Reviewerly/exHarmony huggingface dataset:
GradAlign+: Empowering Gradual Network Alignment Using Attribute Augmentation

Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their satisfactory performance is not without prior anchor link information and/or node attributes, which may not always be available. In this paper, we propose Grad-Align+, a novel NA method using node attribute augmentation that is quite robust to the absence of such additional information. Grad-Align+ is built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers only a part of node pairs until all node pairs are found. Specifically, Grad-Align+ is composed of the following key components: 1) augmenting node attributes based on nodes' centrality measures, 2) calculating an embedding similarity matrix extracted from a graph neural network into which the augmented node attributes are fed, and 3) gradually discovering node pairs by calculating similarities between cross-network nodes with respect to the aligned cross-network neighbor-pair. Experimental results demonstrate that Grad-Align+ exhibits (a) superiority over benchmark NA methods, (b) empirical validation of our theoretical findings, and (c) the effectiveness of our attribute augmentation module.
Learning Hypersphere for Few-shot Anomaly Detection on Attributed Networks

The existence of anomalies is quite common, but they are hidden within the complex structure and high-dimensional node attributes of the attributed networks. As a latent hazard in existing systems, anomalies can be transformed into important instruction information once we detect them, e.g., computer network admins can react to the leakage of sensitive data if network traffic anomalies are identified. Extensive research in anomaly detection on attributed networks has proposed various techniques, which do improve the quality of data in networks, while they rarely cope with the few-shot anomaly detection problem. Few-shot anomaly detection task with only a few dozen labeled anomalies is more practical since anomalies are rare in number for real-world systems.
A workload-driven method for designing aggregate-oriented NoSQL databases

Due to the scalability and availability problems with traditional relational database systems, a variety of NoSQL stores have emerged over the last decade to deal with big data. How data are structured in a NoSQL store has a large impact on the query and update performance and the storage usage. Thus, different from the traditional database design, not only the data structure but also the data access patterns need to be considered in the design of NoSQL database schemas. In this paper, we present a general workload-driven method for designing key-value, wide-column, and document NoSQL database schemas. We first present a generic logical model Query Path Graph (QPG) that can represent the data structures of the UML class diagram. We also define mappings from the SQL-based query patterns to QPG and from QPG to aggregate-oriented NoSQL schemas. We use a cost model to measure the query and update performance and optimize the QPG schemas. We evaluate the proposed method with several typical case studies by simulating workloads on databases with different schema designs. The results demonstrate that our method preserves the generality and the quality of the design.
Few-Shot Continuous Authentication for Mobile-Based Biometrics

The rapid growth of smartphone financial services raises the need for secure mobile authentication. Continuous authentication is a user-friendly way to strengthen the security of smartphones by implicitly monitoring a user’s identity through sessions. Mobile continuous authentication can be viewed as an anomaly detection problem in which models discriminate between one genuine user and the rest of the impostors (anomalies). In practice, complete impostor profiles are hardly available due to the time and monetary cost, while leveraging genuine data alone yields poorly generalized models due to the lack of knowledge about impostors. To address this challenge, we recast continuous mobile authentication as a few-shot anomaly detection problem, aiming to enhance the inference robustness of unseen impostors by using partial knowledge of available impostor profiles. Specifically, we propose a novel deep learning-based model, namely a local feature pooling-based temporal convolution network (LFP-TCN), which directly models raw sequential mobile data, aggregating global and local feature information. In addition, we introduce a random pattern mixing augmentation to generate class-unconstrained impostor data for the training. The augmented pool enables characterizing various impostor patterns from limited impostor data. Finally, we demonstrate practical continuous authentication using score-level fusion, which prevents long-term dependency or increased model complexity due to extended re-authentication time. Experiments on two public benchmark datasets show the effectiveness of our method and its state-of-the-art performance.
Development of a Framework to Assess Challenges to Virtual Education in an Emergency Remote Teaching Environment: A Developing Country Student Perspective—The Case of Peru

