Reviewerly/exHarmony huggingface dataset:
Pricing strategies for blockchain payment service under customer heterogeneity

The growing popularity of cryptocurrency payment among supply chain members have resulted in considerable waiting in blockchain payment services. In response, service providers offer “priority payment” which allows users to pay additional transaction fee in exchange for priority transaction processing. Despite its growing popularity, there is very limited formal research that studies this service and its impact. In this paper, we examine the impact of the priority option on SC members' blockchain payment service adoption behavior, payment service provider's strategies, as well as his revenue generation. We find that offering priority blockchain payment option increases heavy members' usage of the option and reduces their usage of the waiting blockchain payment option. On the other hand, it boosts certain light players' usage of both blockchain payment services when the transaction fee rate is not too high. The service provider should charge a lower (higher) transaction fee rate if the trust-enabling rate of the priority blockchain payment option is relatively high (low). Finally, we demonstrate that offering priority blockchain payment option actually reduces his revenue when the trust-enabling rate of the option is sufficiently low. Thus, blindly charging transaction fees actually does more harm than good to the service providers.
Multimodal particle swarm optimization for feature selection

The purpose of feature selection (FS) is to eliminate redundant and irrelevant features and leave useful features for classification, which can not only reduce the cost of classification, but also improve the classification accuracy. Existing algorithms mainly focus on finding one best feature subset for an optimization target or some Pareto solutions that best fit multiple targets, neglecting the fact that the FS problem may have more than one best feature subset for a single target. In fact, diffident feature subsets are likely to exhibit similar classification ability, so the FS problem is also a multimodal optimization problem. This paper firstly attempts to study the FS problem from the perspective of multimodal optimization. A novel multimodal niching particle swarm optimization (MNPSO) algorithm, aiming at finding out all the best feature combinations in a FS problem is proposed. Unlike traditional niching methods, the proposed algorithm uses the Hamming distance to measure the distance between any two particles. Two niching updating strategies are adopted for multimodal FS, and the two proposed variants of MNPSO are termed MNPSO-C (using crowding clustering) and MNPSO-S (using speciation clustering) respectively. To enable the particles in the same niche to exchange information properly, the particle velocity update is modified based on the best particle in the niche instead of the traditional globally best one. An external archive is applied to store the feature subsets with the highest classification accuracy. Datasets with various dimensions of attributes have been tested. Particularly, the number of multimodal solutions and the successful rates of the proposed algorithms have been extensively analyzed and compared with the state-of-the-art algorithms. The experimental results show that the proposed algorithms can find more multimodal feature solutions and have advantages in classification accuracy.
Will bots take over the supply chain? Revisiting agent-based supply chain automation

Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since early 2000; industrial uptake of them has been lagging. The reasons quoted include the immaturity of the technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.
SNIFF: Reverse Engineering of Neural Networks With Fault Attacks

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a change of value that is currently being computed. They can be realized by various fault injection techniques, ranging from clock/voltage glitching to application of lasers to rowhammer. In this paper we explore the possibility to reverse engineer neural networks with the usage of fault attacks. SNIFF stands for sign bit flip fault, which enables the reverse engineering by changing the sign of intermediate values. We develop the first exact extraction method on deep-layer feature extractor networks that provably allows the recovery of the model parameters. Our experiments with Keras library show that the precision error for the parameter recovery for the tested networks is less than $10^{-13}$ with the usage of 64-bit floats, which improves the current state of the art by 6 orders of magnitude. Additionally, we discuss the protection techniques against fault injection attacks that can be applied to enhance the fault resistance.
LTSM: Lightweight and Trusted Sharing Mechanism of IoT Data in Smart City

With the development of smart cities, the chimney construction method can no longer meet service needs. It is extremely urgent to build a unified urban brain, and the core issue is data sharing and fusion. Aiming at the problems of data island, data leakage, and high trust cost in the IoT of the smart city, a lightweight and trusted sharing mechanism (LTSM) is proposed. First, the blockchain is combined with federated learning to realize the data sharing, which not only protects the private data, but also ensures the sharing process trust. Then, a node selection algorithm based on credit value and a node evaluation algorithm based on smart contract are designed to improve the quality of federated learning. Finally, we propose an improved raft consensus to meet the delay and security requirements of the consortium blockchain in the smart city scenario. In the simulation, we evaluate the federated learning algorithm, the node selection algorithm, and the improved raft consensus, respectively. The experimental results show that the LTSM mechanism has a good application value. The federated learning model has a better accuracy, but its training time is also longer. The node selection algorithm is helpful to improve the accuracy of the federated learning model. The improved raft consensus improves the throughput.
Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging

