Reviewerly/exHarmony huggingface dataset:
Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cells in the bone marrow. Computer vision techniques provide automated analysis that can help specialists diagnose this disease. Microscopy image analysis is the most economical method for the initial screening of patients with ALL, but this task is subjective and time-consuming. In this study, we propose a hybrid model using a genetic algorithm (GA) and a residual convolutional neural network (CNN), ResNet-50V2, to predict ALL using microscopy images available in ALL-IDB dataset. However, accurate prediction requires suitable hyperparameters setup, and tuning these values manually still poses challenges. Hence, this paper uses GA to find the best hyperparameters that lead to the highest accuracy rate in the models. Also, we compare the performance of GA hyperparameter optimization with Random Search and Bayesian optimization methods. The results show that GA optimization improves the accuracy of the classifier, obtaining 98.46% in terms of accuracy. Additionally, our approach sheds new perspectives on identifying leukemia based on computer vision strategies, which could be an alternative for applications in a real-world scenario.
Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.
Blockchain: From Bitcoin to the Internet of Value and beyond

Information Systems (IS) scholars have made significant contributions to blockchain knowledge since blockchain technologies were first implemented in 2009 with the launch of Bitcoin. The overarching espoused aim of blockchain technologies is the decentralization of power over the Internet. Peer-to-peer payments were the first applications, followed by platforms for decentralized applications, decentralized autonomous organizations, and, more recently, self-sovereign identity, decentralized finance, nonfungible tokens, and metaverses. JIT invited Mary Lacity, director of the Blockchain Center of Excellence at the University of Arkansas, to provide an overview of the major innovations, explain how early enterprise adopters are using these innovations, and where these technologies might be headed. This paper, formatted as a conversation, hopes to inspire more IS scholars to engage in this rapidly evolving area.
zk-AuthFeed: Protecting Data Feed to Smart Contracts With Authenticated Zero Knowledge Proof

The emerging blockchain technology, combined with the smart contract paradigm, is expected to transform traditional applications with decentralization. When the blockchain technology is applied to decentralize traditional applications, blockchain validators may need to take in sensitive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off-chain</i> data to execute a smart contract. On the one hand, decentralized applications (DApps) require authentic off-chain input data to correctly execute a given business procedure. On the other hand, users are reluctant to expose their sensitive privacy on the blockchain. For example, for a decentralized medical insurance DApp that takes as input personal health data, it is critical to guarantee authenticity and privacy of the data sent to the smart contract, such that the data can be verified by validators without leaking sensitive information. However, no satisfactory solution has been proposed to attain privacy and authenticity at the same time. In this work, we first present a highly efficient authenticated zero knowledge proof protocol called zk-DASNARK by extending the classical zk-SNARK scheme with data authentication. Based on zk-DASNARK, we design zk-AuthFeed, a zero-knowledge authenticated off-chain data feed scheme to achieve both data privacy and authenticity for blockchain-based DApps. Following the strategy of “compute off-chain and verify on-chain”, zk-AuthFeed can significantly reduce computation cost of blockchain validators. We fully implement a prototype of zk-AuthFeed, and conduct comprehensive experiments on a medical insurance DApp. We consider 4 typical computation models for insurance premium/reimbursement in the experiments. It shows that zk-AuthFeed is highly efficient: key generation takes about 10 seconds only, proof generation takes less than 4 seconds, and proof verification takes less than 40 ms.
Unified Graph Structured Models for Video Understanding

Accurate video understanding involves reasoning about the relationships between actors, objects and their environment, often over long temporal intervals. In this paper, we propose a message passing graph neural network that explicitly models these spatio-temporal relations and can use explicit representations of objects, when supervision is available, and implicit representations otherwise. Our formulation generalises previous structured models for video understanding, and allows us to study how different design choices in graph structure and representation affect the model’s performance. We demonstrate our method on two different tasks requiring relational reasoning in videos – spatio-temporal action detection on AVA and UCF101-24, and video scene graph classification on the recent Action Genome dataset – and achieve state-of-the-art results on all three datasets. Furthermore, we show quantitatively and qualitatively how our method is able to more effectively model relationships between relevant entities in the scene.
Mutual Supervision for Dense Object Detection

