Reviewerly/exHarmony huggingface dataset:
Blockchain-based solution for COVID-19 vaccine waste reduction

Coronavirus 2019 (COVID-19) vaccines have been produced on a large scale since 2020. However, large-scale vaccine production has led to two forms of waste; namely, overproduction and underutilization. Most of today's systems and technologies used to manage waste data related to COVID-19 vaccines fall short of providing transparency, traceability, accountability, trust, and security features. In this paper, we address the problem of COVID-19 vaccines waste due to their overproduction and underutilization. We propose a blockchain-based solution that is composed of five phases: registration, commitment; production and delivery; consumption; and waste assessment. These phases make up the complete life cycle of a COVID-19 vaccine, and they are governed by several smart contracts to ensure accountability of all the actions taken by the involved entities and reduce any excessive waste caused by overproduction, overordering, or underconsumption. We ensure security, traceability, and data provenance by recording all actions through smart contracts in the form of events on an immutable ledger. We utilize decentralized storage such as the InterPlanetary File System (IPFS) to reduce the costs posed by large-sized file storage when stored on-chain. We present algorithms that describe the logic behind our developed smart contracts. We test and validate the functionalities of our proposed solution. We conduct security, cost, and scalability analyses to show that our solution is affordable, scalable, and secure. We compare our solution with the existing blockchain-based solutions to show its novelty and superiority. The smart contract code is made publicly available on GitHub.
Privacy-preserving ledger for blockchain and Internet of Things-enabled cyber-physical systems

In recent years, decentralized applications such as Distributed Ledger Technologies and blockchain have evolved as suitable applications for secure sharing of information in a decentralized fashion using privacy preserving techniques like zero-knowledge protocols. However, the biggest issue with the traditional zero-knowledge protocols on a blockchain ledger is their slow performance on big data. This paper presents the advance zero-knowledge ledger by replacing their range-proof technique with the most efficient range-proof technique based on the improved inner product based zero-knowledge proofs. Moreover, this technique allows the aggregation of multiple range-proofs into a single range-proof, which makes the current zero-knowledge ledger system more efficient than the existing one.
Towards High Accuracy Pedestrian Detection on Edge GPUs

Despite the rapid development of pedestrian detection algorithms, the balance between detection accuracy and efficiency is still far from being achieved due to edge GPUs (low computing power) limiting the parameters of the model. To address this issue, we propose the YOLOv4-TP-Tiny based on the YOLOv4 model, which mainly includes two modules, two-dimensional attention (TA) and pedestrian-based feature extraction (PFM). First, we integrate the TA mechanism into the backbone network, which increases the attention of the network to the visible area of pedestrians and improves the accuracy of pedestrian detection. Then, the PFM is used to replace the original spatial pyramid pooling (SPP) structure in the YOLOv4 to obtain the YOLOv4-TP algorithm, which can adapt to different sizes of people to obtain higher detection accuracy. To maintain detection speed, we replaced the normal convolution with a ghost network with a TA mechanism, resulting in more feature maps with fewer parameters. We constructed a one-way multi-scale feature fusion structure to replace the down-sampling process, thereby reducing network parameters to obtain the YOLOv4-TP-Tiny model. The experimental results show that the YOLOv4-TP-tiny has 58.3% AP and 31 FPS in the winder person pedestrian dataset. With the same hardware conditions and dataset, the AP of the YOLOv4-tiny is 55.9%, and the FPS is 29.
DeleSmell: Code smell detection based on deep learning and latent semantic analysis

The presence of code smells will increase the risk of failure, make software difficult to maintain, and introduce potential technique debt in the future. Although many deep-learning-based approaches have been proposed to detect code smells, most existing works suffer from the problem of incomplete feature extraction and unbalanced distribution between positive samples and negative samples. Furthermore, the accuracy of existing works can be further improved. This paper proposes a novel approach named DeleSmell to detect code smells based on a deep learning model. The dataset is built by extracting samples from 24 real-world projects. To improve the imbalance in the dataset, a refactoring tool is developed to automatically transform good source code into smelly code and to generate positive samples based on real cases. DeleSmell collects both structural features through iPlasma and semantic features via latent semantic analysis and word2vec. DeleSmell’s model includes a convolutional neural network(CNN) branch and gate recurrent unit(GRU)-attention branch. The final classification is conducted by an support vector machine(SVM). In the experimentation, the effectiveness of DeleSmell is evaluated by answering seven research questions. The experimental results show that DeleSmell improves the accuracy of brain class (BC) and brain method (BM) code smells detection by up to 4.41% compared with existing approaches, demonstrating the effectiveness of our approach.
Personalized Federated Learning on Non-IID Data via Group-based Meta-learning

Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client. One natural choice for PFL is to leverage the fast adaptation capability of meta-learning, where it first obtains a single global model, and each client achieves a personalized model by fine-tuning the global one with its local data. However, existing meta-learning-based approaches implicitly assume that the data distribution among different clients is similar, which may not be applicable due to the property of data heterogeneity in federated learning. In this work, we propose a Group-based Federated Meta-Learning framework, called G-FML , which adaptively divides the clients into groups based on the similarity of their data distribution, and the personalized models are obtained with meta-learning within each group. In particular, we develop a simple yet effective grouping mechanism to adaptively partition the clients into multiple groups. Our mechanism ensures that each group is formed by the clients with similar data distribution such that the group-wise meta-model can achieve “personalization” at large. By doing so, our framework can be generalized to a highly heterogeneous environment. We evaluate the effectiveness of our proposed G-FML framework on three heterogeneous benchmarking datasets. The experimental results show that our framework improves the model accuracy by up to 13.15% relative to the state-of-the-art federated meta-learning.
Tools or peers? Impacts of anthropomorphism level and social role on emotional attachment and disclosure tendency towards intelligent agents

Owing to the development of anthropomorphic intelligent agent (IA) designs, users consider IAs as more than just inanimate tools. Previous studies have reported that anthropomorphic features can promote users' social feedback and aid in establishing intimate human–agent relationships. The present study examined the main and interaction effects of anthropomorphism level (a human-like IA vs. robot-like IA) and social role (servant vs. mentor) on emotional attachment, information disclosure tendency, and satisfaction in a smart home. The study participants were randomly assigned into four groups with balanced gender. The results indicate that high anthropomorphism and mentor role can positively predict users' emotional attachment. Additionally, users tend to disclose more personal information to the human-servant and robot-mentor IAs than the human-mentor and robot-servant IAs. Interestingly, social presence was determined to be a positive and significant mediator between anthropomorphic design and emotional attachment. The study findings highlight the importance of social role in anthropomorphic IA design and explain the mechanism of establishing effective human–agent relationships. Moreover, both theoretical and practical implications of these findings are analyzed.
Sand Cat Swarm Optimization Based on Stochastic Variation With Elite Collaboration

The sand cat swarm optimization (SCSO) is a new heuristic optimization algorithm that simulates the behavior of sand cat groups in the desert using hearing to hunt. To address the shortcomings of low accuracy of SCSO solution, slow convergence in late iterations and easy convergence stagnation, a sand cat swarm optimization based on stochastic variation and elite collaboration (SE-SCSO) was proposed. SE-SCSO first introduces a nonlinear periodic adjustment mechanism to balance the exploration and local exploitation ability of the algorithm and accelerate the convergence of the algorithm.Secondly, the pseudo-opposition and pseudo-reflection learning mechanisms are used to speed up the optimization-seeking efficiency of the SCSO algorithm and improve the global convergence capability. Designing elite collaborative strategies with random variation to enable the algorithm to jump away from the local extrema, further improving the algorithm&#x2019;s optimization-seeking accuracy and convergence speed. In the simulation experiments, SE-SCSO is compared with Sand Cat Swarm Optimization (SCSO), Sine Cosine Algorithm (CSA), Circle Search Algorithm (SCA), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Golden Jackal Optimization (GJO) are tested for comparison. The experimental results validate the effectiveness of the proposed improvement strategies. Finally, SE-SCSO is applied to three engineering optimization problems. The results show that the improved strategy can effectively improve the performance performance of the algorithm, which gives SE-SCSO the advantages of high convergence accuracy, fast convergence, and the ability to jump out of local optimal solutions.
Stargazer: A Transformer-based Driver Action Detection System for Intelligent Transportation

Distracted driver actions can be dangerous and cause severe accidents. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. To this end, we study driver action detection using videos captured inside the vehicle. We propose Stargazer, an efficient, transformer-based system exploiting rich temporal features about the human behavioral information, with a simple yet effective action temporal localization framework. The core of our system contains an improved version of the multi-scale vision transformer network, which learns a hierarchy of robust representations. We then use a sliding-window classification strategy to facilitate temporal localization of actions-of-interest. The proposed system wins the second place in the Naturalistic Driving Action Recognition of AI City Challenge 2022 (Track 3) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The code and models are released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Eye Know You Too: Toward Viable End-to-End Eye Movement Biometrics for User Authentication

