Reviewerly/exHarmony huggingface dataset:
PRoA: A Probabilistic Robustness Assessment Against Functional Perturbations

In safety-critical deep learning applications robustness measurement is a vital pre-deployment phase. However, existing robustness verification methods are not sufficiently practical for deploying machine learning systems in the real world. On the one hand, these methods attempt to claim that no perturbations can “fool” deep neural networks (DNNs), which may be too stringent in practice. On the other hand, existing works rigorously consider $$L_p$$ bounded additive perturbations on the pixel space, although perturbations, such as colour shifting and geometric transformations, are more practically and frequently occurring in the real world. Thus, from the practical standpoint, we present a novel and general probabilistic robustness assessment method (PRoA) based on the adaptive concentration, and it can measure the robustness of deep learning models against functional perturbations. PRoA can provide statistical guarantees on the probabilistic robustness of a model, i.e., the probability of failure encountered by the trained model after deployment. Our experiments demonstrate the effectiveness and flexibility of PRoA in terms of evaluating the probabilistic robustness against a broad range of functional perturbations, and PRoA can scale well to various large-scale deep neural networks compared to existing state-of-the-art baselines. For the purpose of reproducibility, we release our tool on GitHub: https://github.com/TrustAI/PRoA .
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks

A number of approaches for anomaly detection on attributed networks have been proposed. However, most of them suffer from two major limitations: (1) they rely on unsupervised approaches which are intrinsically less effective due to the lack of supervisory signals of what information is relevant for capturing anomalies, and (2) they rely only on using local, e.g., one- or two-hop away node neighbourhood information, but ignore the more global context. Since anomalous nodes differ from normal nodes in structures and attributes, it is intuitive that the distance between anomalous nodes and their neighbors should be larger than that between normal nodes and their (also normal) neighbors if we remove the edges connecting anomalous and normal nodes. Thus, estimating hop counts based on both global and local contextual information can help us to construct an anomaly indicator. Following this intuition, we propose a hop-count based model (HCM) that achieves that. Our approach includes two important learning components: (1) Self-supervised learning task of predicting the shortest path length between a pair of nodes, and (2) Bayesian learning to train HCM for capturing uncertainty in learned parameters and avoiding overfitting. Extensive experiments on real-world attributed networks demonstrate that HCM consistently outperforms state-of-the-art approaches.
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification

Abstract The rapid development of histopathology scanners allowed the digital transformation of pathology. Current devices fastly and accurately digitize histology slides on many magnifications, resulting in whole slide images (WSI). However, direct application of supervised deep learning methods to WSI highest magnification is impossible due to hardware limitations. That is why WSI classification is usually analyzed using standard Multiple Instance Learning (MIL) approaches, that do not explain their predictions, which is crucial for medical applications. In this work, we fill this gap by introducing ProtoMIL, a novel self-explainable MIL method inspired by the case-based reasoning process that operates on visual prototypes. Thanks to incorporating prototypical features into objects description, ProtoMIL unprecedentedly joins the model accuracy and fine-grained interpretability, as confirmed by the experiments conducted on five recognized whole-slide image datasets.
A Robust Triangular Sigmoid Pattern-Based Obstacle Detection Algorithm in Resource-Limited Devices

Object detection and classification are key processes in advanced driver-assistance systems. The existing object detection and classification methods are effective in normal daylight conditions. However, the performance of these methods deteriorates in adverse driving conditions, such as those involving low light, illumination changes, and nighttime conditions. To overcome these limitations, several feature-based algorithms have been developed that introduce local features, such as local binary pattern, local tetra pattern, and local density encoding, for adverse driving conditions. However, these local patterns cannot effectively address the noise in real driving conditions because the relationship between the neighboring pixels cannot be comprehensively encoded. To solve these problems, this study developed a robust feature-based method by introducing a triangular-pattern-based sigmoid function to effectively encode and establish the robust feature of neighboring pixels in the local region. The performance of the proposed pattern is evaluated by integrating it into state-of-the-art object detection algorithms. The proposed method significantly increases the vehicle detection ratio of YOLOv5s by 11.7% for an intersection over a union of 0.5 in difficult driving conditions for the CCD dataset. Moreover, the detection ratios of the proposed method are comparable to those of other state-of-the-art object detection methods such as Retina, Faster RCNN, and Deformable DETR over various datasets such as KITTI, COCO, HCI, and CCD. Additionally, the proposed algorithm is implemented on a Raspberry Pi-based autonomous car system to evaluate its performance during real driving conditions. Our proposed method supports robust input feature extraction and can thus be used to enhance the performance of the existing obstacle detection and classification systems.
A Systematic Literature Review on Binary Neural Networks

