Reviewerly/exHarmony huggingface dataset:
Detecting code smells using industry-relevant data

Code smells are patterns in source code associated with an increased defect rate and a higher maintenance effort than usual, but without a clear definition. Code smells are often detected using rules hard-coded in detection tools. Such rules are often set arbitrarily or derived from data sets tagged by reviewers without the necessary industrial know-how. Conclusions from studying such data sets may be unreliable or even harmful, since algorithms may achieve higher values of performance metrics on them than on models tagged by experts, despite not being industrially useful. Our goal is to investigate the performance of various machine learning algorithms for automated code smell detection trained on code smell data set(MLCQ) derived from actively developed and industry-relevant projects and reviews performed by experienced software developers. We assign the severity of the smell to the code sample according to a consensus between the severities assigned by the reviewers, use the Matthews Correlation Coefficient (MCC) as our main performance metric to account for the entire confusion matrix, and compare the median value to account for non-normal distributions of performance. We compare 6720 models built using eight machine learning techniques. The entire process is automated and reproducible. Performance of compared techniques depends heavily on analyzed smell. The median value of our performance metric for the best algorithm was 0.81 for Long Method, 0.31 for Feature Envy, 0.51 for Blob, and 0.57 for Data Class. Random Forest and Flexible Discriminant Analysis performed the best overall, but in most cases the performance difference between them and the median algorithm was no more than 10% of the latter. The performance results were stable over multiple iterations. Although the F-score omits one quadrant of the confusion matrix (and thus may differ from MCC), in code smell detection, the actual differences are minimal.
Pose-guided counterfactual inference for occluded person re-identification

Occluded person re-identification (ReID) is a challenging task as the body is partially occluded by obstacles in crowd scenarios. Many previous studies employed an attention mechanism to focus on fine-grained local information with conventional likelihood while ignoring the inherent causality between the final prediction results and attention, especially occluded person always possesses biased clues. To address this problem, we propose a Pose-Guided Multi-Attention Network (PGMA-Net) for occluded person ReID in an end-to-end manner. PGMA-Net contains two main novel components: Pose-Guided Counterfactual Inference Branch (PGCIB) and Striped- and Patched-Attention Module (SPAM). The PGCIB jointly explores the causality between the predicted identities and input clues to alleviate the negative effects brought by occluded bias. Specifically, the counterfactual inference can directly guide the attention learning process via the counterfactual intervention. The SPAM generates a set of attention vectors for storing part prototypes over multiple rounds of attention. We empirically demonstrate that PGMA-Net can improve the recognition in both occluded and non-occluded ReID. With the above designs, our framework achieves 52.2% in mAP and 62.0% in top-1 on the Occluded-DukeMTMC dataset, surpassing the baseline by a large margin.
No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans

Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound scans to evaluate the possibility of inexpensive, fully automated breast ultrasound acquisition and preliminary interpretation without an experienced sonographer or radiologist. This study was conducted using examinations from a curated data set from a previously published clinical study of breast VSI. Examinations in this data set were obtained by medical students without prior ultrasound experience who performed VSI using a portable Butterfly iQ ultrasound probe. Standard of care ultrasound exams were performed concurrently by an experienced sonographer using a high-end ultrasound machine. Expert-selected VSI images and standard of care images were input into S-Detect which output mass features and classification as "possibly benign" and "possibly malignant." Subsequent comparison of the S-Detect VSI report was made between 1) the standard of care ultrasound report by an expert radiologist, 2) the standard of care ultrasound S-Detect report, 3) the VSI report by an expert radiologist, and 4) the pathological diagnosis. There were 115 masses analyzed by S-Detect from the curated data set. There was substantial agreement of the S-Detect interpretation of VSI among cancers, cysts, fibroadenomas, and lipomas to the expert standard of care ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), the standard of care ultrasound S-Detect interpretation (Cohen's κ = 0.79 (0.65-0.94 95% CI), p<0.0001), the expert VSI ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), and the pathological diagnosis (Cohen's κ = 0.80 (0.64-0.95 95% CI), p<0.0001). All pathologically proven cancers (n = 20) were designated as "possibly malignant" by S-Detect with a sensitivity of 100% and specificity of 86%. Integration of artificial intelligence and VSI could allow both acquisition and interpretation of ultrasound images without a sonographer and radiologist. This approach holds potential for increasing access to ultrasound imaging and therefore improving outcomes related to breast cancer in low- and middle- income countries.
Multipartite Entanglement in Rabi-Driven Superconducting Qubits

