Reviewerly/exHarmony huggingface dataset:
Causal Discovery with Flow-based Conditional Density Estimation

Causal-effect discovery plays an essential role in many disciplines of science and real-world applications. In this paper, we introduce a new causal discovery method to solve the classic problem of inferring the causal direction under a bivariate setting. In particular, our proposed method first leverages a flow model to estimate the joint probability density of the variables. Then we formulate a novel evaluation metric to infer the scores for each potential causal direction based on the variance of the conditional density estimation. By leveraging the flow-based conditional density estimation metric, our causal discovery approach alleviates the restrictive assumptions made by the conventional methods, such as assuming the linearity relationship between the two variables. Therefore, it could potentially be able to better capture the complex causal relationship among data in various problem domains that comes in arbitrary forms. We conduct extensive evaluations to compare our method with decent causal discovery approaches. Empirical results show that our method could promisingly outperform the baseline methods with noticeable margins on both synthetic and real-world datasets.
FAIR: A Blockchain-based Vaccine Distribution Scheme for Pandemics

Demand forecasting, supply acquisition in healthcare supply chains are significant concerns spanning various organizations and bodies, rendering a crucial backbone to medical services necessary for everyday living. A global pandemic resulting in critical demand for medications and vaccines was an eye-opener in the current era. The intrinsic complexity among the bodies involved in the supply chain results in a lack of data transparency, security, privacy, and reliable communication. The counterfeited drug is an outcome of such limitations that adversely affects a global population. Consecutively, fair allocation and distribution of drugs and vaccines to administer them to a global mass equally is also a significant concern. Blockchain as technology grants an essential platform to track and manage transactions among communicating parties in the supply chain using a peer-to-peer, secured, distributed ledger, removing the need for intermediaries or entrusted third parties. Most existing studies focus on tracking and tracing supply chain systems in a centralized manner, leading to transparency, authenticity, data privacy, and authenticity concerns in healthcare supply chains. In this article, we propose a FAIR blockchain-based approach deploying smart contracts leading to transparent traceability of data and transactions in the healthcare supply chain between the communicating parties We propose an approach that allows fair allocation and distribution of vaccines as per the demand generated from the global population. We present a system architecture and algorithm representing the communication between parties that governs our proposed approach. We have computed network performance based and blockchain based evaluation of the proposed system. We have calculated the communication and computation cost of 1152 bits and 12.6 ms respectively.
How to Avoid Zero-Spacing in Fractionally-Strided Convolution? A Hardware-Algorithm Co-Design Methodology

Fractionally Strided Convolution (FSC) is a key operation in popular image-based Deep Learning models, for example, back propagation in CNN training, the decoding stage of convolutional auto-encoders and generative CNNs (GAN), etc. FSC typically performs up-convolution on a 2-D grid image, i.e., expands it to a larger one, as compared to conventional (down)-convolution, resulting in more complex computation patterns. Specifically, it introduces additional interleaved zero-spacing (i.e. insertion and padding of zeros) in feature maps that impose excessive computation and memory access overheads on traditional convolution methods such as im2col. The resulting hardware under-utilization is especially severe in layers with large kernels and large strides, commonly seen in typical CNNs and Generative CNNs. In this paper, we propose a methodology to address this challenge using a multi-channel-multi-kernel parallel algorithm, kn2row, to eliminate zero-computations in FSC. We further develop a unified accelerator for kn2row-based convolution and FSC operations in High-Level Synthesis (HLS). Benefiting from the compute-reduction of kn2row, we achieve up to 14.6x improvement in effective resource utilization in typical convolutional auto-decoding layers, GAN layers and backward pass of Nature-CNN, a reinforcement learning bench-marking model. These lead to overall speedup of up to 3.8x in the complete forward or backward propagation phases of the above benchmarks. Our methodology leads up to 8x speedup and 11x better power efficiency than general-purpose processors. Compared with existing GAN accelerators, our methodology achieves higher normalized throughput with high portability.
Patterns for visual programming

Software plays an increasingly important role in our daily lives. The development of this software is complex and hard to understand by non-professionals. Fundamental knowledge about programming languages and software structure is required to change it. By changing the logical complexity represented in software code in something more intuitively understandable, like graphical elements, even non-professionals can understand and learn to design software. Systems that are inspired by flow-based programming offer an interesting approach to this problem. This paper presents two pattern and five pattern candidates, which are a distillation from practices that are already in use. The presented pattern are Functions as building blocks and Connect building blocks. These pattern are used in established products like Node-Red[3] or n8n[2].
Towards Secure IoT-Based Payments by Extension of Payment Card Industry Data Security Standard (PCI DSS)

