Reviewerly/exHarmony huggingface dataset:
VacChain: A Blockchain-Based EMR System to Manage Child Vaccination Records

The digitalization of healthcare-related information service systems has become a trend across the world. However, several crucial services are still provided manually due to a lack of trust in digital solutions. One such service is keeping records of children’s vaccination, which still relies on a paper-based file system in most parts of the world. This approach causes serious data integrity problems. Recently, healthcare has become a potential application area of the blockchain, as it can preserve and protect highly sensitive private medical records while sharing these records in a decentralized manner without losing personal ownership. Therefore, we propose a new digital model to track a child’s vaccination records using blockchain. In particular, this proposed application helps improve the vaccination record-keeping process by ensuring the integrity of the preserved data in a more secure way. In an emerging pandemic situation, our approach can be extended to manage the overall vaccination process effectively.
Deep Embedded Clustering of Urban Communities Using Federated Learning

Deep clustering utilizes representation learning to learn features in an unsupervised setting. Although successful, the current models rely on the assumption of the centralized dataset, which due to the privacy concerns is becoming less realistic. To address this challenge, we propose a federated deep convolutional embedded clustering framework. Our framework relies on a federated server to orchestrate the training between workers where each participant individually trains the model with the objective of decreasing clustering loss using Kullback–Leibler divergence. To avoid feature space being distorted by the clustering loss, each worker maintains their own local decoder which for privacy reasons is not shared with the federated server. Empirical results with both IID and non-IID client data on benchmark datasets demonstrates the feasibility of our federated training when compared to the centralized counterpart. We also evaluate our model on a real world application of community detection using GPS traces and measure the computational complexity and energy consumption on a smartphone.
Ascetic: Enhancing Cross-Iterations Data Efficiency in Out-of-Memory Graph Processing on GPUs

Graph analytics are widely used in real-world applications, and GPUs are major accelerators for such applications. However, as graph sizes become significantly larger than the capacity of GPU memory, the performance can degrade significantly due to the heavy overhead required in moving a large amount of graph data between CPU main memory and GPU memory.
Open-source Software for Determining the Dynamic Areas of Interest for Eye Tracking Data Analysis

There is a vivid need for an effective, universal and easy-to-use software tool supporting visual attention analysis based on eye-tracking data using the areas of interest for objects moving within the system of coordinates in which gaze position is recorded. Thus, we decided to design such software using existing algorithms offered by the discipline of computer vision, with an intention the eye-tracking research community could freely use it. According to the tests carried out using two datasets, the MOTLD algorithm had the best efficiency among all implemented in the presented software in determining the dynamic areas of interest. Therefore, we recommend it as the default one. This paper presents the first version of the tool and is an invitation to collaborate on developing this software.
Three-Dimensional Path Planning of UAV Based on Improved Artificial Potential Field

In order to find a safe path for Unmanned Aerial Vehicle (UAV) in forest fires, a global path planning method based on improved artificial potential field (APF) is proposed. This method applies the grid model to model threats in three-dimensional space; then uses the target as the source of gravity to establish a gravitational field, and the threat as the source of repulsion to establish a repulsive field. At the same time, it improves the modeling of gravitational and repulsive fields to overcome the disadvantages of the traditional artificial potential field; then with dynamic obstacle avoidance, the UAV can quickly escape the threat of dynamic obstacles. Simulation results prove that this method can get the optimal route in a given environment.
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective

Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) due to improved convergence. BN is observed to increase the model accuracy while at the cost of adversarial robustness. There is an increasing interest in the ML community to understand the impact of BN on DNNs, especially related to the model robustness. This work attempts to understand the impact of BN on DNNs from a non-robust feature perspective. Straightforwardly, the improved accuracy can be attributed to the better utilization of useful features. It remains unclear whether BN mainly favors learning robust features (RFs) or non-robust features (NRFs). Our work presents empirical evidence that supports that BN shifts a model towards being more dependent on NRFs. To facilitate the analysis of such a feature robustness shift, we propose a framework for disentangling robust usefulness into robustness and usefulness. Extensive analysis under the proposed framework yields valuable insight on the DNN behavior regarding robustness, e.g. DNNs first mainly learn RFs and then NRFs. The insight that RFs transfer better than NRFs, further inspires simple techniques to strengthen transfer-based black-box attacks.
Analysis of Quantum Radar Cross-Section of Dihedral Corner Reflector

In this study, we develop the calculation models for single and double scattering of dihedral corner reflectors and obtained the QRCS expression for dihedral corner reflectors of any structure. The simulation results demonstrate that the center of QRCS mainly exhibits double scattering characteristics, while the edge exhibits single scattering characteristics. Dihedral corner reflector with acute dihedral angle has stronger scattering intensity in the edge. However, the QRCS of the obtuse dihedral corner reflectors show an obvious single scattering peak, which approaches to the bisector as the dihedral angle increases. When detecting dihedral corner reflectors with different size ratios, the side with a larger facet has a higher detection probability. The characteristics of dihedral corner reflectors in experiments provide specific guidance for quantum radar to detect targets with dihedral.
Psychological Safety in Agile Software Development Teams: Work Design Antecedents and Performance Consequences