In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians often need corresponding images without abnormal findings of interest or, conversely, images that contain similar abnormal findings regardless of normal anatomical context. This is called comparative diagnostic reading of medical images, which is essential for a correct diagnosis. To support comparative diagnostic reading, content-based image retrieval (CBIR) that can selectively utilize normal and abnormal features in medical images as two separable semantic components will be useful. In this study, we propose a neural network architecture to decompose the semantic components of medical images into two latent codes: normal anatomy code and abnormal anatomy code. The normal anatomy code represents counterfactual normal anatomies that should have existed if the sample is healthy, whereas the abnormal anatomy code attributes to abnormal changes that reflect deviation from the normal baseline. By calculating the similarity based on either normal or abnormal anatomy codes or the combination of the two codes, our algorithm can retrieve images according to the selected semantic component from a dataset consisting of brain magnetic resonance images of gliomas. Moreover, it can utilize a synthetic query vector combining normal and abnormal anatomy codes from two different query images. To evaluate whether the retrieved images are acquired according to the targeted semantic component, the overlap of the ground-truth labels is calculated as metrics of the semantic consistency. Our algorithm provides a flexible CBIR framework by handling the decomposed features with qualitatively and quantitatively remarkable results.
YOLODrone: Improved YOLO Architecture for Object Detection in Drone Images

Recent advances in robotics and computer vision fields yield emerging new applications for camera equipped drones. One such application is aerial-based object detection. However, despite the recent advances in the relevant literature, object detection remains as a challenging task in computer vision. Existing object detection algorithms demonstrate even lower performance on drone (or aerial) images since the object detection problem is a more challenging problem in aerial images, when compared to the detection task in ground-taken images. There are many reasons for that including: (i) the lack of large drone datasets with large object variance, (ii) the larger variance in both scale and orientation in drone images, and (iii) the difference in shape and texture features between the ground and the aerial images. In this paper, we introduce an improved YOLO algorithm: YOLODrone for detecting objects in drone images. We evaluate our algorithm on VisDrone2019 dataset and report improved results when compared to YOLOv3 algorithm.
PriScore: Blockchain-Based Self-Tallying Election System Supporting Score Voting

Election and voting play crucial roles in democratic society for an elactorate to make a collective decision. E-voting is one of the most challenging problems in cryptographic research to provide multiple dimensions security assurances. In this paper, we study an important voting paradigm, score voting, with privacy protection, which has not been investigated in previous work. We propose a blockchain based self-tallying election system to support score voting, dubbed “PriScore”, where the ballots are recorded on blockchain to prevent vote forgery or tampering. PriScore makes it possible for each voter to assign different evaluation scores (within a certain range) for the candidates as ranked-choice, where the sum of the scores in each ballot should be a predefined constant, and the evaluation scores are encrypted to maintain confidentiality. A major challenge in score voting is to simultaneously prove two constraint conditions: range proof and sum proof. We introduce a new technique, called dual zero-knowledge proof (dual-ZKP), to prove the scores satisfying two crucial requirements, which integrates “1-out-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> ” proof and distributed ElGamal crypto in a non-trivial way. The self-tallying mechanism in PriScore enables any party in the system to calculate and verify the election result, which provides fairness, dispute-freeness. The security analysis demonstrates that PriScore achieves completeness, soundness, eligibility, universal/individual verifiability and multiple-voting detection. We evaluate the performance of PriScore on modern workbench to test the performance, and also on a blockchain platform to measure the resource consumption. The experiments show that PriScore preserves privacy of score voting with reasonable overheads.
Digital evidence: Unaddressed threats to fairness and the presumption of innocence

Contemporary criminal investigation assisted by computing technology imposes challenges to the right to a fair trial and the scientific validity of digital evidence. This paper identifies three categories of unaddressed threats to fairness and the presumption of innocence during investigations – (i) the inappropriate and inconsistent use of technology; (ii) old procedural guarantees, which are not adapted to contemporary digital evidence processes and services; (iii) and the lack of reliability testing in digital forensics practice. Further, the solutions that have been suggested to overcome these issues are critically reviewed to identify their shortcomings. Ultimately, the paper argues for the need of legislative intervention and enforcement of standards and validation procedures for digital evidence in order to protect innocent suspects and all parties in the criminal proceedings from the negative consequences of technology-assisted investigations.
When LoRa Meets EMR: Electromagnetic Covert Channels Can Be Super Resilient

Due to the low power of electromagnetic radiation (EMR), EM convert channel has been widely considered as a short-range attack that can be easily mitigated by shielding. This paper overturns this common belief by demonstrating how covert EM signals leaked from typical laptops, desktops and servers are decoded from hundreds of meters away, or penetrate aggressive shield previously considered as sufficient to ensure emission security. We achieve this by designing EMLoRa – a super resilient EM covert channel that exploits memory as a LoRa-like radio. EMLoRa represents the first attempt of designing an EM covert channel using state-of-the-art spread spectrum technology. It tackles a set of unique challenges, such as handling complex spectral characteristics of EMR, tolerating signal distortions caused by CPU contention, and preventing adversarial detectors from demodulating covert signals. Experiment results show that EMLoRa boosts communication range by 20x and improves attenuation resilience by up to 53 dB when compared with prior EM covert channels at the same bit rate. By achieving this, EMLoRa allows an attacker to circumvent security perimeter, breach Faraday cage, and localize air-gapped devices in a wide area using just a small number of inexpensive sensors. To countermeasure EMLoRa, we further explore the feasibility of uncovering EMLoRa's signal using energy- and CNN-based detectors. Experiments show that both detectors suffer limited range, allowing EMLoRa to gain a significant range advantage. Our results call for further research on the countermeasure against spread spectrum-based EM covert channels.
A GWO-based multi-robot cooperation method for target searching in unknown environments