The exploration of highly connected networks of qubits is invaluable for implementing various quantum algorithms and simulations, as it allows for entangling qubits with reduced circuit depth. Here, we demonstrate a multiqubit sideband tone-assisted Rabi-driven gate. Our scheme is inspired by the ion-qubit Mølmer-Sørensen gate and is mediated by a shared photonic mode and Rabi-driven superconducting qubits, which relaxes restrictions on qubit frequencies during fabrication and supports scalability. We achieve a two-qubit gate with a maximum state fidelity of 95% in 310 ns, a three-qubit gate with a state fidelity of 90.5% in 217 ns, and a four-qubit gate with a state fidelity of 66% in 200 ns. Furthermore, we develop a model of the gate that shows that the four-qubit gate is limited by shared resonator losses and the spread of qubit-resonator couplings, which must be addressed to reach high-fidelity operations.7 MoreReceived 17 July 2022Accepted 24 October 2022Corrected 20 December 2022DOI:https://doi.org/10.1103/PRXQuantum.3.040322Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum controlQuantum entanglementQuantum gatesQuantum information processingSuperconductorsQuantum InformationCondensed Matter, Materials & Applied Physics
Securing the Ethereum from Smart Ponzi Schemes: Identification Using Static Features

Malware detection approaches have been extensively studied for traditional software systems. However, the development of blockchain technology has promoted the birth of a new type of software system–decentralized applications. Composed of smart contracts, a type of application that implements the Ponzi scheme logic (called smart Ponzi schemes) has caused irreversible loss and hindered the development of blockchain technology. These smart contracts generally had a short life but involved a large amount of money. Whereas identification of these Ponzi schemes before causing financial loss has been significantly important, existing methods suffer from three main deficiencies, i.e., the insufficient dataset, the reliance on the transaction records, and the low accuracy. In this study, we first build a larger dataset. Then, a large number of features from multiple views, including bytecode, semantic, and developers, are extracted. These features are independent of the transaction records. Furthermore, we leveraged machine learning methods to build our identification model, i.e., Mul ti-view Cas cade Ensemble model (MulCas). The experiment results show that MulCas can achieve higher performance and robustness in the scope of our dataset. Most importantly, the proposed method can identify smart Ponzi scheme at the creation time.
Multiscale spatial temporal attention graph convolution network for skeleton-based anomaly behavior detection

Anomaly behavior detection plays a significant role in emergencies such as robbery. Although a lot of works have been proposed to deal with this problem, the performance in real applications is still relatively low. Here, to detect abnormal human behavior in videos, we propose a multiscale spatial temporal attention graph convolution network (MSTA-GCN) to capture and cluster the features of the human skeleton. First, based on the human skeleton graph, a multiscale spatial temporal attention graph convolution block (MSTA-GCB) is built which contains multiscale graphs in temporal and spatial dimensions. MSTA-GCB can simulate the motion relations of human body components at different scales where each scale corresponds to different granularity of annotation levels on the human skeleton. Then, static, globally-learned and attention-based adjacency matrices in the graph convolution module are proposed to capture hierarchical representation. Finally, extensive experiments are carried out on the ShanghaiTech Campus and CUHK Avenue datasets, the final results of the frame-level AUC/EER are 0.759/0.311 and 0.876/0.192, respectively. Moreover, the frame-level AUC is 0.768 for the human-related ShanghaiTech subset. These results show that our MSTA-GCN outperforms most of methods in video anomaly detection and we have obtained a new state-of-the-art performance in skeleton-based anomaly behavior detection.
Auto-weighted collective matrix factorization with graph dual regularization for multi-view clustering

Multi-view clustering (MVC) is an attractive clustering paradigm that can incorporate comprehensive information from multiple views. Among the MVC schemes, collective matrix factorization (CMF) has shown its great power in extracting shared information of multi-view data. Based on CMF, we propose a novel unified MVC framework, named Auto-weighted Collective Matrix Factorization with Graph Dual Regularization (ACMF-GDR). Specifically, we assign adaptive weights for each view and incorporate the smoothing cluster structure learning term to construct a unified auto-weighted CMF for MVC. Our ACMF-GDR model can obtain the cluster labels and common representations of the samples in a one-step manner. Furthermore, to make the common representations discriminative, graph dual regularization terms with orthogonality constraints are adopted on multiple views to preserve the geometrical structure of the decomposed factors simultaneously. Experimental results show the superior clustering performance of the proposed method.
Multi-dimensional shared representation learning with graph fusion network for Session-based Recommendation

