Reviewerly/exHarmony huggingface dataset:
White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific attention to the discriminative regions of a white blood cell. The performance of the proposed method/model was evaluated through an extensive set of experiments and validation. The obtained results demonstrate the superior performance of the model achieving 99.69 % accuracy compared to other state-of-the-art approaches. The proposed model is a good alternative and complementary to existing computer diagnosis tools to assist pathologists in evaluating white blood cells from blood smear images.
Multi-Channel LSTM-Capsule Autoencoder Network for Anomaly Detection on Multivariate Data

Deep learning techniques have recently shown promise in the field of anomaly detection, providing a flexible and effective method of modelling systems in comparison to traditional statistical modelling and signal processing-based methods. However, there are a few issues that Neural Networks (NN)s face, such as generalisation ability, requiring large volumes of labelled data to train effectively, and understanding spatial context in data. This paper introduces a novel NN architecture to tackle these problems, which utilises a Long-Short-Term-Memory (LSTM) encoder and Capsule decoder in a multi-channel input Autoencoder architecture for use on multivariate time series data. Experimental results show that using Capsule decoders increases the resilience of the model to overfitting and improves training efficiency, which is shown by the improvement of Mean Squared Error (MSE) on unseen data from an average of 10.61 to 2.08 for single channel architectures, and 10.08 to 2.05 for multi-channel architectures. Additionally, results also show that the proposed model can learn multivariate data more consistently, and was not affected by outliers in the training data. The proposed architecture was also tested on an open-source benchmark, where it achieved state-of-the-art performance in outlier detection, and performs best overall with a total accuracy of 0.494 over the metrics tested.
Variational inference of fractional Brownian motion with linear computational complexity

We introduce a simulation-based, amortized Bayesian inference scheme to infer the parameters of random walks. Our approach learns the posterior distribution of the walks' parameters with a likelihood-free method. In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk. In the second step an invertible neural network generates the posterior distribution of the parameters from the learned summary statistics using variational inference. We apply our method to infer the parameters of the fractional Brownian motion model from single trajectories. The computational complexity of the amortized inference procedure scales linearly with trajectory length, and its precision scales similarly to the Cram\'er-Rao bound over a wide range of lengths. The approach is robust to positional noise, and generalizes to trajectories longer than those seen during training. Finally, we adapt this scheme to show that a finite decorrelation time in the environment can furthermore be inferred from individual trajectories.
Temporal burstiness and collaborative camouflage aware fraud detection

With the prosperity and development of the digital economy, many fraudsters have emerged on e-commerce platforms to fabricate fraudulent reviews to mislead consumers’ shopping decisions for profit. Moreover, in order to evade fraud detection, fraudsters continue to evolve and present the phenomenon of adversarial camouflage and collaborative attack. In this paper, we propose a novel temporal burstiness and collaborative camouflage aware method (TBCCA) for fraudster detection. Specifically, we capture the hidden temporal burstiness features behind camouflage strategy based on the time series prediction model, and identify highly suspicious target products by assigning suspicious scores as node priors. Meanwhile, a propagation graph integrating review collusion is constructed, and an iterative fraud confidence propagation algorithm is designed for inferring the label of nodes in the graph based on Loop Belief Propagation (LBP). Comprehensive experiments are conducted to compare TBCCA with state-of-the-art fraudster detection approaches, and experimental results show that TBCCA can effectively identify fraudsters in real review networks with achieving 6%–10% performance improvement than other baselines.
Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision

Graph convolutional networks (GCNs) have become a popular tool for learning unstructured graph data due to their powerful learning ability. Many researchers have been interested in fusing topological structures and node features to extract the correlation information for classification tasks. However, it is inadequate to integrate the embedding from topology and feature spaces to gain the most correlated information. At the same time, most GCN-based methods assume that the topology graph or feature graph is compatible with the properties of GCNs, but this is usually not satisfied since meaningless, missing, or even unreal edges are very common in actual graphs. To obtain a more robust and accurate graph structure, we intend to construct an adaptive graph with topology and feature graphs. We propose Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision (MFGCN), which learn a connected embedding by fusing the multi-graphs and node features. We can obtain the final node embedding for semi-supervised node classification by propagating node features over multi-graphs. Furthermore, to alleviate the problem of labels missing in semi-supervised classification, a pseudo-label generation mechanism is proposed to generate more reliable pseudo-labels based on the similarity of node features. Extensive experiments on six benchmark datasets demonstrate the superiority of MFGCN over state-of-the-art classification methods.
Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models.
A novel pedal musculoskeletal response based on differential spatio-temporal LSTM for human activity recognition

Human activity recognition (HAR) with wearable devices has shown superior usability in everyday life tracking and healthcare monitoring in recent years. To solve the existing challenges of weak sensor durability and difficulty capturing dynamic features of traditional wearable devices based on the long short-term memory neural network (LSTM), a new HAR system based on a pedal wearable device is proposed by utilizing a novel differential spatio-temporal LSTM (DST-LSTM) method. The pedal wearable device is embedded in the tongue area of the general footwear to obtain pedal musculoskeletal response (PMR) data of dorsum pedis. Based on the PMR data, the DST-LSTM is developed to classify five typical activity statuses as standing, sitting, floor walking, down the stairs and up the stairs. First, the dynamic differential information of the PMR data is taken into consideration to build a new LSTM unit. Second, the spatial relationship features of the PMR data are obtained by multi-head graph attention networks (GAT). Third, a novel spatial gate is added to the new LSTM unit to filter irrelevant spatial features and get valid information. The effectiveness and superiority of both the pedal wearable device and the DST-LSTM method are validated by comparison with the state-of-the-art methods and wearable devices.
The effect of quality, security and privacy factors on trust and intention to use e-government services

In keeping abreast with the digitized and automated world today, governments of developing and developed nations must provide appropriate e-government services to assure confidence and effective and efficient usage among their citizens. The quality, security, and privacy of current e-government implementation have been impairing the trust and participation of users, in Jordan especially. Hence, this study examined the impacts of quality, security and privacy of e-government services on the intention to use e-government services among Jordanian citizens. Questionnaires were used to gather data, and questionnaire items covered the constructs of quality factors (information quality, system quality, and service quality), perceived security, and perceived privacy as independent variables, and the constructs of trust and intention to use as dependent variables. The study samples comprised academics in Jordanian public universities. The universities were selected using stratified sampling method, while the respondents were chosen using simple random sampling method - 212 respondents were selected. SPSS Version 18 and PLS Version 3.3.6 were used in data analyses and hypotheses testing. Results affirmed a positive and significant link between information quality, system quality, service quality, perceived security, perceived privacy and trust in e-government services, and a positive and significant link between trust in e-government services on intention to use. In e-government services implementation, Jordanian government should take into account the quality factors (information quality, system quality, and service quality), perceived privacy, and perceived security, to increase trust of the citizens and consequently their intention to use the e-government services.
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks

In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To address these tasks, we propose X-DETR, whose architecture has three major components: an object detector, a language encoder, and vision-language alignment. The vision and language streams are independent until the end and they are aligned using an efficient dot-product operation. The whole network is trained end-to-end, such that the detector is optimized for the vision-language tasks instead of an off-the-shelf component. To overcome the limited size of paired object-language annotations, we leverage other weak types of supervision to expand the knowledge coverage. This simple yet effective architecture of X-DETR shows good accuracy and fast speeds for multiple instance-wise vision-language tasks, e.g., 16.4 AP on LVIS detection of 1.2K categories at $$\sim $$ 20 frames per second without using any LVIS annotation during training. The code is available at https://github.com/amazon-research/cross-modal-detr.
Multi-Robot Active Mapping via Neural Bipartite Graph Matching