The COVID-19 pandemic forced most countries’ higher-education systems to shift to distance learning, which has been called either “Corona Teaching” or, more formally, “Emergency Remote Teaching (ERT).” Students were suddenly faced with a new class format delivery and the many challenges of virtual education. The present study aims to identify and measure the challenges in three stages: (1) a qualitative method approach was used to gather the opinions of 50 students that were then analyzed and coded to identify their perceived major challenges; (2) a survey was completed by 165 students to prioritize the relative importance of the previously identified challenges using the AHP as the weighting approach; (3) an assessment framework was developed, using statistical techniques to measure the extent of the challenges for specific stakeholders based on survey responses. The main challenges students face are inadequate physical facilities at home, difficulties with the learning platforms, and financial concerns. These results are applicable beyond the present research context. For the first time, an ERT assessment framework of the challenges was developed using composite indicators derived from students’ opinions and perspectives. This ERT framework allows for the exploration of a community of students’ vulnerability to the challenges within the context of an emergency remote environment.
Source-free unsupervised domain adaptation with maintaining model balance and diversity

Source-free unsupervised domain adaptation (SFUDA) uses the knowledge learned from the source domain pre-trained model to perform the task on the target domain without directly accessing the source domain. However, the source domain pre-trained model is biased towards the source domain but deviates from the target domain, so its pseudo-label on the target domain tends to have a large noise. Thus, we should avoid using noisy pseudo-labels to train the model. In this article, we propose a novel kind of source-free unsupervised domain adaptation with maintaining model balance and diversity (SFMBD), which designs a target domain-specific classifier whose classification boundary is far from the high-density area of the target domain feature distribution. In addition, we keep the model balance and promote the model diversity while maintaining its ability to discriminate the target domain. Experimental evaluation of multiple benchmark datasets illustrates the effectiveness of our proposed approach on SFUDA.
Missing-edge aware knowledge graph inductive inference through dual graph learning and traversing

Knowledge graph (KG) is a kind of structured human knowledge of modeling the relations between real-world entities. This paper studies the KG inductive inference problem, i.e., predicting the relations for out-of-KG entities. However, due to the incomplete nature of the KGs, the connections of some relations are missing. This makes existing differentiable rule learning methods unable to represent some possible rule candidates, which will further affect the inductive inference result. To solve this challenge, our research hypothesis is that the semantics of relation’s argument can be well used to reflect the possible connections between relations. We propose a KG inductive inference model, RuleNet, which consists of two parts. Firstly, a query-dependent dual graph construction method is proposed, which is able to learn the relation connections using the information of the relation’s argument. Secondly, a dual graph traversing method is proposed, which is able to traverse all possible rule candidates even if some rules cannot be formed due to the missing edges. Performance of the proposed methods is evaluated using the FB15K237 (10%–20%), WN18RR (10%–20%) and YAGO3-10 (10%–20%) benchmarks. Experimental results show that RuleNet achieves a superior performance compared with many strong baselines. Ablation studies have verified the effectiveness of the proposed network components. Qualitative analysis shows that RuleNet can learn meaningful dual graph and logic rules.
A Practical Approach to the Analysis and Optimization of Neural Networks on Embedded Systems

The exponential increase in internet data poses several challenges to cloud systems and data centers, such as scalability, power overheads, network load, and data security. To overcome these limitations, research is focusing on the development of edge computing systems, i.e., based on a distributed computing model in which data processing occurs as close as possible to where the data are collected. Edge computing, indeed, mitigates the limitations of cloud computing, implementing artificial intelligence algorithms directly on the embedded devices enabling low latency responses without network overhead or high costs, and improving solution scalability. Today, the hardware improvements of the edge devices make them capable of performing, even if with some constraints, complex computations, such as those required by Deep Neural Networks. Nevertheless, to efficiently implement deep learning algorithms on devices with limited computing power, it is necessary to minimize the production time and to quickly identify, deploy, and, if necessary, optimize the best Neural Network solution. This study focuses on developing a universal method to identify and port the best Neural Network on an edge system, valid regardless of the device, Neural Network, and task typology. The method is based on three steps: a trade-off step to obtain the best Neural Network within different solutions under investigation; an optimization step to find the best configurations of parameters under different acceleration techniques; eventually, an explainability step using local interpretable model-agnostic explanations (LIME), which provides a global approach to quantify the goodness of the classifier decision criteria. We evaluated several MobileNets on the Fudan Shangai-Tech dataset to test the proposed approach.
Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter

The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method's root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest.
Large Mask Image Completion with Conditional GAN