Eye movement biometrics (EMB) is a relatively recent behavioral biometric modality that may have the potential to become the primary authentication method in virtual- and augmented-reality (VR/AR) devices due to their emerging use of eye-tracking sensors to enable foveated rendering techniques. However, existing EMB models have yet to demonstrate levels of performance that would be acceptable for real-world use. The present study proposes an improved methodology for EMB with the goal of satisfying the FIDO Biometrics Requirements&#x2019; recommendation of 5% false rejection rate at 1-in-10,000 false acceptance rate. A DenseNet-based convolutional neural network is proposed that is memory-efficient, relatively quick to train, and has only &#x007E;123K learnable parameters. The model is trained over an array of different eye-tracking tasks to improve the generalizability of learned features. Authentication performance is evaluated on a held-out set of up to 59 individuals across different eye-tracking tasks, test-retest intervals, and with increasing amounts of data available for enrollment and authentication. The impact of degraded sampling rates and spatial precision on authentication performance is also briefly explored to set the stage for future research targeting modern VR/AR devices. The proposed technique not only outperforms the previous state of the art but is also the first to approach a level of authentication performance that would be acceptable for real-world use.
Adaptive momentum variance for attention-guided sparse adversarial attacks

The phenomenon that deep neural networks are vulnerable to adversarial examples has been found for several years. Under the black-box setting, transfer-based methods usually produce the adversarial examples on a white-box model, which serves as the surrogate model in the black-box attack, and hope that the same adversarial examples can also fool the black-box model. However, these methods have high success rates for the surrogate model and exhibit weak transferability for the black-box model. In addition, some studies have shown that deep neural networks are also vulnerable to sparse alterations of the input, but existing sparse attacks mainly focus on the number of attacked pixels without restricting the size of the perturbations, which is perceptible to human eyes. To address the above problems, we propose a transfer-based sparse attack method, called adaptive momentum variance based iterative gradient method with a class activation map, where the method considers a simple adaptive momentum variance and a refining perturbation mechanism to improve the transferability of adversarial examples. Also, a class activation map, which is also known as attention mechanism, is employed to explore the relationship between the number of the perturbed pixels and the attack performance in the case of limiting the intensity of perturbation. The proposed method is compared with a number of the state-of-the-art transfer-based adversarial attack methods on the ImageNet dataset, and the empirical results demonstrate that our method achieves a significant increase in transferability with only attacking about 50% of the pixels.
An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models

The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websites and domains. Such threats open the doors for many critical attacks such as spams, spyware, phishing, and malware. Therefore, detecting malicious URL is crucially important to prevent the occurrence of many cybercriminal activities. In this study, we examined a set of machine learning (ML) and deep learning (DL) models to detect malicious websites using a dataset comprising 66,506 records of URLs. We engineered three different types of features including lexical-based, network-based and content-based features. To extract the most discriminative features in the dataset, we applied several features selection algorithms, namely, correlation analysis, Analysis of Variance (ANOVA), and chi-square. Finally, we conducted a comparative performance evaluation for several ML and DL models considering set of criteria commonly used to evaluate such models. Results depicted that Naïve Bayes (NB) was the best model for detecting malicious URLs using the applied data with an accuracy of 96%. This research has made contribution to the field by conducting significant features engineering and analysis to identify the best features for malicious URLs predictions, compare different models and achieve a high accuracy using a large new URL dataset.
Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media

In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They choose to use Word2Vec to construct the word vector space and CBOW (continuous bag of words) to obtain the contextual information of the words. If there is a word that has similar contextual information to the OOV, the word can be used to understand the OOV. They chose the Weibo corpus as the dataset for the experiments. The results show that the proposed model achieves 97.10% accuracy, which is better than Skip-Gram by 8.53%.
A data-driven, comparative review of the academic literature and news media on blockchain-enabled supply chain management: Trends, gaps, and research needs