Psychological safety has been postulated as a key factor for the success of agile software development teams, yet there is a lack of empirical studies investigating the role of psychological safety in this context.The present study examines how work design characteristics of software development teams (autonomy, task interdependence, and role clarity) influence psychological safety and, further, how psychological safety impacts team performance, either directly or indirectly through team reflexivity.We test our model using survey data from 236 team members in 43 software development teams in Norway.Our results show that autonomy boosts psychological safety in software teams, and that psychological safety again has a positive effect on team reflexivity and a direct effect on team performance.
Towards the Representation of Network Assets in Health Care Environments Using Ontologies

The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies.Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network.As a result of this work, the "Software Defined Networking Description Language-CUREX Asset Discovery Tool Ontology" (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc. CONCLUSION: The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.
Learning various length dependence by dual recurrent neural networks

Recurrent neural networks (RNNs) are widely used as a memory model for sequence-related problems. Many variants of RNN have been proposed to solve the gradient problems of training RNNs and process long sequences. Although some classical models have been proposed, capturing long-term dependence while responding to short-term changes remains a challenge. To address this problem, we propose a new model named Dual Recurrent Neural Networks (DuRNN). The DuRNN consists of two parts to learn the short-term dependence and progressively learn the long-term dependence. The first part is a recurrent neural network with constrained full recurrent connections to deal with short-term dependence in sequence and generate short-term memory. Another part is a recurrent neural network with independent recurrent connections which helps to learn long-term dependence and generate long-term memory. A selection mechanism is added between two parts to transfer the needed long-term information to the independent neurons. Multiple modules can be stacked to form a multi-layer model for better performance. Our contributions are: 1) a new recurrent model developed based on the divide-and-conquer strategy to learn long and short-term dependence separately, and 2) a selection mechanism to enhance the separating and learning of different temporal scales of dependence. Both theoretical analysis and extensive experiments are conducted to validate the performance of our model. Experimental results indicate that the proposed DuRNN model can handle not only very long sequences (over 5,000 time steps), but also short sequences very well.
DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings

Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability.Conventional approaches employ the siamese-network for this task, which obtains the sentence embeddings through modeling the context-response semantic relevance by applying a feed-forward network on top of the sentence encoders.However, as the semantic textual similarity is commonly measured through the element-wise distance metrics (e.g.cosine and L2 distance), such architecture yields a large gap between training and evaluating.In this paper, we propose DialogueCSE, a dialogue-based contrastive learning approach to tackle this issue.DialogueCSE first introduces a novel matching-guided embedding (MGE) mechanism, which generates a contextaware embedding for each candidate response embedding (i.e. the context-free embedding) according to the guidance of the multi-turn context-response matching matrices.Then it pairs each context-aware embedding with its corresponding context-free embedding and finally minimizes the contrastive loss across all pairs.We evaluate our model on three multi-turn dialogue datasets: the Microsoft Dialogue Corpus, the Jing Dong Dialogue Corpus, and the E-commerce Dialogue Corpus.Evaluation results show that our approach significantly outperforms the baselines across all three datasets in terms of MAP and Spearman's correlation measures, demonstrating its effectiveness.Further quantitative experiments show that our approach achieves better performance when leveraging more dialogue context and remains robust when less training data is provided.
Joint Inference of Multiple Graphs with Hidden Variables from Stationary Graph Signals

Learning graphs from sets of nodal observations represents a prominent problem formally known as graph topology inference. However, current approaches are limited by typically focusing on inferring single networks, and they assume that observations from all nodes are available. First, many contemporary setups involve multiple related networks, and second, it is often the case that only a subset of nodes is observed while the rest remain hidden. Motivated by these facts, we introduce a joint graph topology inference method that models the influence of the hidden variables. Under the assumptions that the observed signals are stationary on the sought graphs and the graphs are closely related, the joint estimation of multiple networks allows us to exploit such relationships to improve the quality of the learned graphs. Moreover, we confront the challenging problem of modeling the influence of the hidden nodes to minimize their detrimental effect. To obtain an amenable approach, we take advantage of the particular structure of the setup at hand and leverage the similarity between the different graphs, which affects both the observed and the hidden nodes. To test the proposed method, numerical simulations over synthetic and real-world graphs are provided.
Automated Malaria Cells Detection from Blood Smears Under Severe Class Imbalance via Importance-Aware Balanced Group Softmax