IoT emergence has given rise to a new digital experience of payment transactions where physical objects like refrigerators, cars, and wearables will make payments. These physical objects will be storing the cardholder credentials and will directly make payments with the vendors over insecure public networks. For such payment transactions, government regulations and standards organizations require to implement PCI DSS for adapting similar set of security measures at the global level. The current version of PCI DSS is not suitable for IoT-based payment systems due to characteristics of IoT such as resource-constrained nature of devices and updating software/firmware of so many physical devices. Also, there arises an emergent need of implementing PCI DSS requirements and assessments for security of all stakeholders that store or process the user credentials in a payment. This paper is an initial effort to bring the researcher’s attention to make upcoming versions of PCI DSS suitable for IoT and thus securing the new ways of IoT-based payment systems. The paper has reviewed the traditional payment process along with considerations for IoT-based payment systems to make recommendations to modify the PCI DSS in a suitable way for IoT.
A single-layer asymmetric RNN with low hardware complexity for solving linear equations

A single layer neural network for the solution of linear equations is presented. The proposed circuit is based on the standard Hopfield model albeit with the added flexibility that the interconnection weight matrix need not be symmetric. This results in an asymmetric Hopfield neural network capable of solving linear equations. PSPICE simulation results are given which verify the theoretical predictions. A simple technique to incorporate re-configurability into the circuit for setting the different weights of the interconnection is also included. Experimental results for circuits set up to solve small problems further confirm the operation of the proposed circuit.
Fine-grained Human Analysis under Occlusions and Perspective Constraints in Multimedia Surveillance

Human detection in the wild is a research topic of paramount importance in computer vision, and it is the starting step for designing intelligent systems oriented to human interaction that work in complete autonomy. To achieve this goal, computer vision and machine learning should aim at superhuman capabilities. In this work, we address the problem of fine-grained human analysis under occlusions and perspective constraints. More specifically, we discuss some issues and some possible solutions to effectively detect people using pose estimation methods and to detect humans under occlusions both in the two-dimensional (2D) image plane and in the 3D space exploiting single monocular cameras. Dealing with occlusion can be done at the joint level or pixel level: We discuss two different solutions, the former based on a supervised neural network architecture for detecting occluded joints and the latter based on a semi-supervised specialized GAN that exploits both appearance and human shape attributes to determine the missing parts of the visible shape. To deal with perspective constraints, we further discuss a neural approach based on a double architecture that learns to create an optimal neural representation, which is useful to reconstruct the 3D position of human keypoints starting with simple RGB images. All these approaches have a critical point in common: the need for large annotated datasets. To have large, fair, consistent, transparent, and ethical datasets, we propose the adoption of synthetic datasets as, for example, JTA and MOTSynth. In this article, we discuss the pros and cons of using synthetic datasets while tackling several human-centered AI issues with respect to European GDPR rules for privacy. We further explore and discuss an application in the field of risk assessment by space occupancy estimation during the COVID-19 pandemic called Inter-Homines.
Blockchain-Enabled Parallel Learning in Industrial Edge-Cloud Network: a Fuzzy DPoSt-PBFT Approach

Recently, parallel reinforcement learning (PRL) based Industrial Internet of Things (IIoT) edge-cloud resource scheduling has elicited escalating attention. However, with the scale of IIoT expands, there are several challenges in the existing researches: 1) large number of parallel servers slows down the convergence rate of PRL; 2) malicious parallel server affects resource allocation efficiency. In order to solve the above efficiency and security problem, blockchain-based approaches are introduced in PRL based resource allocation problem. However, traditional consensus algorithm in blockchain is not suitable for resource allocation and is inefficient. Thus, in this article, based on a novel fuzzy delegated proof of state and practical byzantine fault tolerance (fuzzy DPoSt+PBFT) consensus algorithm, we propose a blockchain-enabled collaborative parallel Q-learning (CPQL) approach to address the above challenges. To be specific, we first construct an edge-cloud collaborative architecture for executing the diversity intelligence IIoT applications. Then, we propose a CPQL algorithm for edge-cloud resource allocation and choosing the optimal number of parallel edge servers to speed up the Q-table training. In the Q-table aggregation process in CPQL, a fuzzy DPoSt+PBFT algorithm is designed for secure CPQL training and efficient consensus. Experimental results show the superior performance of the proposed approach. And the proposed approach has great potential in IIoT resource allocation problem.
Named Entity Recognition for Chinese Aviation Security Incident Based on BiLSTM and CRF