Recently, learning-based image completion methods have made encouraging progress on square or irregular masks. The generative adversarial networks (GANs) have been able to produce visually realistic and semantically correct results. However, much texture and structure information will be lost in the completion process. If the missing part is too large to provide useful information, the result will be ambiguity, residual shadow, and object confusion. In order to complete large mask images, we present a novel model using conditional GAN called coarse-to-fine condition GAN (CF CGAN). We use a coarse-to-fine generator with symmetry and new perceptual loss based on VGG-16. The generator is symmetric in structure. For large mask image completion, our method produces visually realistic and semantically correct results. The generalization ability of our model is also excellent. We evaluate our model on the CelebA dataset and use FID, LPIPS, and SSIM as the metrics. Experiments demonstrate superior performance in terms of both quality and reality in free-form image completion.
A Digital Media Subscription Management System Combined with Blockchain and Proxy Re-Encryption Mechanisms

The subscription economy was born because the relationship between creators and customers is different than it used to be. The era of the creator economy seems to be filled with boundless promise, but at the end of the day, creators are just slaves to tech giants. Neither the control of the content created, nor the money made in their pockets is in complete control of the creator. The blockchain can completely solve these injustices monopolized by enterprises. In the blockchain era, all kinds of creations from music, and video-to-text can be turned into assets that can be purchased and traded through smart contracts. In the music industry, for example, creators do not need to share profits with streaming platforms and record labels and get all the benefits directly. In addition, when the content created is on the chain, every transaction will be recorded on the blockchain, and everyone can inquire about it, avoiding opacity or causing disputes in the future. However, with the structure of the standard blockchain, as long as the registration is successful, each role in the chain will have permanent data access rights, and the flexibility of access control is poor. Therefore, this study proposes a digital media subscription mechanism based on the Hyperledger blockchain architecture combined with proxy re-encryption. We use symmetric and asymmetric cryptography, smart contracts, and algorithms to design our protocol. When the licensee violates the agreement with the creator, the creator can revoke the access rights to the digital media of the licensee at any time, to realize more secure and convenient digital media transmission. The proposed scheme meets various security requirements of blockchain architecture, and we have also applied the BAN logic proof model to evaluate the correctness of the proposed scheme. This study also proposes an arbitration mechanism when the dispute occurs, and performed well in terms of communication and computational costs.
Embedding-based team formation for community question answering

Finding a qualified individual who can independently answer a question on a community question answering platform is becoming more challenging due to the increasing multidisciplinary nature of posted questions. As such, finding a group of experts to collaboratively answer the questions is of paramount importance. To this end, we propose a novel approach to form teams of experts who can collectively answer new questions. The proposed approach, called team2box, learns neural embedding representations based on the content of the posted questions, experts’ engagement with these questions, and past expert collaboration history in order to form a team to answer the posted question. It embeds experts and questions as points and existing teams as regions within the embedding space. Therefore, team2box forms a team whose members (1) collectively cover the knowledge required to answer a question, (2) have successful past experience in jointly answering similar questions, and (3) can work efficiently together to answer the question. Extensive experiments on real-life datasets from Stack Exchange show that team2box outperforms the state-of-the-art by discovering teams with on average 38.97% more covering the skills required to answer new questions and employing experts with collectively a high expertise level.
Solving maximum weighted matching on large graphs with deep reinforcement learning

Maximum weighted matching (MWM), which finds a subset of vertex-disjoint edges with maximum weight, is a fundamental topic with a wide spectrum of applications in various fields, such as biotechnology, social analysis, web services, etc. However, traditional methods cannot achieve a good balance between cost and quality. Inspired by the reinforcement learning techniques, we propose an MWM framework, L2M, with a Deep Reinforcement Learning (DRL) model, which can efficiently and effectively solve the MWM problem on large-scale general graphs. First, in contrast to traditional DRL methods that define the Markov Decision Process (MDP) without considering the efficiency issue, we represent MWM as an MDP that is carefully designed to accelerate computation. In particular, this MDP supports selecting multiple edges for each action and uses a pruning method to reduce search space efficiently. Second, since none of the existing DRL methods can support edge selection on large-scale graphs efficiently, we propose an edge-message-passing network EEN to generate edge embedding. To the best of our knowledge, this is the first attempt that uses DRL model for solving the MWM problem. Experimental results show that L2M outperforms state-of-the-art algorithms and generalizes well on graphs of different sizes and distributions.
BinProv: Binary Code Provenance Identification without Disassembly