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN utilizes binary weights and activation function parameters to substitute the full-precision values. In digital implementations, BNN replaces the complex calculations of Convolutional Neural Networks (CNNs) with simple bitwise operations. BNN optimizes large computation and memory storage requirements, which leads to less area and power consumption compared to full-precision models. Although there are many advantages of BNN, the binarization process has a significant impact on the performance and accuracy of the generated models. To reflect the state-of-the-art in BNN and explore how to develop and improve BNN-based models, we conduct a systematic literature review on BNN with data extracted from 239 research studies. Our review discusses various BNN architectures and the optimization approaches developed to improve their performance. There are three main research directions in BNN: accuracy optimization, compression optimization, and acceleration optimization. The accuracy optimization approaches include quantization error reduction, special regularization, gradient error minimization, and network structure. The compression optimization approaches combine fractional BNN and pruning. The acceleration optimization approaches comprise computing in-memory, FPGA-based implementations, and ASIC-based implementations. At the end of our review, we present a comprehensive analysis of BNN applications and their evaluation metrics. Also, we shed some light on the most common BNN challenges and the future research trends of BNN.
Federated Representation Learning With Data Heterogeneity for Human Mobility Prediction

The advancement of smart wearable devices and location-based smart services has enabled a new paradigm for smart human mobility prediction (HMP), which has a broad range of applications in smart healthcare and smart cities. Due to the privacy concerns and rigorous data regulations, federated learning provides a distributed learning framework to collaboratively train the HMP model without sharing the highly sensitive location data with others. However, in real-world scenarios, federated human mobility prediction suffers from data heterogeneity challenge, which includes two main aspects: heterogeneity mobility patterns, and data scarcity. In this paper, we propose an end-to-end federated representation learning framework for human mobility prediction, named FR-HMP, to overcome all the above obstacles. Specially, in order to enhance the representation abilities of data-scarcity clients, a two-phase learning process is proposed. The clustering module could cluster similar clients together on the parameter server to address the heterogeneous mobility patterns, and the representation learning module learns the enhanced representations of each client through the graph learning layer and graph convolution layer on the third-part server. Finally, extensive experiments are conducted using two diverse real-world HMP datasets to show the advantages of FR-HMP over state-of-the-art methods.
An Efficient Decentralized Identity Management System Based on Range Proof for Social Networks

Online social networks (OSNs) are becoming more and more popular in people's lives as the demand for online interaction continues to grow. Current OSNs are using centralized identity management system (IDM), which has some problems of single point of failure and privacy data leakage. The emergence of decentralized identity (DID) can solve these problems. However, most existing DID systems have some privacy issues that a user's attributes value are disclosed while accessing service. In this paper, we design a DID protocol to solve these challenges. The proposed protocol includes a range proof protocol to provide attribute privacy. The range proof protocol works with anonymous credentials and does not need a trusted setup. Moreover, the identity model behind the DID protocol is extended from an existing model, which achieves identity revocation. Finally, we implement a system prototype on the blockchain for evaluation. The security analysis shows that our protocol provides stronger privacy protection. The performance evaluation indicates that the computation cost and blockchain overheads of our protocol are acceptable in OSNs.
Neural network model based on global and local features for multi-view mammogram classification

Mammography is an important screening criterion for breast cancer, one of the major diseases causing numerous deaths among female patients. Meanwhile, manual diagnosis of mammography is a time-consuming and labor-consuming job. Mammogram classification based on deep learning plays a vital role in computer-aided diagnosis (CAD) systems to mitigate the pressure on physicians. This paper proposes a learning-based multi-view mammogram classification model that captures long-distance dependence and extracts features of multiple receptive fields. Our model considers global and local features of mammography images using Transformer for global features and the proposed multiplex convolutions module for local features. We evaluate our proposed method on a dataset of mammography images obtained from a hospital in China. The proposed method achieves 90.57% accuracy and 94.86% AUC in benign or malignant classification tasks and outperforms other advanced methods for mammogram classification. It is worth noting that the proposed method only requires image-level labels and acts on the whole raw mammogram, which has clinical significance.
Boundary-constrained robust regularization for single image dehazing