Malaria is one of the main threats to global health. Manual examination of thick and thin blood smears is the current gold standard for diagnosing malaria. However, it is of extremely low throughput and susceptible to human bias, and hence, automated detection tools are highly demanded in practice. Developing an automated detection algorithm is a quite challenging due to (1) the wide range of variations in bright field microscopy images, and (2) more importantly, the severe class imbalance problem in this task. While recently proposed balanced group softmax is somehow able to alleviate the problem of class imbalance, the crucial prerequisite for its success is that the samples can be correctly categorized into different classes. We present a novel importance-aware BGS (IaBGS) to address the class imbalance problem and thereby improve the detection performance. Our main idea is to introduce a relation module (RM) before the group softmax module in the network to learn the relationships between different cells. We then figure out the feature of a cell by considering the relationships between this cell and other cells in the input image with different cells having different learned weights. In the RM module, we leverage both the appearance features and locations to calculate the feature of each cell to take full advantage of the relationships to obtain more discriminative features for BGS. We conducted extensive experiments on a famous dataset to evaluate the proposed IaBGS. Experimental results demonstrate the effectiveness of the proposed approach, consistently outperforming state-of-the-art methods. Codes will be released upon publication.
I-DLV-sr: A Stream Reasoning System based on I-DLV

Abstract We introduce a novel logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the $${{\mathcal I}^2}$$ - DLV system. The architecture allows to take advantage from both the powerful distributed stream processing capabilities of Flink and the incremental reasoning capabilities of $${{\mathcal I}^2}$$ - DLV , based on overgrounding techniques. Besides the system architecture, we illustrate the supported input language and its modeling capabilities, and discuss the results of an experimental activity aimed at assessing the viability of the approach.
Digital Learning for Adults in the Context of Education Market Development

The article identifies the phenomenon of education digitalization and the transition to new methods of adult education, namely e-learning based on various educational platforms. Nowadays, the most important transformation of the education market takes place – the transition from offline to online mode. Global digitalization has a significant impact on increasing the requirements to adults. A person who does not have modern digital technologies and cannot use them in his or her professional and daily activities can simply get lost in the flow of information and advanced technologies. It highlights the problem of increasing the digital competence of an adult. The article analyzes the relationship between the concept of "digital education" and those related to it using Vosviewer software. The study deals with the problem regarding the formation and development of one of the key competencies - digital adult education. The article examines the number of publications on the keywords "digital education", identifies trends in investment in digital education at the enterprise.
Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations

We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images. Unlike most existing methods that require voxel-wise densely labeled training data, our weakly-supervised CIVA-Net is the first model that only needs image-level class labels as guidance to learn accurate volumetric segmentation. Our model learns from cross-image co-occurrence for integral region generation, and explores inter-voxel affinity relations to predict segmentation with accurate boundaries. We empirically validate our model on both simulated and real cryo-ET datasets. Our experiments show that CIVA-Net achieves comparable performance to the state-of-the-art models trained with stronger supervision.
A Multiplier-Less Convolutional Neural Network Inference Accelerator for Intelligent Edge Devices

As the demand for neural network operations on edge devices increases, energy-efficient neural network inference solutions become necessary. To this end, this paper proposes a compact 4-bit number format (SD4) for neural network weights. In addition to significantly reducing the amount of neural network data transmission, SD4 also reduces the neural network convolution operation from multiplication and addition (MAC) to only addition. MNIST and CIFAR-10 CNNs with SD4 weights achieve results similar to their FP32-trained counterparts. The difference between the top-1 accuracy of 4-bit ResNet CNN for ImageNet and the baseline FP32 CNN is less than 0.5%. In the hardware design, we have implemented a multiplier-less convolution acceleration circuit. Compared with the 8-bit weight circuit, the power consumption and area of a 4-bit <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> convolution circuit are reduced by nearly 50%. This work also proposes a systematic CNN deployment solution consisting of software CNN training and hardware acceleration. The proposed FPGA-based accelerator for VGG7 image classification achieves a peak throughput of 345.6 GOPS when running at a 100-MHz clock rate. The proposed convolution accelerator’s power consumption and energy efficiency are 1.19W and 289. 5 GOPS/W, respectively. Compared to the CPU implementation of VGG7-128 inference, the multiplier-less acceleration circuit is 4.8 times faster and achieves 384 times higher energy efficiency.
Subgraph anomaly detection in financial transaction networks

Effective anomaly detection is crucial for the success of many AI-based solutions in the financial domain, including e.g. fraud detection and risk modeling. Identifying anomaly from financial transaction networks is one of the challenging tasks that can be cast as a special instance of anomaly detection in networks. Existing methods typically attempt to detect only node-level anomalies, and assume prior knowledge to extract representative features for identifying anomalies. However, there exist collective fraudulent behaviors at the level of subgraphs rather than individual node. A ring structure for money laundering and a tree structure for pyramid schemes would be common examples. Also, in practice it is difficult to decide which features are more representative beforehand. In this paper, we introduce SADE (Subgraph Anomaly DEtection) framework that addresses these needs. SADE consists of two steps: 1) role-guided subgraph embedding, and 2) subgraph anomaly detection. Our approach for learning the subgraph embeddings allows to preserve both the local structure of subgraphs and the global structure of entire network by making use of global roles and local connections of nodes. The learnt representation allows effective use of the state of art anomaly detection approaches. Our extensive experiments on synthetic and real-world financial transaction networks demonstrate the effectiveness of SADE in learning subgraph embeddings without requiring any prior knowledge and detecting anomalous subgraphs.