Reviewerly/exHarmony huggingface dataset:
Item Relationship Graph Neural Networks for E-Commerce

In a modern e-commerce recommender system, it is important to understand the relationships among products. Recognizing product relationships-such as complements or substitutes-accurately is an essential task for generating better recommendation results, as well as improving explainability in recommendation. Products and their associated relationships naturally form a product graph, yet existing efforts do not fully exploit the product graph's topological structure. They usually only consider the information from directly connected products. In fact, the connectivity of products a few hops away also contains rich semantics and could be utilized for improved relationship prediction. In this work, we formulate the problem as a multilabel link prediction task and propose a novel graph neural network-based framework, item relationship graph neural network (IRGNN), for discovering multiple complex relationships simultaneously. We incorporate multihop relationships of products by recursively updating node embeddings using the messages from their neighbors. An edge relational network is designed to effectively capture relational information between products. Extensive experiments are conducted on real-world product data, validating the effectiveness of IRGNN, especially on large and sparse product graphs.
Trust but Verify: The Oracle Paradox of Blockchain Smart Contracts

ABSTRACT Although research shows that blockchain provides fairly immutable virtual provenance workflows, proof that the blockchain accurately represents physical events lacks truly independent verification. This dilemma, the Oracle Paradox, challenges blockchain architecture and is perhaps one reason why businesses have hesitated to adopt smart contracts. Blockchain proponents claim that people can serve as trusted Oracles in a smart contract. However, auditing research shows that people are the weak link in almost every internal control application, including those pertaining to blockchain. People are susceptible to collusion, bribery, error, and fraud and these tendencies are not entirely mitigated by blockchain technologies (Balagurusamy et al. 2019; Nakamoto 2008). This research proposes a framework to mitigate the paradox of the Oracle: A Business Process Management (BPM) model of a Blockchain Smart Contract-enabled supply chain with IoT as the sole “third-party” Oracle participant, utilizing design science research.
Elastic Deep Learning in Multi-Tenant GPU Clusters

We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i.e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster. Elasticity can benefit multi-tenant GPU cluster management in many ways, for example, achieving various scheduling objectives (e.g., job throughput, job completion time, GPU efficiency) according to cluster load variations, utilizing transient idle resources, and supporting performance profiling, job migration, and straggler mitigation. We propose EDL, which enables elastic deep learning with a simple API and can be easily integrated with existing deep learning frameworks such as TensorFlow and PyTorch. EDL also incorporates techniques that are necessary to reduce the overhead of parallelism adjustments, such as stop-free scaling and dynamic data pipeline. We demonstrate with experiments that EDL can indeed bring significant benefits to the above-listed applications in GPU cluster management.
Hierarchical Temporal Attention Network for Thyroid Nodule Recognition Using Dynamic CEUS Imaging

Contrast-enhanced ultrasound (CEUS) has emerged as a popular imaging modality in thyroid nodule diagnosis due to its ability to visualize vascular distribution in real time. Recently, a number of learning-based methods are dedicated to mine pathological-related enhancement dynamics and make prediction at one step, ignoring a native diagnostic dependency. In clinics, the differentiation of benign or malignant nodules always precedes the recognition of pathological types. In this paper, we propose a novel hierarchical temporal attention network (HiTAN) for thyroid nodule diagnosis using dynamic CEUS imaging, which unifies dynamic enhancement feature learning and hierarchical nodules classification into a deep framework. Specifically, this method decomposes the diagnosis of nodules into an ordered two-stage classification task, where diagnostic dependency is modeled by Gated Recurrent Units (GRUs). Besides, we design a local-to-global temporal aggregation (LGTA) operator to perform a comprehensive temporal fusion along the hierarchical prediction path. Particularly, local temporal information is defined as typical enhancement patterns identified with the guidance of perfusion representation learned from the differentiation level. Then, we leverage an attention mechanism to embed global enhancement dynamics into each identified salient pattern. In this study, we evaluate the proposed HiTAN method on the collected CEUS dataset of thyroid nodules. Extensive experimental results validate the efficacy of dynamic patterns learning, fusion and hierarchical diagnosis mechanism.
Can You Fake It Until You Make It?