Generally, for single image dehazing, regularization-based schemes improve the initial transmission map iteratively by using a guidance map as a structural prior. We conducted experiments on a large number of hazy images and observed that a constrained transmission map affects the quality of the recovered image. However, regularization-based methods do not constrain the transmission map to its physically valid range during the iterative process. It degrades its robustness to outliers, and consequently, deteriorates the quality of the recovered image. In addition, conventional methods fuse the structural information of the guidance and initial transmission map without considering any structural differences between them. To address these issues, in this paper, we present a robust regularization scheme that constraints the transmission map during its enhancement. In the proposed scheme, a nonconvex energy function is constructed that leverages the mutual structural information of the guidance and transmission map. The nonconvex problem is solved by a majorize-minimization algorithm, and the intermediate transmission maps are constrained through the appropriate lower and upper bounds. The retrieved transmission map has better edge-preserving properties, and ultimately, results in a high-quality haze-free image that has faithful colors and fine details. The proposed scheme is tested on benchmark datasets and results are evaluated through quantitative metrics. The comparative analysis has revealed the effectiveness of the proposed scheme.
Mass segmentation and classification from film mammograms using cascaded deep transfer learning

Breast cancer is the most common type of cancer among women worldwide. Early breast cancers have a high chance of cure so early diagnosis is critical. Mammography screening allows early detection of breast cancer. There has been an increasing interest in the investigation of computer-aided breast cancer diagnosis recently due in part to the development of the novel high-performing deep learning models. In this study, cascaded deep transfer learning (DTL)-based segmentation methods were investigated to segment mass lesions using mammograms of Breast Cancer Digital Repository. In the first stage, the noise sources in the mammogram background were removed by deep learning-based breast segmentation. In the second stage, the mass segmentation performances of five-layer U-net and U-nets having pre-trained weights from VGG16, ResNet50, and Xception networks in the encoding path were investigated. The performances of attention U-net, residual U-net, MultiResUnet, DeepLabV3Plus, and Unet++ were also investigated. A Unet++ model that uses Xception network weights in the encoder region is proposed. The mass segmentation model predictions were used to estimate mass lesion characterization using DTL. On the test data, an AUC of 0.7829, Dice's similarity coefficient of 0.6356 and intersection over union of 0.5408 were obtained for mass segmentation using the proposed U-net++Xception model. An AUC of 0.8188 and accuracy of 0.7619 were obtained for mass classification into benign versus malignant. The results show that the proposed DTL pipeline can be used for automatic mass segmentation and classification without using clinical data and may reduce the workload of radiologists.
Crypto Tokens and Token Systems

Cyptographic tokens are one of the cornerstones of the new blockchain world but the knowledge about these digital objects is still limited. In this research, we argue that crypto tokens, cryptographically secured digital tokens connected to DLT systems, form socio-technical systems through their reciprocal relationship with their foundational DLT systems. We also argue that today's crypto token systems evolved out of earlier physical and digital token systems, a socio-technical transition facilitated by changes in the wider socio-cultural, economic, and technical environment. Based on an extensive structured literature review as well as the application of text analytics methods to more than 506 blockchain whitepapers, we develop two results: a crypto token classification built around three crypto token archetypes, and a crypto token system taxonomy. Our findings are relevant for both blockchain researchers and practitioners alike by enhancing our understanding of complex blockchain systems.
“Born for a Storm”: Hard-Right Social Media and Civil Unrest

Does activity on hard-right social media lead to hard-right civil unrest? If so, why? We created a spatial panel dataset comprising hard-right social media use and incidents of unrest across the United States from January 2020 through January 2021. Using spatial regression analyses with core-based statistical area (CBSA) and month fixed effects, we find that greater CBSA-level hard-right social media activity in a given month is associated with an increase in subsequent unrest. The results of robustness checks, placebo tests, alternative analytical approaches, and sensitivity analyses support this finding. To examine why hard-right social media activity predicts unrest, we draw on an original dataset of users’ shared content and status in the online community. Analyses of these data suggest that hard-right social media shift users’ perceptions of norms, increasing the likelihood they will participate in contentious events they once considered taboo. Our study sheds new light on social media’s offline effects, as well as the consequences of increasingly common hard-right platforms.
DycSe: A Low-Power, Dynamic Reconfiguration Column Streaming-Based Convolution Engine for Resource-Aware Edge AI Accelerators

