Reviewerly/exHarmony huggingface dataset:
A Graph Pre-image Method Based on Graph Edit Distances

The pre-image problem for graphs is increasingly attracting attention owing to many promising applications. However, it is a challenging problem due to the complexity of graph structure. In this paper, we propose a novel method to construct graph pre-images as median graphs, by aligning graph edit distances (GEDs) in the graph space with distances in the graph kernel space. The two metrics are aligned by optimizing the edit costs of GEDs according to the distances between the graphs within the space associated with a particular graph kernel. Then, the graph pre-image can be estimated using a median graph method founded on the GED. In particular, a recently introduced method to compute generalized median graphs with iterative alternate minimizations is revisited for this purpose. Conducted experiments show very promising results while opening the computation of graph pre-image to any graph kernel and to graphs with non-symbolic attributes.
Robust and Secure Watermarking Technique for E-Health Applications

Advancement in information and communication technology in health sector has been developed at a rapid pace, and has consequently improved its different services. Securing patients’s health documents becomes crucial and researchers have investigated more than one privacy measures involving the access control, the security and the integrity of these documents. In this paper, we propose a novel QR-code-based watermarking technique for medical images. The generated QR code pattern is the result of converting a watermarked image which is obtained by embedding the concatenation of diagnostic results and a specific collusion-secure code to the original image.The proposed scheme is a two-stage scheme which has essentially a two fold purpose: at a first step, we propose to embed successively two types of watermarks to the image and then to convert it to a set of QR codes to ensure simultaneously and independently its protection. At a second step, we apply the tracing process whose target is the identification of eventual hackers by extracting the hidden identifier from the suspicious document and analyse it. Experimental assessments show that the proposed scheme provides good results in terms of security, robustness and tracing accuracy constraints.
Cross-domain Knowledge Distillation for Retrieval-based Question Answering Systems

Question Answering (QA) systems have been extensively studied in both academia and the research community due to their wide real-world applications. When building such industrial-scale QA applications, we are facing two prominent challenges, i.e., i) lacking a sufficient amount of training data to learn an accurate model and ii) requiring high inference speed for online model serving. There are generally two ways to mitigate the above-mentioned problems. One is to adopt transfer learning to leverage information from other domains; the other is to distill the “dark knowledge” from a large teacher model to small student models. The former usually employs parameter sharing mechanisms for knowledge transfer, but does not utilize the “dark knowledge” of pre-trained large models. The latter usually does not consider the cross-domain information from other domains. We argue that these two types of methods can be complementary to each other. Hence in this work, we provide a new perspective on the potential of the teacher-student paradigm facilitating cross-domain transfer learning, where the teacher and student tasks belong to heterogeneous domains, with the goal to improve the student model’s performance in the target domain. Our framework considers the “dark knowledge” learned from large teacher models and also leverages the adaptive hints to alleviate the domain differences between teacher and student models. Extensive experiments have been conducted on two text matching tasks for retrieval-based QA systems. Results show the proposed method has better performance than the competing methods including the existing state-of-the-art transfer learning methods. We have also deployed our method in an online production system and observed significant improvements compared to the existing approaches in terms of both accuracy and cross-domain robustness.
Practical Approach of Security Enhancement Method based on the Protection Motivation Theory

In order to strengthen information security, practical solutions to reduce information security stress are needed because the motivation of the members of the organization who use it is needed to work properly. Therefore, this study attempts to suggest the key factors that can enhance security while reducing the information security stress of organization members. To this end, based on the theory of protection motivation, trust and security stress in information security policies are set as mediating factors to explain changes in security reinforcement behavior, and risk, efficacy, and reaction costs of cyberattacks are considered as prerequisites. Our study suggests a solution to the security reinforcement problem by analyzing the factors that influence the behavior of organization members that can raise the protection motivation of the organization members.
A Novel BBO Algorithm Based on Local Search and Nonuniform Variation for Iris Classification