The recent adoption of machine learning models in high-risk settings such as medicine has increased demand for developments in privacy and fairness. Rebalancing skewed datasets using synthetic data created by generative adversarial networks (GANs) has shown potential to mitigate disparate impact on minoritized subgroups. However, such generative models are subject to privacy attacks that can expose sensitive data from the training dataset. Differential privacy (DP) is the current leading solution for privacy-preserving machine learning. Differentially private GANs (DP GANs) are often considered a potential solution for improving model fairness while maintaining privacy of sensitive training data. We investigate the impact of using synthetic images from DP GANs on downstream classification model utility and fairness. We demonstrate that existing DP GANs cannot simultaneously maintain model utility, privacy, and fairness. The images generated from GAN models trained with DP exhibit extreme decreases in image quality and utility which leads to poor downstream classification model performance. Our evaluation highlights the friction between privacy, fairness, and utility and how this directly translates into real loss of performance and representation in common machine learning settings. Our results show that additional work improving the utility and fairness of DP generative models is required before they can be utilized as a potential solution to privacy and fairness issues stemming from lack of diversity in the training dataset.
An Input Weights Dependent Complex-Valued Learning Algorithm Based on Wirtinger Calculus

Complex-valued neural network is a kind of learning model which can deal with problems in complex domain. Fully complex extreme learning machine (CELM) is a much faster training algorithm than the complex backpropagation (CBP) scheme. However, it is at the cost of using more hidden nodes to obtain the comparable performance. An upper-layer-solution-aware algorithm has been proposed for training single-hidden layer feedforward neural networks, which performs much better than its counterparts, pseudo-inverse learning (PIL)/extreme learning machine and gradient decent-based backpropagation neural networks. Consequently, there exist two challenges that need to be dealt with: 1) How to combine the advantages of CBP and CELM to develop a novel complex learning algorithm? and 2) What is the convergent behavior of the presented algorithm? In this article, an input weights dependent complex-valued (IWDCV) learning algorithm based on Wirtinger calculus has been proposed, which effectively solves the nonanalytic problem of the common activation functions during training neural networks. In addition, the monotonicity of the error function and the deterministic convergence of the proposed model have been strictly proved, which theoretically guarantee the efficiency and effectiveness of the given model, IWDCV. Finally, for real and complex-valued problems, a variety of simulations have been done to demonstrate the comparable performance of the proposed algorithm which support the theoretical observations as well.
Structure-Guided Arbitrary Style Transfer for Artistic Image and Video

Recently, neural style transfer has become a popular task in both academic research and industrial applications. Although the existing methods made great progress in terms of quality and efficiency, most of them mainly focus on extracting high-level features. Therefore, it is still challenging to display the hierarchical structure of the content image due to lack of texture information, which causes blurred boundaries and distortion of the stylized image. In this paper, a novel neural image and video style transfer scheme is proposed to suppress distortion and preserve the semantic content of the content image, which is capable of yielding satisfactory stylized images and videos of a variety of scenarios. We first propose to assemble a refine network into an auto-encoder framework to guide style transfer, which can ensure that the stylized image have diverse levels of details. Then, we introduce the global content loss and the local region structure loss to train the model and enhance the robustness of the model. In addition, in order to produce a high-quality stylized video, our method not only preserves the image structure, but also introduces a temporal consistency loss and a cycle-temporal loss to avoid temporal incoherence and motion blur as far as possible. Our approach is also friendly for photographic and exposed image and video style transfer. Both quantitative and qualitative evaluation demonstrated the effectiveness of our method.
Word sense disambiguation based on context selection using knowledge-based word similarity

In this paper, we introduce a novel knowledge-based word-sense disambiguation (WSD) system. In particular, the main goal of our research is to find an effective way to filter out unnecessary information by using word similarity. For this, we adopt two methods in our WSD system. First, we propose a novel encoding method for word vector representation by considering the graphical semantic relationships from the lexical knowledge bases, and the word vector representation is utilized to determine the word similarity in our WSD system. Second, we present an effective method for extracting the contextual words from a text for analyzing an ambiguous word based on word similarity. The results demonstrate that the suggested methods significantly enhance the baseline WSD performance in all corpora. In particular, the performance on nouns is similar to those of the state-of-the-art knowledge-based WSD models, and the performance on verbs surpasses that of the existing knowledge-based WSD models.
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous regions when applying reinforcement learning (RL) on real-world tasks, like autonomous driving. However, existing studies mostly use model-free constrained RL, which causes inevitable constraint violations. This paper proposes a model-based feasibility enhancement technique of constrained RL, which enhances the feasibility of policy using generalized control barrier function (GCBF) defined on the distance to constraint boundary. By using the model information, the policy can be optimized safely without violating actual safety constraints, and the sample efficiency is increased. The infeasibility in solving the constrained policy gradient is handled by an adaptive coefficient mechanism. We evaluate the proposed method in both simulations and real vehicle experiments in a complex autonomous driving collision avoidance task. The proposed method achieves up to four times fewer constraint violations and converges 3.36 times faster than baseline constrained RL approaches.
Research on Practical Byzantine Fault Tolerant Consensus Algorithm Based on Blockchain