The Session-based Recommendation (SBR) system aims to forecast anonymous users' short-term decisions. Many prior research have demonstrated that using Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) to solve SBR tasks can lead to excellent results. However, the existing SBR models only use single feature extraction method to represent an item in the recommendation process. These models either use the GNN methods which ignore the sequence location features in the training process or use RNN methods which cannot obtain the weight features of every item in the sequence. All of them fail to extract features of different dimensions at the same time, which results in low-quality item representations. To tackle the aforementioned issue, this research introduces a novel method called Multi-dimensional Shared Representation Learning (MSR). (i) To get multi-dimensional features, we use the multi-dimensional feature extraction in a double-flow way based on transformer layers and graph attention network (GAT) layers. (ii) Through the MSR module, we train the joint representation of items in the session by using multiple GNNs, then merge the multi-dimensional features by using an attention mechanism. A variety of experiments have been carried out and the simulation results demonstrate the designed strategy largely surpasses the state-of-the-art proposal in recommendation accuracy.
Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm

The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C light to automate the disinfection processes. But performing this process in an optimum way introduces some challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the environment. In this work, both challenges are addressed with the design of a complete coverage path planning (CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN), a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed. One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on the performance of the robot navigation.
Meta-Regulation: Adaptive Adjustment to Block Size and Creation Interval for Blockchain Systems

Once deployed, a decentralized blockchain system ensures that it will operate faithfully so that no one can interfere with or manipulate its predefined regulations, such as block size and block creation interval investigated in this paper. However, fixed regulations prevent that system from adapting to the change of the environment, such as increasing the underlying network capacity, and result in sub-optimal performance. For example, Bitcoin remains at 7 TPS (transactions per second), even operating over the current Internet. In this paper, we propose a new paradigm for defining the behavior of a consensus system, named as Meta-Regulation, which allows autonomous evolution of the system behavior. A meta-regulation adjusts the actual behavior of a consensus system in response to the changing capacity of the underlying infrastructure and the community of participants. We demonstrate the effectiveness of the proposed meta-regulation by achieving significantly improved throughput and latency for Bitcoin, adapted to the current capacity of the Internet. Our experimental results show that Meta-Regulation can achieve at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times $ </tex-math></inline-formula> performance improvement over Bitcoin network deployed in 2009, resulting in 49.7 TPS or 68% reduction confirmation latency by fully utilizing the bandwidth and the computing power of average network nodes.
Modeling graph-structured contexts for image captioning

The performance of image captioning has been significantly improved recently through deep neural network architectures combining with attention mechanisms and reinforcement learning optimization. Exploring visual relationships and interactions between different objects appearing in the image, however, is far from being investigated. In this paper, we present a novel approach that combines scene graphs with Transformer, which we call SGT, to explicitly encode available visual relationships between detected objects. Specifically, we pretrain an scene graph generation model to predict graph representations for images. After that, for each graph node, a Graph Convolutional Network (GCN) is employed to acquire relationship knowledge by aggregating the information of its local neighbors. As we train the captioning model, we feed the potential relation-aware information into the Transformer to generate descriptive sentence. Experiments on the MSCOCO dataset and the Flickr30k dataset validate the superiority of our SGT model, which can realize state-of-the-art results in terms of all the standard evaluation metrics.
A Blockchain-Based cryptographic interaction method of digital museum collections

