Reviewerly/exHarmony huggingface dataset:
Multi-task Adversarial Learning for Semi-supervised Trajectory-User Linking

Trajectory-User Linking (TUL), which aims to link the trajectories to the users who have generated them, is critically important to many real applications. Existing approaches generally consider TUL as a supervised learning problem which requires a large number of labeled trajectory-user pairs. However, in real scenarios users may not be willing to make their identities publicly available due to data privacy concerns, leading to the scarcity of labeled trajectory-user pairs. In addition, the trajectory data are usually sparse as users will not always check-in when they go to POIs. To address these issues, in this paper we propose a multi-task adversarial learning model named TULMAL for semi-supervised TUL with spare trajectory data. Specifically, TULMAL first conducts sparse trajectory completion through a proposed seq2seq model. Kalman filter is also coupled into the decoder of the seq2seq model to calibrate the generated new locations. The completed trajectories are next input into a generative adversarial learning model for semi-supervised TUL. The insight is that we consider all the users and their trajectories as a whole and perform TUL in the data distribution level. We first project users and trajectories into the common latent feature space through learning a projection function (generator) to minimize the distance between the user distribution and the trajectory distribution. Then each unlabeled trajectory will be linked to the user who is closest to it in the latent feature space without much guidance of labels. The two tasks are jointly conducted and optimized under a multi-task learning framework. Extensive experimental results on two real-world trajectory datasets demonstrate the superiority of our proposal by comparison with existing approaches.
GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction

Data imbalance, i.e., some classes may have much fewer samples than others, is a serious problem that can lead to unfavorable node classification. However, most existing GNNs are based on the assumption that node samples for different classes are balanced. In this case, directly training a GNN classifier with raw data would under-represent samples from those minority classes and result in sub-optimal performance. This paper proposes GraphMixup, a novel mixup-based framework for improving class-imbalanced node classification on graphs. However, directly performing mixup in the input space or embedding space may produce out-of-domain samples due to the extreme sparsity of minority classes; hence we construct semantic relation spaces that allow Feature Mixup to be performed at the semantic level. Moreover, we apply two context-based self-supervised techniques to capture both local and global information in the graph structure and specifically propose Edge Mixup to handle graph data. Finally, we develop a Reinforcement Mixup mechanism to adaptively determine how many samples are to be generated by mixup for those minority classes. Extensive experiments on three real-world datasets have shown that GraphMixup yields truly encouraging results for the task of class-imbalanced node classification. Codes are available at: https://github.com/LirongWu/GraphMixup .
Explaining Predictions by Characteristic Rules

Characteristic rules have been advocated for their ability to improve interpretability over discriminative rules within the area of rule learning. However, the former type of rule has not yet been used by techniques for explaining predictions. A novel explanation technique, called CEGA (Characteristic Explanatory General Association rules), is proposed, which employs association rule mining to aggregate multiple explanations generated by any standard local explanation technique into a set of characteristic rules. An empirical investigation is presented, in which CEGA is compared to two state-of-the-art methods, Anchors and GLocalX, for producing local and aggregated explanations in the form of discriminative rules. The results suggest that the proposed approach provides a better trade-off between fidelity and complexity compared to the two state-of-the-art approaches; CEGA and Anchors significantly outperform GLocalX with respect to fidelity, while CEGA and GLocalX significantly outperform Anchors with respect to the number of generated rules. The effect of changing the format of the explanations of CEGA to discriminative rules and using LIME and SHAP as local explanation techniques instead of Anchors are also investigated. The results show that the characteristic explanatory rules still compete favorably with rules in the standard discriminative format. The results also indicate that using CEGA in combination with either SHAP or Anchors consistently leads to a higher fidelity compared to using LIME as the local explanation technique.
Edge-Computing-Facilitated Nighttime Vehicle Detection Investigations With CLAHE-Enhanced Images