Abstract Practical Byzantine Fault-tolerant Algorithm (PBFT) is the most widely used consensus algorithm in alliance chain, which has the advantages of fault tolerance and large throughput. However, PBFT also has some problems that can’t be ignored in specific blockchain applications, such as bad behavior of master node, high network communication overhead and low system flexibility. In this paper, the improvement measures of PBFT consensus algorithm are summarized from many aspects, and then compared with traditional PBFT algorithm. Finally, the development direction and trend of PBFT algorithm are prospected, hoping to provide reference for the research and innovation of PBFT consensus algorithm in the future.
Attention-based Machine Learning Model for Smart Contract Vulnerability Detection

Abstract Ethereum attracts extensive attention due to its distinctive function of smart contract and decentralized applications (Dapps). Since the number of contracts on blockchain has increased vigorously, various security vulnerabilities come up. Researchers rely on static symbolic analysis method at first, and it seems to perform well in the accuracy of vulnerability detection. However, this method requires manual analysis in advance and it needs to traverse all the possible execution paths to find out the vulnerable ones. The deeper the path goes, the more time it costs to detect the contracts. This paper proposes an approach to detect smart contracts vulnerability on blockchain by using machine learning(ML) methods. This approach aims to build a general benchmark for new vulnerability detection in order to reduce the demand of expert manpower. Moreover, the high-speed-performance ML algorithm makes quick detection comes true. As long as we adjust the threshold of the model, it can work as a fast prefilter for the traditional symbolic analysis tools in further improvement of accuracy.
Light-weight network for real-time adaptive stereo depth estimation

Self-supervised learning methods have been proved effective in the task of real-time stereo depth estimation with the requirement of lower memory space and less computational cost. In this paper, a light-weight adaptive network (LWANet) is proposed by combining the self-supervised learning method to perform online adaptive stereo depth estimation for low computation cost and low GPU memory space. Instead of a regular 3D convolution, the pseudo 3D convolution is employed in the proposed light-weight network to aggregate the cost volume for achieving a better balance between the accuracy and the computational cost. Moreover, based on U-Net architecture, the downsample feature extractor is combined with a refined convolutional spatial propagation network (CSPN) to further refine the estimation accuracy with little memory space and computational cost. Extensive experiments demonstrate that the proposed LWANet effectively alleviates the domain shift problem by online updating the neural network, which is suitable for embedded devices such as NVIDIA Jetson TX2. The relevant codes are available at https://github.com/GANWANSHUI/LWANet
Critical Factors for Blockchain Technology Implementation: A Supply Chain Perspective

Blockchain is an emerging technology in the digitalization age which has the potential to transform the shape of various business and their related activities. Blockchain is known as the transformative technology capable of reforming the modern supply chain network by offering greater transparency in the information and physical products transformations. There are numerous benefits to implementing blockchain technology (BT) within the supply chain. However, the BT implementation in the supply chain domain is relatively low, because it depends on several factors. Hence, the primary objective of this research is to identify and investigate the critical factor in the supply chain to adopt the BT. To achieve this goal, this study identified the ten critical factors for implementing BT through the literature survey and finalized them with expert feedback. Further, an MCDM method TOPSIS is applied to prioritize these critical factors. The finding of this study shows that “develop the framework for blockchain”, “knowledge and expertise”, “communication among supply chain partners” are high priority critical factors. Therefore, this research recommends that organizations should develop the framework for blockchain and create knowledge and expertise in this domain. Further, they also maintain the effective communication SC partners as we as supply chain stakeholder for collaboration and coordination to implement the blockchain at supply chain level. This study analyses the critical factors associated with BT implementation in the context of the supply chain that will assist the managers for effective execution.
Interacting Multiple Model Based on Maximum Correntropy Kalman Filter

Traditional interacting multiple model Kalman filter (IMM-KF) was derived for linear discrete time system with Markovian coefficients and it works well under Gaussian noise. In practice, when the system is affected by the impulsive noise, the performance of the traditional IMM-KF model becomes poor. Then, maximum correntropy Kalman filter (MCKF) which has advantages in dealing with non-Gaussian noise was proposed. However, the performance of traditional MCKF becomes poor when the motion state is not single. In this brief, we propose a new algorithm called interacting multiple model based on maximum correntropy Kalman filter (IMM-MCKF) by combining interacting multiple model (IMM) with MCKF to deal with the impulsive noise. Also, the traditional MCKF works poorly when the kernel bandwidth is small, we made a further improvement by changing the kernel. Simulation examples are given to demonstrate the effectiveness of the new algorithms.
Medical image segmentation based on active fusion-transduction of multi-stream features