We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimation to enable more efficient robot movements. Previous approaches either choose the frontier as the goal position via a myopic solution that hinders the time efficiency, or maximize the long-term value via reinforcement learning to directly regress the goal position, but does not guarantee the complete map construction. In this paper, we propose a novel algorithm, namely NeuralCoMapping, which takes advantage of both approaches. We reduce the problem to bipartite graph matching, which establishes the node correspondences between two graphs, denoting robots and frontiers. We introduce a multiplex graph neural network (mGNN) that learns the neural distance to fill the affinity matrix for more effective graph matching. We optimize the mGNN with a differentiable linear assignment layer by maximizing the long-term values that favor time efficiency and map completeness via reinforcement learning. We compare our algorithm with several state-of-the-art multi-robot active mapping approaches and adapted reinforcement-learning baselines. Experimental results demonstrate the superior performance and exceptional generalization ability of our algorithm on various indoor scenes and unseen number of robots, when only trained with 9 indoor scenes.
Motivated Belief Updating and Rationalization of Information

We study belief updating about relative performance in an ego-relevant task. Manipulating the perceived ego-relevance of the task, we show that subjects update their beliefs optimistically because they derive direct utility flows from holding positive beliefs. This finding provides a behavioral explanation why and how overconfidence can evolve in the presence of objective information. Moreover, we document that subjects, who received more bad signals, downplay the ego-relevance of the task. Taken together, these findings suggest that subjects use two alternative strategies to protect their ego when presented with objective information.
Ganster R-CNN: Occluded Object Detection Network Based on Generative Adversarial Nets and Faster R-CNN

Object detection has shown noticeably rapid improvement, despite most existing methods still scrabbling in occluded object detection. In response to this problem, this paper proposes a method for occluded object detection called Ganster R-CNN, which is on a basis of improved Generative Adversarial Nets (IGAN) and Faster R-CNN to enhance the detection ability of occluded objects. IGAN combines the generator of Generative Adversarial Nets and the detector of Faster R-CNN. By considering the lack of diversity of information in the feature maps, we first integrated feature maps from the shallow layer to the deep layer using Feature Pyramid Network. Next, the generator can generate occluded fake samples, and the scale of the training samples and the proportion of occluded objects in the dataset are expanded. Thus, the precision rate of occluded objects can be improved. Thus, the adversarial learning strategy can improve the detection ability of Faster R-CNN detector. Experiments show that compared with Faster R-CNN, this method achieves an improvement of +10.3 AP on the MS COCO dataset, and the mean average precision of this method is improved by 4.31% on the VOC2007 dataset and 3.92% on the VOC2012 dataset. Compared with classically existing models on PASCAL VOC datasets and some Transformer based models on MS COCO dataset, this method improves the average precision value and the mean average precision value of occluded objects.
EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices

Understanding social interactions from egocentric views is crucial for many applications, ranging from assistive robotics to AR/VR. Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view. However, research in this area is severely hindered by the lack of datasets. Existing datasets are limited in terms of either size, capture/annotation modalities, ground-truth quality, or interaction diversity. We fill this gap by proposing EgoBody, a novel large-scale dataset for human pose, shape and motion estimation from egocentric views, during interactions in complex 3D scenes. We employ Microsoft HoloLens2 headsets to record rich egocentric data streams (including RGB, depth, eye gaze, head and hand tracking). To obtain accurate 3D ground truth, we calibrate the headset with a multi-Kinect rig and fit expressive SMPL-X body meshes to multi-view RGB-D frames, reconstructing 3D human shapes and poses relative to the scene, over time. We collect 125 sequences, spanning diverse interaction scenarios, and propose the first benchmark for 3D full-body pose and shape estimation of the interaction partner from egocentric views. We extensively evaluate state-of-the-art methods, highlight their limitations in the egocentric scenario, and address such limitations leveraging our high-quality annotations. Data and code are available at https://sanweiliti.github.io/egobody/egobody.html .
A Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis

Unequal data distribution among different classes usually cause a class imbalance problem.Due to the class imbalance, the classification models become biased toward the majority class and misclassify the minority class.Class imbalance issue becomes more complex when it occurs in multi-class data.The most common method to handle the class imbalance is data resampling that involves either oversampling minority class instances or under-sampling majority class instances.In the case of undersampling, there is a chance of losing some crucial information, whereas over-sampling can cause an overfitting problem.Therefore, we propose a novel Cluster-based Hybrid Sampling for Imbalance Data (CBHSID) strategy to address these issues.We calculate the mean of the data observations based on the number of classes.CBHSID uses the calculated mean as a threshold value to segregate majority and minority classes.We apply affinity propagation cluster analysis to each class to create sub-clusters.We calculate the distance of each data item of sub-cluster using centroid mean.We remove data observations that are away from the center of sub-cluster during under-sampling.On the other hand, during the oversampling, we generate synthetic samples using data observations near to the center of sub-cluster.We compared our proposed approach with a few state-of-the-art data balancing methods on 12 binary and 4 multi-class benchmark datasets.Based on Geometric-Mean (G-Mean), Recall, and F1-score, our method outperformed the other compared methods on 14 datasets out of 16.We identified that CBHSID is suitable for addressing class imbalance issues in both binary and multi-class classifications.In the current state, we have only validated CBHSID on stationary data streams.Consequently, CBHSID can further be tested on non-stationary data streams in online learning environments.
Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks

Figure 1.[Better seen in color].Overview of the proposed solution.Our Agglomerator is a novel architecture for vision applications, in which column structure (c) mimics hyper-columns typical of the human visual cortex [14].The input data (a) is fed to the columns using a patch-based embedding (b).The Agglomerator architecture iteratively routes the information across its structure, creating a neural representation of each image, similar to neural fields [37].In the neural representation, part-whole hierarchies (d) emerge at different levels of the columns.The same column can represent the same patch of the image with different levels of abstraction (e.g., the ears, the head, and the dog) corresponding to each level in the column.Neighbor columns agree on a part representation (e.g ears, head) at lower levels, ideally representing the same whole (e.g.dog) at the top level.The resulting feature space represents the conceptual-semantic relationships between data (e) resembling the human hierarchical organization [38].Samples belonging to the same super-class (e.g., animals, vehicles) are clustered together, with conceptually close categories (e.g., birds and airplanes) represented on the edge of the super-classes.
Time-Space Lower Bounds for Finding Collisions in Merkle-Damgård Hash Functions

We revisit the problem of finding B-block-long collisions in Merkle-Damgård Hash Functions in the auxiliary-input random oracle model, in which an attacker gets a piece of S-bit advice about the random oracle and makes T oracle queries. Akshima, Cash, Drucker and Wee (CRYPTO 2020), based on the work of Coretti, Dodis, Guo and Steinberger (EUROCRYPT 2018), showed a simple attack for $$2\le B\le T$$ (with respect to a random salt). The attack achieves advantage $$\widetilde{\varOmega }(STB/2^n+T^2/2^n)$$ where n is the output length of the random oracle. They conjectured that this attack is optimal. However, this so-called STB conjecture was only proved for $$B\approx T$$ and $$B=2$$ . Very recently, Ghoshal and Komargodski (CRYPTO 22) confirmed STB conjecture for all constant values of B, and provided an $$\widetilde{O}(S^4TB^2/2^n+T^2/2^n)$$ bound for all choices of B. In this work, we prove an $$\widetilde{O}((STB/2^n)\cdot \max \{1,ST^2/2^n\}+ T^2/2^n)$$ bound for every $$2< B < T$$ . Our bound confirms the STB conjecture for $$ST^2\le 2^n$$ , and is optimal up to a factor of S for $$ST^2>2^n$$ (note as $$T^2$$ is always at most $$2^n$$ , otherwise finding a collision is trivial by the birthday attack). Our result subsumes all previous upper bounds for all ranges of parameters except for $$B=\widetilde{O}(1)$$ and $$ST^2>2^n$$ . We obtain our results by adopting and refining the technique of Chung, Guo, Liu, and Qian (FOCS 2020). Our approach yields more modular proofs and sheds light on how to bypass the limitations of prior techniques. Along the way, we obtain a considerably simpler and illuminating proof for $$B=2$$ , recovering the main result of Akshima, Cash, Drucker and Wee.