In order to improve the iris classification rate, a novel biogeography-based optimization algorithm (NBBO) based on local search and nonuniform variation was proposed in this paper. Firstly, the linear migration model was replaced by a hyperbolic cotangent model which was closer to the natural law. And, the local search strategy was added to traditional BBO algorithm migration operation to enhance the global search ability of the algorithm. Then, the nonuniform variation was introduced to enhance the algorithm in the later iteration. The algorithm could achieve a stronger iris classifier by lifting weaker similarity classifiers during the training stage. On this base, the convergence condition of NBBO was proposed by using the Markov chain strategy. Finally, simulation results were given to demonstrate the effectiveness and efficiency of the proposed iris classification method.
LOOPS: A Holistic Control Approach for Resource Management in Cloud Computing

In cloud computing model, resource sharing introduces major benefits for improving resource utilization and total cost of ownership, but it can create technical challenges on the running performance. In practice, orchestrators are required to allocate sufficient physical resources to each Virtual Machine (VM) to meet a set of predefined performance goals. To ensure a specific service level objective, the orchestrator needs to be equipped with a dynamic tool for assigning computing resources to each VM, based on the run-time state of the target environment. To this end, we present LOOPS, a multi-loop control approach, to allocate resources to VMs based on the service level agreement (SLA) requirements and the run-time conditions. LOOPS is mainly composed of one essential unit to monitor VMs, and three control levels to allocate resources to VMs based on requests from the essential node. A tailor-made controller is proposed with each level to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one host to another. The three levels work together to meet the required SLA. The experimental results have shown that the proposed approach can meet applications' performance goals by assigning the resources required by cloud-based applications.
Metrics of Syntactic Equivalence to Assess Translation Difficulty

We propose three linguistically motivated metrics to quantify syntactic equivalence between a source sentence and its translation. Firstly, syntactically aware cross (SACr) measures the degree of word group reordering by creating syntactically motivated groups of words that are aligned. Secondly, an intuitive approach is to compare the linguistic labels of the word-aligned source and target tokens. Finally, on a deeper linguistic level, aligned syntactic tree edit distance (ASTrED) compares the dependency structure of both sentences. To be able to compare source and target dependency labels, we make use of Universal Dependencies (UD). We provide an analysis of our metrics by comparing them with translation process data in mixed models. Even though our examples and analysis focus on English as the source language and Dutch as the target language, the proposed metrics can be applied to any language for which UD models are attainable. An open-source implementation is made available.
Residual Deep Monocular 3D Human Pose Estimation using CVAE synthetic data

Abstract Estimating 3D human pose from a monocular RGB image is a challenging task in computer vision because annotating a large number of 3D pose ground-truth data is costly. To address the problem of lack of 3D data, many methods have been proposed. In this paper, we propose to address this issue by synthesizing data. Firstly we exploit Conditional Variational Autodecoder to generate 3D human skeletons data. In CVAE network, we acquire generous 3D pose samples through the predicted 2D pose and the existing 3D ground truth. Secondly, base the generated 3D samples, we obtain the corresponding 2D pose by projection, thus augmenting the data of 2D-3D network. Finally, we train these synthetic data on 3D pose residual estimation network. Extensive experiments show that our approach achieves state-of-the-art accuracy on standard benchmark datasets.
An Efficient Post‐quantum Identity‐Based Signature

Digital signature is one of the most important cryptography primitives. Recently, more and more works have been done to construct signatures over lattice problems to keep them secure in the quantum age. Among them, a ring-based signature scheme named Dilithium is the most efficient one and a candidate in the third round of the National Institute of Standards and Technology's post-quantum cryptography project. To make those schemes work well in large network, we constructed the first ring-based Identity-based signature (IBS) scheme for light-weight authentication. The construction in this paper relies on the transformations introduced by Bellare et al. in Journal of Cryptology (Vol.22, No.1, pp.1–61, 2009) and its security can be proved under the hardness of ring-learning with errors problem in the random oracle model. Due to better trapdoor and polynomial ring setting, our proposed scheme are much better than the previous ones in terms of both computation and communication complexities.
A Novel Access Control for Internet of Things Based on Blockchain Smart Contract