The application of blockchain technology in supply chain management has become a popular area of discussion in research and practice. This paper develops a computational, data-driven synthesis of the scholarly literature versus news media on BT-enabled supply chain management (BT-SCM) to uncover major trends, understand how academic research is aligned with business practice, and find out existing gaps. Through text mining and topic modeling of 1148 full-text research papers and 5130 news articles, major themes within each domain, their patterns of evolution over time, and the depth and breadth of their associations were identified. Mapping analyses were also conducted based on the supply chain operations reference (SCOR) model and the main SCM research streams to further explore existing knowledge gaps. The findings revealed that BT-enabled supply chain asset management, BT-enabled reverse logistics and closed-loop supply chain, and actual versus anticipated performance outcomes of BT-SCM are among important pathways for future research. The findings also highlighted where there is more need to enhance the practical relevance of BT-SCM research considering advances in business adoption. The paper provides a comprehensive, unbiased assessment of the BT-SCM knowledge landscape and a taxonomy of the research questions related to the technical and managerial aspects of BT-SCM that are particularly useful for the community of researchers in the field. It offers a practical framework that can be applied to assess the academic literature on other emerging technologies in SCM where state-of-the-practice is key to guiding research efforts.
RPformer: A Robust Parallel Transformer for Visual Tracking in Complex Scenes

The Siamese architecture has shown remarkable performance in the field of visual tracking. Although the existing Siamese-based tracking methods have achieved a relative balance between accuracy and speed, the performance of many trackers in complex scenes is often unsatisfactory, which is mainly caused by interference factors, such as target scale changes, occlusion, and fast movement. In these cases, excessive trackers cannot employ sufficiently the target feature information and face the dilemma of information loss. In this work, we propose a novel parallel Transformer network architecture to achieve robust visual tracking. The proposed method designs the Transformer-1 module, the Transformer-2 module, and the feature fusion head (FFH) based on the attention mechanism. The Transformer-1 module and the Transformer-2 module are regarded as corresponding complementary branches in the parallel architecture. The FFH is used to integrate the feature information of the two parallel branches, which can efficiently exploit the feature dependence relationship between the template and the search region, and comprehensively explore rich contextual information. Finally, by combining the core ideas of Siamese and Transformer, we present a simple and robust tracking framework called RPformer, which does not require any prior knowledge and avoids the trouble of adjusting hyperparameters. Numerous experiments show that the proposed tracking method achieves more outstanding performance than the state-of-the-art trackers on seven tracking benchmarks, which can meet the real-time requirements at a running speed exceeding 50.0 frames/s.
A comprehensive analysis of blockchain and its applications in intelligent systems based on IoT, cloud and social media

Distributed Ledger Technology (DLT) driven blockchain is currently one of the most promising technology revolutions with enormous potential across a wide range of applications. Distributed ledger is essentially a distributed and encrypted database that can address several concerns pertaining to Internet security and trust with transparency. In fact, the scope of blockchain applications is broadening with each passing day. The exclusive features of safe and transparent data exchange provided by blockchain technology provide compelling arguments for its implementation in a variety of application scenarios. In this paper, we present the comprehensive view of blockchain and its related concepts. Though, there exists intensive research on these domains; however, these fields are still dealing with several security issues, data reliability and storage and scalability. Blockchain has emerged as a critical technology that can address these issues through its features like decentralization, transparency, immutability and auditability. Building trust in distributed systems without the need for authority is a technological advancement that can be well leveraged by domain, like, Internet of Things (IoT), cloud and social media. Technologies like IoT and Cloud computing can be perceived as the trivial ones to get benefitted from the blockchain. However, the integration of social media and blockchain can revolutionize the domains, like, content creating and sharing, fake news scourge, trademarking and rights management. In this paper, we illustrate the integration of blockchain with IoT, cloud and social media and the related issues and challenges. Moreover, we also show some of the major research works done in each domain.
QOC

Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC learning, the training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain PQC gradients is parameter shift whose cost scales linearly with the number of qubits. We present QOC, the first experimental demonstration of practical on-chip PQC training with parameter shift. Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naïve parameter shift have low fidelity and thus degrading the training accuracy. To this end, we further propose probabilistic gradient pruning to firstly identify gradients with potentially large errors and then remove them. Specifically, small gradients have larger relative errors than large ones, thus having a higher probability to be pruned. We perform extensive experiments with the Quantum Neural Network (QNN) benchmarks on 5 classification tasks using 5 real quantum machines. The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks. The probabilistic gradient pruning brings up to 7% PQC accuracy improvements over no pruning. Overall, we successfully obtain similar on-chip training accuracy compared with noise-free simulation but have much better training scalability. The QOC code is available in the TorchQuantum library.