The classification and regression head are both indispensable components to build up a dense object detector, which are usually supervised by the same training samples and thus expected to have consistency with each other for detecting objects accurately in the detection pipeline. In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency. MuSu defines training samples for the regression head mainly based on classification predicting scores and in turn, defines samples for the classification head based on localization scores from the regression head. Experimental results show that the convergence of detectors trained by this mutual supervision is guaranteed and the effectiveness of the proposed method is verified on the challenging MS COCO benchmark. We also find that tiling more anchors at the same location benefits detectors and leads to further improvements under this training scheme. We hope this work can inspire further researches on the interaction of the classification and regression task in detection and the supervision paradigm for detectors, especially separately for these two heads.
Just-in-time software vulnerability detection: Are we there yet?

Software vulnerabilities are weaknesses in source code that might be exploited to cause harm or loss. Previous work has proposed a number of automated machine learning approaches to detect them. Most of these techniques work at release-level, meaning that they aim at predicting the files that will potentially be vulnerable in a future release. Yet, researchers have shown that a commit-level identification of source code issues might better fit the developer’s needs, speeding up their resolution. To investigate how currently available machine learning-based vulnerability detection mechanisms can support developers in the detection of vulnerabilities at commit-level. We perform an empirical study where we consider nine projects accounting for 8991 commits and experiment with eight machine learners built using process, product, and textual metrics. We point out three main findings: (1) basic machine learners rarely perform well; (2) the use of ensemble machine learning algorithms based on boosting can substantially improve the performance; and (3) the combination of more metrics does not necessarily improve the classification capabilities. Further research should focus on just-in-time vulnerability detection, especially with respect to the introduction of smart approaches for feature selection and training strategies.
A deterministic source of single photons

New quantum computing applications are now possible because of advances in atomic and solid-state physics.
Blockchain-Assisted Transparent Cross-Domain Authorization and Authentication for Smart City

Secure cross-domain authorization and authentication (AA) enable application service providers (ASPs) to allow users for resource access from different trusted domains. In this article, we propose a unified blockchain-assisted secure cross-domain AA framework for smart city, which can guarantee transparent cross-domain resource access while preserving user privacy. In the framework, ASPs can flexibly delegate their authentication capabilities to the blockchain, and users authorized by different ASPs can be authenticated by the blockchain where the authentication events are publicly audited and traced. Since the blockchain is publicly accessible, users’ sensitive identity attributes may be exposed during the authentication process. To address privacy leakage caused by the authentication events, several privacy-preserving techniques, including threshold-based homomorphic encryption, zero-knowledge proof, and random permutation, are exploited to hide users’ sensitive information on the blockchain. Moreover, to improve user revocation efficiency, we integrate a cryptographic accumulator and secure hash functions into the framework where ASPs are allowed to revoke their users through a global revocation contract. Our security analysis shows that the proposed framework can achieve all desirable security and privacy properties, and a proof-of-concept prototype has been developed to demonstrate the correctness and efficiency of the proposed framework.
ELSD: Efficient Line Segment Detector and Descriptor

We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to simultaneously detect line segments and extract their descriptors in an image. Unlike the traditional pipelines that conduct detection and description separately, ELSD utilizes a shared feature extractor for both detection and description, to provide the essential line features to the higher-level tasks like SLAM and image matching in real time. First, we design a one-stage compact model, and propose to use the mid-point, angle and length as the minimal representation of line segment, which also guarantees the center-symmetry. The non-centerness suppression is proposed to filter out the fragmented line segments caused by lines’ intersections. The fine offset prediction is designed to refine the mid-point localization. Second, the line descriptor branch is integrated with the detector branch, and the two branches are jointly trained in an end-to-end manner. In the experiments, the proposed ELSD achieves the state-of-the-art performance on the Wireframe dataset and YorkUrban dataset, in both accuracy and efficiency. The line description ability of ELSD also outperforms the previous works on the line matching task.
WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting

RF fingerprinting leverages circuit-level variability of transmitters to identify them using signals they send. Signals used for identification are impacted by a wireless channel and receiver circuitry, creating additional impairments that can confuse transmitter identification. Eliminating these impairments or just evaluating them, requires data captured over a prolonged period of time, using many spatially separated transmitters and receivers. In this paper, we present WiSig; a large-scale WiFi dataset containing 10 million packets captured from 174 off-the-shelf WiFi transmitters and 41 USRP receivers over 4 captures spanning a month. WiSig is publicly available, not just as raw captures, but as conveniently pre-processed subsets of limited size, along with the scripts and examples. A preliminary evaluation performed using WiSig shows that changing receivers, or using signals captured on a different day can significantly degrade a trained classifier&#x2019;s performance. While capturing data over more days or more receivers limits the degradation, it is not always feasible, and novel data-driven approaches are needed. WiSig provides the data to develop and evaluate these approaches towards channel and receiver agnostic transmitter fingerprinting.
Removing the Bias of Integral Pose Regression

Heatmap-based detection methods are dominant for 2D human pose estimation even though regression is more intuitive. The introduction of the integral regression method, which, architecture-wise uses an implicit heatmap, brings the two approaches even closer together. This begs the question – does detection really outperform regression? In this paper, we investigate the difference in supervision between the heatmap-based detection and integral regression, as this is the key remaining difference between the two approaches. In the process, we discover an underlying bias behind integral pose regression that arises from taking the expectation after the softmax function. To counter the bias, we present a compensation method which we find to improve integral regression accuracy on all 2D pose estimation benchmarks. We further propose a simple combined detection and bias-compensated regression method that considerably outperforms state-of-the-art baselines with few added components.
Clustering by Maximizing Mutual Information Across Views

We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method consists of two heads that share the same backbone network - a "representation learning" head and a "clustering" head. The "representation learning" head captures fine-grained patterns of objects at the instance level which serve as clues for the "clustering" head to extract coarse-grain information that separates objects into clusters. The whole model is trained in an end-to-end manner by minimizing the weighted sum of two sample-oriented contrastive losses applied to the outputs of the two heads. To ensure that the contrastive loss corresponding to the "clustering" head is optimal, we introduce a novel critic function called "log-of-dot-product". Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets, improving over the best baseline by about 5-7% in accuracy on CIFAR10/20, STL10, and ImageNet-Dogs. Further, the "two-stage" variant of our method also achieves better results than baselines on three challenging ImageNet subsets.
Detecting Invisible People

Monocular object detection and tracking have improved drastically in recent years, but rely on a key assumption: that objects are visible to the camera. Many offline tracking approaches reason about occluded objects post-hoc, by linking together tracklets after the object re-appears, making use of reidentification (ReID). However, online tracking in embodied robotic agents (such as a self-driving vehicle) fundamentally requires object permanence, which is the ability to reason about occluded objects before they re-appear. In this work, we re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects, focusing on the illustrative case of people. We demonstrate that current detection and tracking systems perform dramatically worse on this task. We introduce two key innovations to recover much of this performance drop. We treat occluded object detection in temporal sequences as a short-term forecasting challenge, bringing to bear tools from dynamic sequence prediction. Second, we build dynamic models that explicitly reason in 3D from monocular videos without calibration, using observations produced by monocular depth estimators. To our knowledge, ours is the first work to demonstrate the effectiveness of monocular depth estimation for the task of tracking and detecting occluded objects. Our approach strongly improves by 11.4% over the baseline in ablations and by 5.0% over the state-of-the-art in F1 score.
Deep Relational Metric Learning