Since more and more devices join the Internet of Things(IoT) network, a large amount of user sensitive data is generated. The leakage of these data will cause very serious consequences. Traditional access control is prone to single point of failure. The existing researches on the combination of blockchain and access control have some disadvantages, such as the difficulty of managing access rights, inefficient access efficiency, and the difficulty of supporting lightweight IOT devices. This paper proposes an IoT access control model based on blockchain smart contract, called SCBAC. Firstly, by adopting the idea of IoT Attributes Based Access Control(ABAC), the model supports a dynamic, fine-grained access control. Secondly, by deploying the access control strategy on the blockchain in the form of smart contracts, the computing pressure of IoT devices is reduced, so that the model can be applied to lightweight IoT devices and has tamper resistance and traceability characteristics. The idea of tokens is adopted into the strategy, and subjects obtain access rights by applying for tokens in advance, which improves access efficiency. In addition, Trust recommendation algorithm is adopted in the model, which effectively solves the problem of identity fraud in access control. Finally, we build a prototype system to verify this proposed model, through case analysis and security analysis, it shows that this access control model is effective and versatile. Furthermore, the model can be used as a reference for other IoT applications with access control security requirements.
Weighted Local Outlier Factor for Detecting Anomaly on In-Vehicle Network

Modern vehicles are generally equipped with dozens of (or even hundreds of) electronic and intelligent devices and bloom into more involved information hub in enabling V2X networking. Protecting this increasingly complex vehicle ecosystem can be an arduous task, especially as the proliferation of data across distinct connected devices makes them more vulnerable than ever before. Intrusion detection systems (IDSs) have been found extremely rewarding in monitoring in-vehicle network traffic and detecting potential intrusions. The paper presents WLOF-InV, a novel unsupervised method based on local density for IDS on in-vehicle network. Given historical in-vehicle data of message identifiers, WLOF-InV first segments the traffic into a slice of (e.g., m) sliding windows. For each sliding window, WLOF-InV exerts information gain to select features for dimensionality reduction and squeezes out n features which are then bundled together to form a row vector and eventually gets an m*n matrix. WLOF-InV then adaptively determines the hyper parameters for local outlier factor (LOF) model (optimizing the scores for ranking the training data and the cutoff position for anomalies). In online detection, WLOF-InV determines the features by the information gain and invokes abnormal score weighting mode (which weights the LOF value of each dimension data by entropy method) to obtain the complete LOF score (of the overall traffic), and thereby grabs the anomaly traffic by resorting to the adjusted model. WLOF-InV is validated on the real data of three attack types (DoS, fuzzy, and impersonation). Experimental results demonstrate that WLOF-InV contrives next to optimal performance.
Supervised ML-based approach for auto-tagging of scientific literature

Auto-tagging of scientific papers with appropriate keywords/taxonomies is a text classification problem, one of the fundamental tasks of Natural Language processing. The work behind this paper adopts supervised learning approach to a text classification and uses existing dataset, scrapped articles from the arxiv database, annotated by using the original classification interoperable with ACM Computing Classification System. It tests three conventional Machine Learning algorithms and several approaches to feature engineering (namely, text vectorization and word embeddings) to build the best performing classifier for multi-class, multi-label text classification problem. For the given dataset, the best results were achieved by using Support Vector Machine (SVM) algorithm and Term Frequency-Inverse Document Frequency text vectorization approach.
Source Code Transformations for Improving Security of Time-bounded K-variant Systems

Source code transformation techniques can improve the security of systems against memory exploitation attacks. As such, the chance of exploitation of security vulnerabilities can be decreased by using different controlled source code transformation techniques. In K-variant architecture, multiple variants of a program are generated through a controlled source code transformation to improve the security of systems. To investigate the effectiveness and practicality of source code program transformations in improving the security of time-bounded K-variant systems for memory exploitation attacks. The effectiveness of program transformations in improving the security of time-bounded K-variant systems is experimentally investigated for different memory attacks. The results suggest that generating multiple variants using the presented transformations significantly improves the survivability of time-bounded K-variant systems under memory exploitation attacks. We conclude that generating multi-variants in time-bounded K-variant systems in accordance with the presented program transformations may improve the security of time-bounded K-variant systems significantly for memory exploitation attacks with a reasonable cost and overhead.
Image Enhancement Method Based on Bilinear Interpolating and Wavelet Transform