We constructed a cryptographic interaction method museum art exchange protocol (MAXP) for museum digital collections on the basis of blockchain technology. Using our method, we build a digital collection exchange system on Ethereum to realize the digital collection’s online exchange between two museums. Compared with the traditional centralized collection digital resource database method, MAXP can avoid the security risks caused by subjective factors and force majeure factors in the exchange process of digital collections, such as hackers and network viruses. In our exchange system we have built, the expression of content covered by digital collections is more convenient, and copyright disputes can be quickly resolved. Concurrently, given the decentralization and anonymity of the blockchain, a regulatory mechanism has been added to MAXP to avoid fraud, illegal fundraising, money laundering and smuggling. The regulatory mechanism we constructed is a dual receiver public key encryption scheme based on the Diffie-Hellman algorithm and the SM2 elliptic curve public key encryption algorithm. The sender encrypts the collection data, and both receivers can decrypt the messages using their respective private keys. One of the receivers is the museum that obtained the collection information, and the other receiver is the regulator. These two receivers can decrypt simultaneously, and the regulator can regulate the information exchange on the blockchain. The Beijing Planetarium and the Beijing Museum of Natural History have completed the exchange of collections through the system we have built. The analysis results show that the regulatory scheme based on the exchange blockchain system of the museum’s digital collections proves to be feasible, with security and expansibility. Our new encrypted exchange management method of digital collections in museums can effectively promote the exchange of collections between museums, and is of great significance to the promotion of cultural heritage and the dissemination of scientific knowledge.
Reconstruct face from features based on genetic algorithm using GAN generator as a distribution constraint

Face recognition based on deep convolutional neural networks (CNN) shows superior accuracy performance attributed to the high discriminative features extracted. Yet, the security and privacy of the extracted features from deep learning models (deep features) have often been overlooked. This paper proposes the reconstruction of face images from deep features without accessing the CNN network configurations as a constrained optimization problem. Such optimization minimizes the distance between the features extracted from the original face image and the reconstructed face image. Instead of directly solving the optimization problem in the image space, we innovatively reformulate the problem by looking for a latent vector of a generative adversarial networks (GAN) generator, then use it to generate the face image. The GAN generator serves a dual role in this novel framework, i.e., face distribution constraint of the optimization goal and a face generator. To solve this optimization problem, We present an optimization approach based on a Genetic Algorithm. On top of the novel optimization task, we also propose an attack pipeline to impersonate the target user based on the generated face image. Our results show that the generated face images can achieve a state-of-the-art successful attack rate of 99.33% on Labeled Faces in the Wild (LFW) under type-I attack at a false accept rate of 0.1%. Our work sheds light on biometric deployment to meet privacy-preserving and security policies.
Parameterizing echo state networks for multi-step time series prediction

Prediction of multi-dimensional time-series data, which may represent such diverse phenomena as climate changes or financial markets, remains a challenging task in view of inherent nonlinearities and non-periodic behavior. In contrast to other recurrent neural networks, echo state networks (ESNs) are attractive for (online) learning due to lower requirements w.r.t. training data and computational power. However, the randomly-generated reservoir renders the choice of suitable hyper-parameters as an open research topic. We systematically derive and exemplarily demonstrate design guidelines for the hyper-parameter optimization of ESNs. For the evaluation, we focus on the prediction of chaotic time series, an especially challenging problem in machine learning. Our findings demonstrate the power of a hyper-parameter-tuned ESN when auto-regressively predicting time series over several hundred steps. We found that ESNs’ performance improved by 85.1%-99.8% over an already wisely chosen default parameter initialization. In addition, the fluctuation range is considerably reduced such that significantly worse performance becomes very unlikely across random reservoir seeds. Moreover, we report individual findings per hyper-parameter partly contradicting common knowledge to further, help researchers when training new models.
Multi-search-routes-based methods for minimizing makespan of homogeneous and heterogeneous resources in Cloud computing

Cloud computing, as a large-scale distributed computing system dynamically providing elastic services, is designed to meet the requirement of delivering computing services to users as subscription-oriented services. In general, the problems of resource scheduling in Cloud computing like minimizing makespan are usually NP-Hard problems. Various common algorithms including heuristic, meta-heuristic and machine learning are applied in resource scheduling of Cloud computing to obtain the solutions, which however are still probable and imperative to be optimized. Through innovatively applying heuristic algorithms namely LPT (Longest Processing Time) and BFD (Best Fit Decreasing) as the basic search routes and integrating these with neighborhood search algorithm namely OneStep, this paper proposes multi-search-routes-based algorithms containing LPT-OneStep, BFD-OneStep and their combinations for the sake of enhancing theoretical performance and improving solutions of scheduling schemes especially for problems of minimizing makespan for homogeneous and heterogeneous resources. Theoretical derivations prove that the proposed algorithms possess better theoretical approximation ratios for P||Cmax. Extensive experiments on simulation environment demonstrate the proposed algorithms outperform than corresponding compared algorithms for minimizing makespan problems in both homogenous resources and heterogeneous resources, which validates the superiority of the proposed algorithms.