the complexity of data processing and can detect companion vehicle groups in real time.
Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.
A unified structure learning framework for graph attention networks

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many fields and attracted a lot of attention in the communit. Most Graph Neural Networks can be merely used when graph-structured data is available. However, many graph structures have noise, or data itself has no graph structures, so learning the dynamic and adaptive graph structures is necessary. In this paper, we propose a unified structure learning framework for Graph Attention Networks. Specifically, we first design a strategy to learn the graph structures. Then we develop a novel attention mechanism based on structure context information of graph and node representations. Further, we devise Structure Learning Graph Attention Networks (SLGAT) and Structure Learning Attention-based Graph Neural Networks (SLAGNN) by using the new attention mechanism on the new graph. Finally, we demonstrate that our approaches outperform competing methods on six standard datasets for the semi-supervised node classification task.
Collaborative Distribution Alignment for 2D image-based 3D shape retrieval

Retrieving 3D shapes with 2D images has become a popular research area nowadays, and a great deal of work has been devoted to reducing the discrepancy between 3D shapes and 2D images to improve retrieval performance. However, most approaches ignore the semantic information and decision boundaries of the two domains, and cannot achieve both domain alignment and category alignment in one module. In this paper, a novel Collaborative Distribution Alignment (CDA) model is developed to address the above existing challenges. Specifically, we first adopt a dual-stream CNN, following a similarity guided constraint module, to generate discriminative embeddings for input 2D images and 3D shapes (described as multiple views). Subsequently, we explicitly introduce a joint domain-class alignment module to dynamically learn a class-discriminative and domain-agnostic feature space, which can narrow the distance between 2D image and 3D shape instances of the same underlying category, while pushing apart the instances from different categories. Furthermore, we apply a decision boundary refinement module to avoid generating class-ambiguity embeddings by dynamically adjusting inconsistencies between two discriminators. Extensive experiments and evaluations on two challenging benchmarks, MI3DOR and MI3DOR-2, demonstrate the superiority of the proposed CDA method for 2D image-based 3D shape retrieval task.