This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval. Most existing deep metric learning methods learn an embedding space with a general objective of increasing interclass distances and decreasing intraclass distances. However, the conventional losses of metric learning usually suppress intraclass variations which might be helpful to identify samples of unseen classes. To address this problem, we propose to adaptively learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. We further employ a relational module to capture the correlations among each feature in the ensemble and construct a graph to represent an image. We then perform relational inference on the graph to integrate the ensemble and obtain a relation-aware embedding to measure the similarities. Extensive experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A novel gradient-based matching via voting technique for vision-based structural displacement measurement

The effectiveness of vision-sensing system for field applications in remote measurements of structural displacement can be adversely affected by environmental factors, leading to an incomplete recording of a moving feature target in recorded video frames. The result is a non-ideal solution using traditional template matching techniques, which presents significant challenges for accurate measurements. This paper proposes a novel gradient-based matching via voting (GMV) technique to overcome environmental and operational conditions and provide reliable tracking of moved targets with different degrees of feature loss. When evaluating the region similarity between the template and a subset (called the overlapping region) of video frames in which the moved target is located to obtain a similarity score of the subset, the traditional template matching does not ascribe weights to the similarity at the pixel level, whereas GMV uses a voting scheme to weight all the pixel similarities. Three experiments were used to test GMV, evaluating its tracking accuracy and verifying its robustness with different levels of target feature loss based on a comparison with two commonly used template matching techniques. GMV proved to be capable of retrieving accurate displacement data with a high degree of accuracy, even for a moved target with up to 90% feature losses.
Cloud detection with boundary nets

In satellite optical images, clouds are normally exhibited at different scales with various boundaries. In order to accurately capture the variable visual forms of clouds, we present a deep learning based strategy, i.e., Boundary Nets, which generates a cloud mask for detecting clouds in one cloudy image. The Boundary Nets consist of two nets, i.e., (a) a scalable-boundary net, and (b) a differentiable-boundary net. The scalable-boundary net extracts multi-scale features from a cloudy image, and comprehensively characterizes clouds with variable boundary scales by a multi-scale fusion module. The multi-scale feature extraction and multi-scale fusion consistently capture clouds of different sizes, generating a multi-scale cloud mask for the cloudy image. The differentiable-boundary net characterizes the difference between the multi-scale cloud mask and the ground truth cloud mask by a residual architecture. It generates a difference cloud mask that is a complement of boundary details to the multi-scale cloud mask. Finally, the overall cloud mask is obtained by fusing the multi-scale cloud mask and the difference cloud mask. In the training process, multiple key parts of the Boundary Nets access supervision information in a distributed manner, and the losses are summed up for an overall training computation. Such distributed, overall supervision not only avoids training the two nets separately but also tightly couples the two nets into an overall framework. The experimental results validate that our Boundary Nets perform well and achieve outstanding results. The code for implementing the proposed boundary nets is available at https://gitee.com/kang_wu/boundary-nets.
Scribble-Supervised Semantic Segmentation Inference

In this paper, we propose a progressive segmentation inference (PSI) framework to tackle with scribble-supervised semantic segmentation. In virtue of latent contextual dependency, we encapsulate two crucial cues, contextual pattern propagation and semantic label diffusion, to enhance and refine pixel-level segmentation results from partially known seeds. In contextual pattern propagation, different-granular contextual patterns are correlated and leveraged to properly diffuse pattern information based on graphical model, so as to increase the inference confidence of pixel label prediction. Further, depending on high-confidence scores of estimated pixels, the initial annotated seeds are progressively spread over the image through dynamically learning an adaptive decision strategy. The two cues are finally modularized to form a close-looping update process during pixel-wise label inference. Extensive experiments demonstrate that our proposed progressive segmentation inference can benefit from the combination of spatial and semantic context cues, and meantime achieve the state-of-the-art performance on two public scribble segmentation datasets.