In this study, we propose a novel CLAHE-based nighttime image contrast enhancement approach for vehicle detection under nighttime conditions, which improves the contrast of low-quality nighttime images while preventing over-enhancement by employing the image dehazing technique. To implement and evaluate our proposed contrast enhancement method on nighttime images, we consider a scenario of using a camera-based Internet of Things (IoT)-edge computing device for traffic and road surveillance. Edge-computing and IoT technology enable significant amounts of novel studies to advance traffic system monitoring, sensing, control, and management. Considering multiple metrics of image enhancement quality, the proposed nighttime image contrast enhancement method outperforms some existing well-performing CLAHE-based methods. To provide accurate vehicle detection under nighttime conditions and different challenges, including vehicle overlapping, low-light conditions, camera vibrations, and image distortion, must be addressed. For this purpose, a deep neural network based on YOLOv5 architecture has been designed and trained using our custom-labeled dataset. The developed neural network is proven to be effective in the detection of different vehicles under low-light ambient conditions using video captured from a stationary camera. Experiments on our dataset show that the proposed contrast enhancement method greatly improves the detection performance of the trained YOLOv5 model under low-environment-light conditions compared with the model trained using unenhanced images. The model trained with enhanced images can provide an improvement of 5.7% on F1 score, 6.3% on mAP0.5, and 3.4% on mAP0.5:0.95 under specific conditions.
A compliance-based architecture for supporting GDPR accountability in cloud computing

The implementation of the General Data Protection Regulation (GDPR) in the cloud posed technical challenges for the design of compliance solutions. In particular, the accountability principle in the GDPR requires cloud providers to demonstrate their compliance, which implies that a GDPR compliance solution should maintain tamper-proof evidence for the massive data processing activities in cloud services. Additionally, the transparency of a compliance solution is essential for improving the trust of cloud users. Most of the existing solutions implemented their compliance logic as smart contracts on a blockchain to utilize its immutable transaction history for accountability and to gain user trust through its transparency. However, this widely adopted pattern in the solutions imposed the throughput constraint of blockchains on the implementation of compliance logic. In order to address this, we first conduct a requirement analysis of the GDPR accountability principle. After the analysis, we introduce a domain model of the principle and propose a modularized architecture to support the accountability in the cloud. Then, we present a prototype implementation of the architecture, in which a blockchain-based technique is used to provide immutability and data integrity for event records (e.g., data processing activities of cloud providers), while the compliance logic is not affected by the overhead of blockchains. Finally, we evaluate the prototype using benchmarks and analyses to investigate the throughput, resource consumption, and scalability of the architecture.
Signal accumulation in problems with constraints and its application to real-life magnetometry

Abstract We consider the method of signal accumulation for problems with natural constraints. We focus on the case when the constraint has the implicit form of the type <?CDATA $\|\mathbf{x}\| = C$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>∥</mml:mo> <mml:mrow> <mml:mi mathvariant="bold">x</mml:mi> </mml:mrow> <mml:mo>∥</mml:mo> <mml:mo>=</mml:mo> <mml:mi>C</mml:mi> </mml:math> with the unknown constant C to be determined as part of the problem solution. The construction and derivation are carried out based on application to the real-life magnetometry problems related to public security systems.
A joint local–global search mechanism for long-term tracking with dynamic memory network

Long-term tracking is a very popular tracking framework recently. Previous trackers mostly adopted a sliding window approach for global search, which can lead to significant time consumption. In addition, frequent appearance variations can make the tracking model lack long term adaptability. To solve the above issues, this paper proposes a joint local–global search mechanism for long-term tracking with dynamic memory network (LGST), which uses a global detector to generate “high-quality” region proposals and a dynamic memory network to store reliable template samples. Specifically, we first propose a novel global re-detection module that employs learning target channel-aware representation (TCA-R) to guide the region proposal network (RPN) to perform global search and cover the region of the target of interest to cope with the target disappearance and reappearance. Then, for the local tracker, a dynamic memory network is designed to collect the updated templates at different time periods and store reliable template samples. More importantly, we connect and feed the collected template features into the transformer encoder to generate the attention-enhanced features. Finally, we adopt a verifier to determine whether the tracked object exists or not, and dynamically switch between local search and global search mechanisms in the next image frame to form a long-term tracking framework. Experimental results on VOT-LT2018, VOT-LT2019, LaSOT, TLP, OTB2015, and UAV123 benchmarks show that our proposed tracker achieves comparable performance to state-of-the-art tracking algorithms.
Robust dual-graph discriminative NMF for data classification

In this paper, we propose a new supervised non-negative matrix factorization algorithm, named Robust Dual-graph Discriminative Non-negative Matrix Factorization (RDGDNMF). This model not only considers the differentiability between data samples, but also explores the intrinsic geometric structure information of data and feature space simultaneously, so it can obtain a more effective low-dimensional representation. Considering that the real data usually contains noise and outliers, we adopt ℓ2,12 norm to minimize the loss function of Non-negative Matrix Factorization (NMF) to improve the robustness of the algorithm. In addition, we also give the multiplication update rules and prove the convergence of the algorithm. Then, we use this model and K-Nearest Neighbor (KNN) to reduce dimension and classify data. By comparing the classification results with other advanced methods on multiple datasets, the effectiveness and robustness of the proposed method are demonstrated.
Trust Factors in the Social Figuration of Online Drug Trafficking: A Qualitative Content Analysis on a Darknet Market