Edge AI accelerators are utilized to accelerate the computation in edge AI devices such as image recognition sensors on robotics, door lockers, drones, and remote sensing satellites. Instead of using a general-purpose processor (GPP) or graphic processing unit (GPU), an edge AI accelerator brings a customized design to meet the requirements of the edge environment. The requirements include real-time processing, low-power consumption, and resource-awareness, including resources on field programmable gate array (FPGA) or limited application-specific integrated circuit (ASIC) area. The system’s reliability (e.g., permanent fault tolerance) is essential if the devices target radiation fields such as space and nuclear power stations. This paper proposes a dynamic reconfigurable column streaming-based convolution engine (DycSe) with programmable adder modules for low-power and resource-aware edge AI accelerators to meet the requirements. The proposed DycSe design does not target the FPGA platform only. Instead, it is an intellectual property (IP) core design. The FPGA platform used in this paper is for prototyping the design evaluation. This paper uses the Vivado synthesis tool to evaluate the power consumption and resource usage of DycSe. Since the synthesis tool is limited to giving the final complete system result in the designing stage, we compare DycSe to a commercial edge AI accelerator for cross-reference with other state-of-the-art works. The commercial architecture shares the competitive performance within the low-power ultra-small (LPUS) edge AI scopes. The result shows that DycSe contains 3.56% less power consumption and slight resources (1%) overhead with reconfigurable flexibility.
A Universal Privacy-Preserving Multi-Blockchain Aggregated Identity Scheme

Cryptocurrencies offer various benefits in terms of privacy protection and cross-border transactions, but they have also been used for illicit activities such as money laundering due to their anonymous nature and the difficulty of cross-border regulation. Additionally, the unethical actions of some virtual asset service providers (VASPs), such as rug pulls and the embezzlement of user funds, have further eroded the trust between users and VASPs. Implementing identity management on blockchains can help restore trust between users and VASPs. However, current solutions have privacy concerns as identity providers have access to the asset balances and transaction records of each user’s wallet account, and no solution can support all public blockchains unconditionally. To address these issues, this paper proposes a multi-chain aggregated identity scheme. In this scheme, the identity provider will issue a non-fungible token (NFT) for users who have undergone verification, and wallet accounts from different blockchains will be added to a cryptographic accumulator. The accumulator value is then bound to the identity NFT through a smart contract by the user. This allows the user to prove to others that the identity of the wallet account owner has been verified. The use of accumulators also allows users to combine proof for multiple wallets into a single proof, which significantly improves the efficiency and provides a way for VASPs such as centralized exchanges to demonstrate Proof of Reserves (PoR) to users. Importantly, this scheme preserves privacy as neither the identity provider nor the VASPs can link the user’s real identity with the wallet accounts. Only regulators can access the user’s identity data held by the identity provider and the user’s wallet account held by the VASP to link real identities with wallet accounts for the purpose of sanctions or criminal investigations. Additionally, the scheme supports all blockchains by allowing wallet accounts from any public blockchain to be added to the accumulator. Furthermore, the NFT implementation in the scheme helps prevent identity loss or theft, as it can only be transferred by the identity provider.
Cloud Computing Value Chains: Research from the Operations Management Perspective

Problem definition: Cloud computing is recognized as a critical driver of information technology–enabled innovations. The operations management (OM) community, however, has not been exposed enough to the essential operations problems that arise from the management of cloud value chains. Academic/practical relevance: In this paper, we examine recent research on cloud value chains and explore future research opportunities from an OM perspective. In particular, we focus on major operations management challenges facing a cloud provider in three problem domains: (1) cloud computing resource management, (2) pricing in the cloud computing marketplaces, and (3) capacity planning and management of cloud supply chains. Methodology: We describe prevalent business models and management practices in the cloud value chains, discuss recent research from OM that falls into each of the three problem domains mentioned, and point out opportunities for future research. Results: We note that cloud computing operations are driven by demand that exhibits distinct characteristics, including complex workflow, demand redundancy, multifeatured characteristics, multidimensional resource requirement, and nonstationarity. On the supply side, cloud computing operations also exhibit distinct characteristics, including heterogeneous resources, packing constraints, preconfigured (“bundled”) supply, technology risks, and cost uncertainty. These characteristics of demand and supply are not all prevalent in other operations. Managerial implications: Cloud computing operations not only share many features with classic OM problems, but also bring new challenges and innovative business models. Thus, OM tools and research have the potential to provide vital insights into cloud computing operations and impact management practices in the cloud industry, which, in turn, can stimulate much innovative research from the OM perspective. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1178 .