Targeted at the issue of automatic acquisition of aviation security event entities. In this paper, the entity recognition model of aviation security incident based on character vector BiLSTM and CRF is constructed. In order to avoid word segmentation errors, character vectors are used in this paper. The sentence vector is obtained by PV -DM, and then the character vector and sentence vector are fused to make full use of the lexical information in the text. Then input BiLSTM to obtain the context characteristics of the text, and ensure the consistency of the output entity label through CRF. Finally, a data set of aviation security incidents is constructed, and the experimental results show that the proposed method improves the effectiveness of entity identification in the field of aviation security incidents.
Characterization of Deep Learning-Based Aerial Explosive Hazard Detection using Simulated Data

Automatic object detection is one of the most common and fundamental tasks in computational intelligence (CI). Neural networks (NNs) are now often the tool of choice for this task. Unlike more traditional approaches that have interpretable parameters, explaining what a NN has learned and characterizing under what conditions the model does and does not perform well is a challenging, yet important task. The most straightforward approach to evaluate performance is to run test imagery through a model. However, the gaining popularity of self-supervised methods among big players such as Tesla and Google serve as evidence that labeled data is scarce in real-world settings. On the other hand, modern high-fidelity graphics simulation is now accessible and programmable, allowing for generation of large amounts of accurately labeled training and testing data for CI. Herein, we describe a framework to assess the performance of a NN model for automatic explosive hazard detection (EHD) from an unmanned aerial vehicle using simulation. The data was generated by the Unreal Engine with Microsoft's AirSim plugin. A workflow for generating simulated data and using it to assess and understand strengths and weaknesses in a learned EHD model is demonstrated.
SIoVChain: Efficient and Secure Blockchain Based Internet of Vehicles (IoV)

As the backbone of smart cities, the IoV can greatly enhance transportation effectiveness, passenger and driver safety, energy use reduction, traffic management, and pollution control. Although, the storage and communication between ubiquitous devices such as the vehicle and Road-Side-Unit (RSU) lead to multiple potential challenges, such as data tampering, data integrity, cyber attacks, and risk of leaking or falsifying information. In this work, we have proposed a permissioned blockchain framework for the IoV (i.e., SIoVChain) to overcome insecure data communication and inefficient data storage. SIoVChain registers ubiquitous devices with a node such as a blockchain node and works with transaction verification and ordering node selection processes. SIoVChain directly benefits the drivers and passengers by providing traffic information through secure communication and data integrity. We have implemented SIoVChain on the Raspberry Pi 4 platform. The experimental results demonstrate that SIoVChain surpasses conventional solutions by 50% verification delay, 50% cryptography latency, and 49% communication overhead.
Secure AI and Blockchain-enabled Framework in Smart Vehicular Networks

In recent years, Internet of Things (IoT) devices such as drones, smartphones, and smart vehicles have increased, and Smart Vehicular Networks (SVNs) have formed. SVN are one of the vital components in smart cities and improves their functionality and efficiency. Despite the many applications that SVN have shown, they have security vulnerabilities issues, and there are many reports of them being hacked. Most attacks on SVN occur due to wireless communications and information sharing between vehicles. Also, due to the communication among vehicles, failures can spread from the victim device to the entire network and cause widespread damages. SVN features include mobility, decentralization, resource constraints that make traditional security solutions unsuitable for it. We propose a secure framework using blockchain and Deep Neural networks (DNN) to address these challenges. In this framework, we consider cluster-based architecture that vehicles in each cluster can securely communicate using the blockchain. Also, DNN is adopted to detect abnormal vehicles that have been attacked using their network traffic analysis in each zone. We assess blockchain impacts on throughput using different miners with multiple block sizes in our proposed framework. The experimental result indicates the throughput improvement with an increase in miners and block size. In addition, we evaluated the DNN performance for abnormal vehicle detection, which represents 99.82% in terms of accuracy.
Generalized Automated Energy Function Selection for Protein Structure Prediction on 2D and 3D HP Models

The present work applies Algorithm Selection for automatically determining the best energy functions for search algorithms on Protein Structure Prediction. Protein Structure Prediction is a critical problem concerned with exploring the structure of a protein, given an amino acid sequence. For the sake of improved search performance, various models have been introduced such as the Hydrophobic Polar (HP) models. These models make PSP computationally approachable. There exists a large suite of algorithms introduces to solve PSP. As in all other problem domains, it is possible to see that the PSP algorithms considered to be strong can fail to address some problem instances. One way of providing further performance improvements without devising a new algorithm is to automatically determine the best possible algorithm for a given PSP problem instance. Algorithm Selection focuses on automatically choosing algorithms for any given problem solving scenario. Unlike the traditional use of AS, this study accommodates Algorithm Selection for specifying the best suited energy function used for search. The process is designed as a rank prediction task on 7 energy functions for Iterated Local Search as the optimization algorithm. The idea is to assess the best energy function that can guide the search process of Iterated Local Search on a per-instance basis. An experimental analysis is reported on 30 PSP instances, each half belongs to the 2D and 3D HP models, respectively. The results show that Algorithm Selection is capable to offer proper selection of the energy functions with robustness. Additionally, a brief algorithm and instance analysis is reported on the instance hardness and matching algorithmic capabilities. This analysis further provides insights on the instance hardness besides instance and algorithm similarities.
Exploring a New Security Framework for Future Healthcare Systems