As an important building block in automatic medical systems, image segmentation has made great progress due to the data-driving mechanism of deep architecture. Recently, numerous methods have been proposed to boost the segmentation performance based on U-shape networks. However, the encoders in these models often share only one data routine, which may disempower the representation ability of the features. Although some existing work applied multiple learning paths to fix this problem, deep supervision techniques are additionally required to monitor the training status at the individual path, which may bring extra burdens to their training. Moreover, under these frameworks, the semantic gap between different paths may interfere with the model's learning performance, and the multi-granular features learned from the encoder may not be well coordinated for the segmentation task, since the potential transduction ability of skip connections still needs further investigation. To address these issues, we introduce a novel medical image segmentation framework, namely AFT-Net, in which an attention-based data fusion model is proposed to effectively cooperate with our multi-stream encoder, and an Inception Res-Atrous Convolution block is proposed to collect correlated contextual information in the decoding stage. By progressively accumulate the features from different paths, our method can establish meaningful connections between structural and semantic features, while keeping an integral and flexible layout without deeply customized supervision. Extensive experiments on four medical image data sets demonstrate that our method is able to acquire image features with both diversity and quality, thereby outperforms current state-of-the-art segmentation methods.
“I Suspect That the Pictures Are Stolen”: Romance Fraud, Identity Crime, and Responding to Suspicions of Inauthentic Identities

Romance fraud impacts the lives of thousands of people globally. Financial losses exceed millions of dollars each year and are steadily increasing annually. It occurs when an offender uses the guise of an intimate relationship to gain a financial advantage. Offenders use a variety of platforms and communications methods to target victims and develop the required trust and rapport to defraud them. As part of this process, offenders use false identities. This can be through the misappropriation of a genuine person’s identity, or it can be through the stealing of photos and other images of a genuine person to create a false or fictitious identity. In this way, romance fraud encompasses two categories of victims: those who lose money through a relationship and those who are subject to identity crime by romance fraud offenders. This article examines how individuals responded to suspicions of inauthentic identities to a person they were communicating with online. It uses romance fraud complaints lodged with Scamwatch (an Australian online fraud porting portal) between July 2018 and July 2019 (inclusive). The qualitative analysis indicates that many complainants used an internet search to verify or refute their suspicions about their partner. This yielded substantiated results in many cases and confirmed that the offender’s identity was not genuine. The article explores the implications for this victim-based detection strategy. In doing this, it argues that while effective for romance fraud victims, responses can further exacerbate the ongoing harms experienced by those whose identities have been compromised by romance fraud offenders. The article concludes with a discussion about the future effectiveness of internet searches to reduce romance fraud and the impact of this strategy on third parties whose identities are compromised by offenders.
The Augmented Reality Technology as Enabler for the Digitization of Industrial Business Processes: Case Studies

Augmented Reality Technology Augmented Reality Technology (AR) is one of the key technologies of digital transformation in industrial and non-industrial areas. Due to the rapid development of display hardware and tracking systems, virtual and augmented reality applications are being developed today that would only have been possible in large research laboratories with huge efforts a few years ago. This paper presents the application of the augmented reality technology in the industrial field. Based on industrial case studies the implementation of two AR applications and their potential to digitize the product lifecycle processes will be discussed. The case studies were developed within cooperation projects between the University of Applied Sciences Karlsruhe and a fire truck manufacturer with the focus on the following topics: product configuration management and assembly assistance. The objective of this work is the investigation of the AR application in the industrial environment and its capability as digital transformation technology along the product lifecycle. The presented paper delivers experiences and suggestions in terms of praxis-oriented development and deployment of the AR technology in the engineering field, which can be used for future AR research works.
Wehrl entropy, entropic uncertainty relations, and entanglement

The Wehrl entropy is an entropy associated to the Husimi quasi-probability distribution. We discuss how it can be used to formulate entropic uncertainty relations and for a quantification of entanglement in continuous variables. We show that the Wehrl-Lieb inequality is closer to equality than the usual Bia{\l}ynicki-Birula and Mycielski entropic uncertainty relation almost everywhere. Furthermore, we show how a Wehrl mutual information can be used to obtain a measurable perfect witness for pure state bipartite entanglement, which additionally provides a lower bound on the entanglement entropy.