To solve static and dynamic target searching problems involving multiple robots in unknown environments, a novel adaptive robotic grey wolf optimizer (GWO) algorithm, named the RGWO, is proposed. First, an optimal learning strategy is introduced to improve the position updating formula of the GWO to make the algorithm suitable for use in actual mobile situations involving robots, allowing the searching robots to move towards the target (prey) in a step-by-step manner. Then, an adaptive inertial weighting scheme is adopted. By increasing the “aggregation degree” or decreasing the “evolution speed”, the influence of the inertial weight can be increased, which is helpful for maintaining the search diversity of the robots and avoiding premature convergence. In addition, due to the ability of the prey to escape, the pursuing robots are likely to fall into local optima. To avoid this issue, an adaptive speed adjustment strategy and an escape mechanism are adopted. The RGWO is verified and compared with other methods. The RGWO has obvious advantages over other methods in terms of the number of required iterations, success rate and efficiency, and it is superior in both static and dynamic target searching. However, the search trajectories generated with the RGWO are not smoother than those generated with the other investigated methods.
Implementation of Dlib Deep Learning Face Recognition Technology

In order to overcome the problems of OpenCV in face detection, such as missing detection, false detection and poor recognition effect, a new method of Dlib face recognition based on ERT algorithm is proposed. This method can realize face recognition and feature calibration by Python, which calls a large number of trained face model interfaces, and it has good robustness for occlusion. By testing the process of face detection, feature point calibration, feature vector extraction and comparison in small deflections and positive faces images and videos, the experimental results show that the proposed method is superior to OpenCV method, it can effectively improve detection sensitivity, recognition precision and recognition effect. It can effectively solve the problem of poor real-time performance in dynamic image recognition.
A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code).We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA.Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated.Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization

Traditional video summarization methods generate fixed video representations regardless of user interest. Therefore such methods limit users' expectations in content search and exploration scenarios. Multi-modal video summarization is one of the methods utilized to address this problem. When multi-modal video summarization is used to help video exploration, a text-based query is considered as one of the main drivers of video summary generation, as it is user-defined. Thus, encoding both the text-based query and the video effectively is important for the task of multi-modal video summarization. In this work, a new method is proposed that uses a specialized attention network and contextualized word representations to tackle this task. The proposed model consists of a contextualized video summary controller, multi-modal attention mechanisms, an interactive attention network, and a video summary generator. Based on the evaluation of the existing multi-modal video summarization benchmark, experimental results show that the proposed model is effective with the increase of +5.88% in accuracy and +4.06% increase of F1-score, compared with the state-of-the-art method. https://github.com/Jhhuangkay/GPT2MVS-Generative-Pre-trained-Transformer-2-for-Multi-modal-Video-Summarization.
Self-supervised Product Quantization for Deep Unsupervised Image Retrieval

Supervised deep learning-based hash and vector quantization are enabling fast and large-scale image retrieval systems. By fully exploiting label annotations, they are achieving outstanding retrieval performances compared to the conventional methods. However, it is painstaking to assign labels precisely for a vast amount of training data, and also, the annotation process is error-prone. To tackle these issues, we propose the first deep unsupervised image retrieval method dubbed Self-supervised Product Quantization (SPQ) network, which is label-free and trained in a self-supervised manner. We design a Cross Quantized Contrastive learning strategy that jointly learns codewords and deep visual descriptors by comparing individually transformed images (views). Our method analyzes the image contents to extract descriptive features, allowing us to understand image representations for accurate retrieval. By conducting extensive experiments on benchmarks, we demonstrate that the proposed method yields state-of-the-art results even without supervised pretraining.
Communication-efficient asynchronous federated learning in resource-constrained edge computing

Federated learning (FL) has been widely used to train machine learning models over massive data in edge computing. However, the existing FL solutions may cause long training time and/or high resource (e.g., bandwidth) cost, and thus cannot be directly applied for resource-constrained edge nodes, such as base stations and access points. In this paper, we propose a novel communication-efficient asynchronous federated learning (CE-AFL) mechanism, in which the parameter server will aggregate the local model updates only from a certain fraction α, with 0<α<1, of all edge nodes by their arrival order in each epoch. As a case study, we design efficient algorithms to determine the optimal value of α for two cases of CE-AFL, single learning task and multiple learning tasks, under bandwidth constraints. We formally prove the convergence of the proposed algorithm. We evaluate the performance of our algorithm with experiments on Jetson TX2, deep learning workstation and extensive simulations. Both experimental results and simulation results on the classical models and datasets show the effectiveness of our proposed mechanism and algorithms. For example, CE-AFL can reduce the training time by about 69% while achieving similar accuracy, and improve the accuracy of the trained models by about 18% under resource constraints, compared with the state-of-the-art solutions.