The Internet of Things is driving impactful and significant changes in healthcare systems across the globe. The use of mobile and wireless technologies to support healthcare environments has enormous potential to transform healthcare. For example, healthcare data, which is considered to be very sensitive, must be securely accessed, processed and stored. How-ever, digital healthcare IT platforms are increasingly coming under attack by malware such as Ransomware. In addition, there is now a need to integrate eHealth and mHealth mechanisms into national healthcare systems. New technologies, such as blockchain, are being used to address these issues. What is needed is a new framework which can use these technologies to secure healthcare. This paper proposes a new security framework that responds to these security concerns. The framework is then used to design an implementation framework with new mechanisms including Capabilities, Secure Remote Procedure Calls and a Service Management Framework.
Hybrid Evolutionary Approach to Multi-objective Path Planning for UAVs

The goal of Multi-Objective Path Planning (MOPP) is to find Pareto-optimal paths for autonomous agents with respect to several optimization goals like minimizing risk, path length, travel time, or energy consumption. In this work, we formulate a MOPP for Unmanned Aerial Vehicles (UAVs). We utilize a path representation based on Non-Uniform Rational B-Splines (NURBS) and propose a hybrid evolutionary approach combining an Evolution Strategy (ES) with the exact Dijkstra algorithm. Moreover, we compare our approach in a statistical analysis to state-of-the-art exact (Dijkstra's algorithm), gradient-based (L-BFGS-B), and evolutionary (NSGA-II) algorithms with respect to calculation time and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 2D urban path planning scenario based on real-world data exported from OpenStreetMap. The examination's results indicate that our approach is able to find significantly better solutions for the formulated problem than standard Evolutionary Algorithms (EAs). Moreover, the proposed method is able to obtain more diverse sets of trade-off solutions for different objectives than the standard exact approaches. Thus, the method combines the strengths of both approaches.
Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence

Convolutional Neural Networks (CNNs)-guided deep models have obtained impressive performance for image representation, however the representation ability may still be restricted and usually needs more epochs to make the model converge in training, due to the useful information loss during the convolution and pooling operations. We therefore propose a general feature recovery layer, termed Low-rank Deep Feature Recovery (LDFR), to enhance the representation ability of the convolutional features by seamlessly integrating low-rank recovery into CNNs, which can be easily extended to all existing CNNs-based models. To be specific, to recover the lost information during the convolution operation, LDFR aims at learning the low-rank projections to embed the feature maps onto a low-rank subspace based on some selected informative convolutional feature maps. Such low-rank recovery operation can ensure all convolutional feature maps to be reconstructed easily to recover the underlying subspace with more useful and detailed information discovered, e.g., the strokes of characters or the texture information of clothes can be enhanced after LDFR. In addition, to make the learnt low-rank subspaces more powerful for feature recovery, we design a fusion strategy to obtain a generalized subspace, which averages over all learnt sub-spaces in each LDFR layer, so that the convolutional feature maps in test phase can be recovered effectively via low-rank embedding. Extensive results on several image datasets show that existing CNNs-based models equipped with our LDFR layer can obtain better performance.
Adaptively sharing multi-levels of distributed representations in multi-task learning

In multi-task learning, the performance is often sensitive to the relationships between tasks. Thus it is important to study how to exploit the complex relationships across different tasks. One line of research captures the complex task relationships, by increasing the model capacity and thus requiring a large training dataset. However in many real-world applications, the amount of labeled data is limited. In this paper, we propose a light weight and specially designed architecture, which aims to model task relationships for small or middle-sized datasets. The proposed framework learns a task-specific ensemble of sub-networks in different depths, and is able to adapt the model architecture for the given data. The task-specific ensemble parameters are learned simultaneously with the weights of the network by optimizing a single loss function defined with respect to the end task. The hierarchical model structure is able to share both general and specific distributed representations to capture the inherent relationships between tasks. We validate our approach on various types of tasks, including synthetic task, article recommendation task and vision task. The results demonstrate the advantages of our model over several competitive baselines especially when the tasks are less-related.