The rise in illicit drug trafficking on darknet markets (DNMs) was boosted by those restrictions imposed due to the COVID-19 pandemic. This study aims to put this trend into context by exploring the characteristics of vendors’ services and reputations and understand how products are advertised and what customers tend to value. Qualitative content analysis was conducted on a sample ( n = 100) randomly selected from 6,357 product descriptions and a sample ( n = 500) randomly selected from 34,619 reviews. Both samples are from products found in the drug category of the darknet market Dark0de Reborn. On the supply side, vendors tended to provide basic information on the drugs, a mention of their high quality, the speed and stealth of delivery, their availability for responding to messages, the effects of the drugs, and sometimes even instructions for use. Regarding the demand side, customers usually praised the quality of the product, mentioned the speed and stealth-secure packaging of delivery as essentials, and expressed only a small number of issues. These results support the applicability of Norbert Elias’ social figuration theory in which the interdependencies of the actors are fueled by trust. This theoretical frame sheds light on the social value of the community of DNMs. Furthermore, the findings formulate a robust hypothesis for future research about the previously undervalued role of delivery providers.
Reinforced practical Byzantine fault tolerance consensus protocol for cyber physical systems

Cyber–physical systems (CPS) are becoming an essential component of modern life. CPS services enable information to be exchanged between physical devices and virtual systems. The increasing malicious activities causing confidential data leakage and incorrect performance of devices are posing challenges for protection against cyber threats. Therefore, development of effective solutions that can protect both CPS data and data exchange networks are extremely urgent. This study makes some improvements on Practical Byzantine Fault Tolerance (PBFT), and provides a critical analysis of the feasibility of using blockchain technology to protect constrained CPS data. It also justifies the choice of the improved PBFT for implementation on such devices and simulates the main distributed ledger scenarios. The improved PBFT works as follows: first, the client broadcasts the signed transaction data to the entire network, rather than just the master node, followed by a hash value comparison verification process; second, select the master node based on the longest chain principle, and punish the malicious node via the "blacklist" mechanism; third, add the data synchronization and verification process, as well as dynamically entering and leaving nodes via state transitions. Ultimately, the PBFT commit phase is eliminated, resulting in a two-stage process. In comparison to the original PBFT protocol, the Reinforced Practical Byzantine Fault Tolerance (RPBFT) protocol may substantially enhance system throughput and minimize consensus communication time, thereby improving overall system efficiency while guaranteeing security.
KNN-Based Consensus Algorithm for Better Service Level Agreement in Blockchain as a Service (BaaS) Systems

With services in cloud manufacturing expanding, cloud manufacturers increasingly use service level agreements (SLAs) to guarantee business processing cooperation between CSPs and CSCs (cloud service providers and cloud service consumers). Although blockchain and smart contract technologies are critical innovations in cloud computing, consensus algorithms in Blockchain as a Service (BaaS) systems often overlook the importance of SLAs. In fact, SLAs play a crucial role in establishing clear commitments between a service provider and a customer. There are currently no effective consensus algorithms that can monitor the SLA and provide service level priority. To address this issue, we propose a novel KNN-based consensus algorithm that classifies transactions based on their priority. Any factor that impacts the priority of the transaction can be used to calculate the distance in the KNN algorithm, including the SLA definition, the smart contract type, the CSC type, and the account type. This paper demonstrates the full functionality of the enhanced consensus algorithm. With this new method, the CSP in BaaS systems can provide improved services to the CSC. Experimental results obtained by adopting the enhanced consensus algorithm show that the SLA is better satisfied in the BaaS systems.
A Quick Prototype for Assessing OpenIE Knowledge Graph-Based Question-Answering Systems

Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires a thorough understanding of the underlying graph-oriented structures and, at the same time, an appropriate query language, such as SPARQL, to access relevant data. Natural language interfaces are needed to enable non-technical users to query ever more complex data. The paper proposes a question-answering approach to support end users in querying graph-oriented knowledge bases. The system pipeline is composed of two main modules: one is dedicated to translating a natural language query submitted by the user into a triple of the form &lt;subject, predicate, object&gt;, while the second module implements knowledge graph embedding (KGE) models, exploiting the previous module triple and retrieving the answer to the question. Our framework delivers a fast OpenIE-based knowledge extraction system and a graph-based answer prediction model for question-answering tasks. The system was designed by leveraging existing tools to accomplish a simple prototype for fast experimentation, especially across different knowledge domains, with the added benefit of reducing development time and costs. The experimental results confirm the effectiveness of the proposed system, which provides promising performance, as assessed at the module level. In particular, in some cases, the system outperforms the literature. Finally, a use case example shows the KG generated by user questions in a graphical interface provided by an ad-hoc designed web application.
A Fragile Image Watermarking Scheme in DWT Domain Using Chaotic Sequences and Error-Correcting Codes

With the rapid development of digital signal processing tools, image contents can be easily manipulated or maliciously tampered with. Fragile watermarking has been largely used for content authentication purposes. This article presents a new proposal for image fragile watermarking algorithms for tamper detection and image recovery. The watermarked bits are obtained from the parity bits of an error-correcting code whose message is formed from a binary chaotic sequence (generated from a secret key known to all legitimate users) and from bits of the original image. Part of the codeword (the chaotic bits) is perfectly known to these users during the extraction phase, adding security and robustness to the watermarking method. The watermarked bits are inserted at specific sub-bands of the discrete wavelet transform of the original image and are used as authentication bits for the tamper detection process. The imperceptibility, detection, and recovery of this algorithm are tested for various common attacks over digital images. The proposed algorithm is analyzed for both grayscale and colored images. Comparison results reveal that the proposed technique performs better than some existing methods.
ST-CenterNet: Small Target Detection Algorithm with Adaptive Data Enhancement

General target detection with deep learning has made tremendous strides in the past few years. However, small target detection sometimes is associated with insufficient sample size and difficulty in extracting complete feature information. For safety during autonomous driving, remote signs and pedestrians need to be detected from driving scenes photographed by car cameras. In the early period of a medical lesion, because of the small area of the lesion, target detection is of great significance to detect masses and tumors for accurate diagnosis and treatment. To deal with these problems, we propose a novel deep learning model, named CenterNet for small targets (ST-CenterNet). First of all, due to the lack of visual information on small targets in the dataset, we extracted less discriminative features. To overcome this shortcoming, the proposed selective small target replication algorithm (SSTRA) was used to realize increasing numbers of small targets by selectively oversampling them. In addition, the difficulty of extracting shallow semantic information for small targets results in incomplete target feature information. Consequently, we developed a target adaptation feature extraction module (TAFEM), which was used to conduct bottom-up and top-down bidirectional feature extraction by combining ResNet with the adaptive feature pyramid network (AFPN). The improved new network model, AFPN, was added to solve the problem of the original feature extraction module, which can only extract the last layer of the feature information. The experimental results demonstrate that the proposed method can accurately detect the small-scale image of distributed targets and simultaneously, at the pixel level, classify whether a subject is wearing a safety helmet. Compared with the detection effect of the original algorithm on the safety helmet wearing dataset (SHWD), we achieved mean average precision (mAP) of 89.06% and frames per second (FPS) of 28.96, an improvement of 18.08% mAP over the previous method.
Federated learning‐based colorectal cancer classification by convolutional neural networks and general visual representation learning

Abstract Colorectal cancer is the fourth fatal disease in the world, and the massive burden on the pathologists related to the classification of precancerous and cancerous colorectal lesions can be decreased by deep learning (DL) methods. However, the data privacy of the patients is a big challenge for being able to train deep learning models using big medical data. Federated Learning is a rising star in this era by providing the ability to train deep learning models on different sites without sacrificing data privacy. In this study, the Big Transfer model, which is a new General Visual Representation Learning method and six other classical DL methods are converted to the federated version. The effect of the federated learning is measured on all these models on four different data settings extracted from the MHIST and Chaoyang datasets. The proposed models are tested for single learning, centralized learning, and federated learning. The best AUC values of federated learning on Chaoyang are obtained by the Big Transfer and VGG models at 90.77% and 90.76%, respectively, whereas the best AUC value on MHIST is obtained by the Big Transfer model at 89.72%. The overall obtained results of models on all data settings show that the contribution of Federated Learning with respect to single learning is 4.71% and 11.68% for the “uniform” and “label‐biased” data settings of Chaoyang, respectively, and 6.89% for the “difficulty level‐biased” data setting of MHIST. Thus, it is experimentally shown that federated learning can be applied to the field of computational pathology for new institutional collaborations.