Provenance identification, which is essential for binary analysis, aims to uncover the specific compiler and configuration used for generating the executable. Traditionally, the existing solutions extract syntactic, structural, and semantic features from disassembled programs and employ machine learning techniques to identify the compilation provenance of binaries. However, their effectiveness heavily relies on disassembly tools (e.g., IDA Pro) and tedious feature engineering, since it is challenging to obtain accurate assembly code, particularly, from the stripped or obfuscated binaries. In addition, the features in machine learning approaches are manually selected based on the domain knowledge of one specific architecture, which cannot be applied to other architectures. In this paper, we develop an end-to-end provenance identification system BinProv, which leverages a BERT (Bidirectional Encoder Representations from Transformers) based embedding model to learn and represent the context semantics and syntax directly from the binary code. Therefore, BinProv avoids the disassembling step and manual feature selection in provenance identification. Moreover, BinProv can distinguish the compilers and the four optimization levels (O0/O1/O2/O3) by fine-tuning the classifier model with the embedding inputs for specific provenance identification tasks. Experimental results show that BinProv achieves 92.14%, 99.4%, and 99.8% accuracy at byte sequence, function, and binary levels, respectively. We further demonstrate that BinProv works well on obfuscated binary code, suggesting that BinProv is a viable approach to remarkably mitigate the disassembler dependence in future provenance identification tasks. Finally, our case studies show that BinProv can better identify compiler helper functions and improve the performance of binary code similarity detection.
Path planning and obstacle avoidance of multi-robotic system in static and dynamic environments

Mobile robots have wide applications in challenging real-world scenarios. Therefore, it is necessary to have an advanced controller to control the robotic systems smoothly. An artificial bee colony optimization algorithm and recurrent neural network are combined to develop a hybrid controller and implemented for multi-robotic navigational problems in unknown static and dynamic environments. The designed controller is validated through MATLAB simulations coupled with real-time experiments. Results obtained via both the testing platforms are analysed, and found a good agreement between them as the deviation is less than 5.5%. Further, the developed controller is compared with existing controllers, and improvements of 20%, 10.19%, 13.53% is noted in terms of path length.
Multiple source partial knowledge transfer for manufacturing system modelling

Transfer learning has shown its attractiveness for manufacturing system modelling by leveraging previously acquired knowledge to assist in training the target model, whereas most techniques focus on single-source transfer settings. Since there are usually multiple source domains available in practice, multi-source transfer learning is attracting more attention. Existing researches regard the source instance or the source model as the basic information granularity, which makes it difficult to reduce the global shift and the local discrepancy across domains simultaneously. Therefore, this paper presents a multiple source partial knowledge transfer method (MSPKT) for manufacturing system modelling tasks, in which partial knowledge is defined as a novel information granularity between the instance granularity and model granularity. Firstly, TSK (Takagi-Sugeno-Kang) fuzzy system is introduced as the basic learner to represent partial knowledge effectively. Then, we design a transferability measurement of partial knowledge by considering the similarity and reliability to support transfer learning with multiple source domains. Finally, a synthetic dataset and two manufacturing system datasets are used to verify the effectiveness of the proposed method.
Evaluating the resilience of variational quantum algorithms to leakage noise

As we are entering the era of constructing practical quantum computers, suppressing the inevitable noise to accomplish reliable computational tasks will be the primary goal. Leakage noise, as the amplitude population leaking outside the qubit subspace, is a particularly damaging source of error that error correction approaches cannot handle. However, the impact of this noise on the performance of variational quantum algorithms (VQAs), a type of near-term quantum algorithms that is naturally resistant to a variety of noises, is yet unknown. Here, {we consider a typical scenario with the widely used hardware-efficient ansatz and the emergence of leakage in two-qubit gates}, observing that leakage noise generally reduces the expressive power of VQAs. Furthermore, we benchmark the influence of leakage noise on VQAs in real-world learning tasks. Results show that, both for data fitting and data classification, leakage noise generally has a negative impact on the training process and final outcomes. Our findings give strong evidence that VQAs are vulnerable to leakage noise in most cases, implying that leakage noise must be effectively suppressed in order to achieve practical quantum computing applications, whether for near-term quantum algorithms and long-term error-correcting quantum computing.
Verifying OpenJDK’s LinkedList using KeY (extended paper)

Abstract As a particular case study of the formal verification of state-of-the-art, real software, we discuss the specification and verification of a corrected version of the implementation of a linked list as provided by the Java Collection Framework.