Reviewerly/exHarmony huggingface dataset:
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

As data privacy increasingly becomes a critical societal concern, federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing systems and infrastructures to ease the development of various federated learning algorithms. Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various aspects such as effectiveness, efficiency, and privacy. In this survey, we conduct a comprehensive review on federated learning systems. To understand the key design system components and guide future research, we introduce the definition of federated learning systems and analyze the system components. Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation. The categorization can help the design of federated learning systems as shown in our case studies. By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.
Quantum error correction and universal gate set operation on a binomial bosonic logical qubit

Logical qubit encoding and quantum error correction (QEC) have been experimentally demonstrated in various physical systems with multiple physical qubits, however, logical operations are challenging due to the necessary nonlocal operations. Alternatively, logical qubits with bosonic-mode-encoding are of particular interest because their QEC protection is hardware efficient, but gate operations on QEC protected logical qubits remain elusive. Here, we experimentally demonstrate full control on a single logical qubit with a binomial bosonic code, including encoding, decoding, repetitive QEC, and high-fidelity (97.0% process fidelity on average) universal quantum gate set on the logical qubit. The protected logical qubit has shown 2.8 times longer lifetime than the uncorrected one. A Ramsey experiment on a protected logical qubit is demonstrated for the first time with two times longer coherence than the unprotected one. Our experiment represents an important step towards fault-tolerant quantum computation based on bosonic encoding.
An under‐sampled software defect prediction method based on hybrid multi‐objective cuckoo search

Summary Both the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi‐objective cuckoo search under‐sampled software defect prediction model based on SVM (HMOCS‐US‐SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi‐objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non‐defective sampling and optimize the parameters of SVM. Then, three under‐sampled methods for decision region range are proposed to select the non‐defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G‐mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model. Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem.
Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues

This article presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. Unlike existing review papers, we examine the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations. Deep learning is one potential solution for object detection and scene perception problems, which can enable algorithm-driven and data-driven cars. In this article, we aim to bridge the gap between deep learning and self-driving cars through a comprehensive survey. We begin with an introduction to self-driving cars, deep learning, and computer vision followed by an overview of artificial general intelligence. Then, we classify existing powerful deep learning libraries and their role and significance in the growth of deep learning. Finally, we discuss several techniques that address the image perception issues in real-time driving, and critically evaluate recent implementations and tests conducted on self-driving cars. The findings and practices at various stages are summarized to correlate prevalent and futuristic techniques, and the applicability, scalability and feasibility of deep learning to self-driving cars for achieving safe driving without human intervention. Based on the current survey, several recommendations for further research are discussed at the end of this article.
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches.
Blockchain-Driven IoT for Food Traceability With an Integrated Consensus Mechanism

Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance.Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy.Moreover, the traceability process is time-consuming and complicated in modern supply chain networks.To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability.However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical.In this paper, a blockchain-IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food.To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed.The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units.Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation.
Contrastive Clustering

In this paper, we propose an online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Besides, the proposed method could timely compute the cluster assignment for each individual, even when the data is presented in streams. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19% (39%) performance improvement compared with the best baseline. The code is available at https://github.com/XLearning-SCU/2021-AAAI-CC.
A Survey of Data Augmentation Approaches for NLP

Data augmentation has recently seen increased interest in NLP due to more work in lowresource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data.Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data.In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner.We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches.Next, we highlight techniques that are used for popular NLP applications and tasks.We conclude by outlining current challenges and directions for future research.Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area.We also present a GitHub repository with a paper list that will be continuously updated at https://github.com/styfeng/DataAug4NLP.
ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation

This paper presents a technique to scan neural network based AI models to determine if they are trojaned. Pre-trained AI models may contain back-doors that are injected through training or by transforming inner neuron weights. These trojaned models operate normally when regular inputs are provided, and mis-classify to a specific output label when the input is stamped with some special pattern called trojan trigger. We develop a novel technique that analyzes inner neuron behaviors by determining how output activations change when we introduce different levels of stimulation to a neuron. The neurons that substantially elevate the activation of a particular output label regardless of the provided input is considered potentially compromised. Trojan trigger is then reverse-engineered through an optimization procedure using the stimulation analysis results, to confirm that a neuron is truly compromised. We evaluate our system ABS on 177 trojaned models that are trojaned with various attack methods that target both the input space and the feature space, and have various trojan trigger sizes and shapes, together with 144 benign models that are trained with different data and initial weight values. These models belong to 7 different model structures and 6 different datasets, including some complex ones such as ImageNet, VGG-Face and ResNet110. Our results show that ABS is highly effective, can achieve over 90% detection rate for most cases (and many 100%), when only one input sample is provided for each output label. It substantially out-performs the state-of-the-art technique Neural Cleanse that requires a lot of input samples and small trojan triggers to achieve good performance.
ZombieLoad

In early 2018, Meltdown first showed how to read arbitrary kernel memory from user space by exploiting side-effects from transient instructions. While this attack has been mitigated through stronger isolation boundaries between user and kernel space, Meltdown inspired an entirely new class of fault-driven transient-execution attacks. Particularly, over the past year, Meltdown-type attacks have been extended to not only leak data from the L1 cache but also from various other microarchitectural structures, including the FPU register file and store buffer.
A Survey of Utility-Oriented Pattern Mining

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques and constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, satisfaction, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented in detail, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field.
Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases

Chatbot identity disclosure negatively affects customer purchases because customers perceive the disclosed bot as less knowledgeable and less empathetic.
A comprehensive data level analysis for cancer diagnosis on imbalanced data

The early diagnosis of cancer, as one of the major causes of death, is vital for cancerous patients. Diagnosing diseases in general and cancer in particular is a considerable application of data analysis for medical science. However, imbalanced data distribution and imbalanced quality of the majority and minority classes, which lead to misclassification, is a great challenge in this field. Though the samples of the majority class and their proper classification are more important to classifier, cancer is diagnosed by relying on the minority class samples (cancer data class). While the consequence of wrong diagnosis for non-cancerous patients is several additional clinical tests, the cancerous patients pay the price of wrong diagnosis with their lives. As such, studying the class imbalance problem is vital from the medical’s perspective. To serve this purpose, a comprehensive study on the consequences of imbalanced data problem is performed in this paper on the data of cancer patients for the first time. In this context, oversampling and under sampling as two main balancing techniques including 18 algorithms are employed. The techniques used in oversampling are ADASYN, ADOMS, AHC, Borderline-SMOTE, ROS, Safe-Level-SMOTE, SMOTE, SMOTE-ENN, SMOTE-TL, SPIDER and SPIDER2, while under sampling techniques are CNN, CNNTL, NCL, OSS, RUS, SBC and TL. To examine the impact of balancers on the performance of classifiers, four classifiers named RIPPER, MLP, KNN, and C4.5 are employed as learners. In addition, 15 cancer data sets from SEER program used for the study are kidney, soft tissue, bladder, rectum, colon, bone, larynx, breast, cervix, prostate, oropharynx, melanoma, thyroid, testis, and lip. The findings of the study are centered on examining the impact of class imbalance on the function of classifiers, a general comparing of the function of pre-processing techniques and classifying all data sets and finally determining the best balancer and classifier for each kind of cancer data set. According to the results, significant improvement is obtained through using balancers. Assessing by AUC, the performance of different classifiers of cancer imbalanced data sets has improved in 90% of the cases after using balancing techniques. To be more precise, Friedman statistical tests are applied and interestingly, each kind of cancer data set responded differently to different balancing techniques and classifiers. Moreover, considering the mean rank of each technique and classifier that were used for data sets, oversampling balancing techniques result in better outcomes than under sampling ones.
Infrared-Visible Cross-Modal Person Re-Identification with an X Modality

This paper focuses on the emerging Infrared-Visible cross-modal person re-identification task (IV-ReID), which takes infrared images as input and matches with visible color images. IV-ReID is important yet challenging, as there is a significant gap between the visible and infrared images. To reduce this ‘gap’, we introduce an auxiliary X modality as an assistant and reformulate infrared-visible dual-mode cross-modal learning as an X-Infrared-Visible three-mode learning problem. The X modality restates from RGB channels to a format with which cross-modal learning can be easily performed. With this idea, we propose an X-Infrared-Visible (XIV) ReID cross-modal learning framework. Firstly, the X modality is generated by a lightweight network, which is learnt in a self-supervised manner with the labels inherited from visible images. Secondly, under the XIV framework, cross-modal learning is guided by a carefully designed modality gap constraint, with information exchanged cross the visible, X, and infrared modalities. Extensive experiments are performed on two challenging datasets SYSU-MM01 and RegDB to evaluate the proposed XIV-ReID approach. Experimental results show that our method considerably achieves an absolute gain of over 7% in terms of rank 1 and mAP even compared with the latest state-of-the-art methods.
Fabric-iot: A Blockchain-Based Access Control System in IoT

IoT devices have some special characteristics, such as mobility, limited performance, and distributed deployment, which makes it difficult for traditional centralized access control methods to support access control in current large-scale IoT environment. To address these challenges, this paper proposes an access control system in IoT named fabric-iot, which is based on Hyperledger Fabric blockchain framework and attributed based access control (ABAC). The system contains three kinds of smart contracts, which are Device Contract (DC), Policy Contract (PC), and Access Contract (AC). DC provides a method to store the URL of resource data produced by devices, and a method to query it. PC provides functions to manage ABAC policies for admin users. AC is the core program to implement an access control method for normal users. Combined with ABAC and blockchain technology, fabric-iot can provide decentralized, fine-grained and dynamic access control management in IoT. To verify the performance of this system, two groups of simulation experiments are designed. The results show that fabric-iot can maintain high throughput in large-scale request environment and reach consensus efficiently in a distributed system to ensure data consistency.
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition

Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency. However, current methods cannot locate the semantic regions accurately due to the lack of part-level supervision or semantic guidance. Moreover, they cannot fully explore the mutual interactions among the semantic regions and do not explicitly model the label co-occurrence. To address these issues, we propose a Semantic-Specific Graph Representation Learning (SSGRL) framework that consists of two crucial modules: 1) a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and 2) a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism. Extensive experiments on public benchmarks show that our SSGRL framework outperforms current state-of-the-art methods by a sizable margin, e.g. with an mAP improvement of 2.5%, 2.6%, 6.7%, and 3.1% on the PASCAL VOC 2007 & 2012, Microsoft-COCO and Visual Genome benchmarks, respectively. Our codes and models are available at https://github.com/HCPLab-SYSU/SSGRL.
HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides

PURPOSE Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis. METHODS Here we present HistoQC, a tool for rapidly performing quality control to not only identify and delineate artefacts but also discover cohort-level outliers (eg, slides stained darker or lighter than others in the cohort). This open-source tool employs a combination of image metrics (eg, color histograms, brightness, contrast), features (eg, edge detectors), and supervised classifiers (eg, pen detection) to identify artefact-free regions on digitized slides. These regions and metrics are presented to the user via an interactive graphical user interface, facilitating artefact detection through real-time visualization and filtering. These same metrics afford users the opportunity to explicitly define acceptable tolerances for their workflows. RESULTS The output of HistoQC on 450 slides from The Cancer Genome Atlas was reviewed by two pathologists and found to be suitable for computational analysis more than 95% of the time. CONCLUSION These results suggest that HistoQC could provide an automated, quantifiable, quality control process for identifying artefacts and measuring slide quality, in turn helping to improve both the repeatability and robustness of DP workflows.