In order to enhance the resolution of the image and improve the visual effect of the image, a method of image quality enhancement based on bilinear interpolation and wavelet transformation is proposed. The algorithm first enhances the image by histogram equalization and bilinear interpolation to preserve better detail quality, and then perform wavelet transform on the histogram equalized image and the original imageeach of the input images are devided into 4 subbands, estimates the singular value matrix of the low frequency subband image, and then enhances the image through Wavelet reconstruction, Finally, perform histogram matching on the result image and bilinear interpolation image to obtain an enhanced image. The experimental results show that the performance of the proposed method is better than that of the traditional method.
The Impact of Virtual Reality and Viewpoints in Body Motion Based Drone Teleoperation

The operation of telerobotic systems can be a challenging task, requiring intuitive and efficient interfaces to enable inexperienced users to attain a high level of proficiency. Body-Machine Interfaces (BoMI) represent a promising alternative to standard control devices, such as joysticks, because they leverage intuitive body motion and gestures. It has been shown that the use of Virtual Reality (VR) and first-person view perspectives can increase the user's sense of presence in avatars. However, it is unclear if these beneficial effects occur also in the teleoperation of non-anthropomorphic robots that display motion patterns different from those of humans. Here we describe experimental results on teleoperation of a non-anthropomorphic drone showing that VR correlates with a higher sense of spatial presence, whereas viewpoints moving coherently with the robot are associated with a higher sense of embodiment. Furthermore, the experimental results show that spontaneous body motion patterns are affected by VR and viewpoint conditions in terms of variability, amplitude, and robot correlates, suggesting that the design of BoMIs for drone teleoperation must take into account the use of Virtual Reality and the choice of the viewpoint.
Techniques for Private Transactions in Corporate Blockchain Networks

Blockchain platforms are generally applied to automate operations using a trusted environment with broadcast messaging and distributed data storing. At the same time corporates still have business requirements to also send confidential peer-to-peer messages or private transactions. Mainly these requirements derive from the law obligations (bank secrecy, GDPR, etc.) and the necessity to protect competitive market advantages. Blockchain platforms for public networks ignore requirements for private transactions due to user anonymity and the absence of personal data in blockchain networks. Enterprise blockchain platforms for corporate networks mostly address confidential issues by transferring private data through trusted third parties (TTP) or intermediaries, and off-chain peer-to-peer messaging. However, these approaches miss core blockchain advantages of a trusted environment - immutability and integrity of private data. This article introduces a new technique and its implementation of private transactions in a blockchain that is based on both off-chain peer-to-peer messaging with confidential data and broadcast transactions that contain hashes of private data to verify immutability and integrity. The article provides technical details of the method, also performance issues are discussed.
Falling-Point Recognition and Scoring Algorithm in Table Tennis Using Dual-Channel Target Motion Detection

In table tennis, the ball has numerous characteristics of high speed, small size, and changeable trajectory. Due to these characteristics, the human eye often cannot accurately judge the ball's movement and position, leading to the problem of precise detection of the ball's falling point and movement tracking. In sports, the use of machine learning for locating and detecting the ball and the use of deep learning for reconstructing and displaying the ball's trajectories are considered futuristic technologies. Therefore, this paper proposes a novel algorithm for identifying and scoring points in table tennis based on dual-channel target motion detection. The proposed algorithm consists of multiple input channels to jointly learn different features of table tennis images. The original image is used as the input of the first channel, and then the Sobel operator is used to extract the first-order derivative feature of the original image, which is used as the input of the second channel. The table tennis feature information from the two channels is then fused and sent to the 3D neural network module. The fully connected layer is used to identify the table tennis ball's drop point, compare it with a standard drop point, calculate the error distance, and give a score. We also constructed a data set and conducted experiments. The experimental results show that the method in this paper is effective in sports.
X-2ch: Quad-Channel Collaborative Graph Network over Knowledge-Embedded Edges

Carrying abundant side information, knowledge graph (KG) has shown its great potential in enriching the sparsity of collaborative filtering (CF) for recommendation. Although graph neural networks (GNNs) have been successfully employed to learn user preferences from KG and CF signals simultaneously, most models suffer from inferior performance due to their deficient designs, i.e., 1) formulating no distinction between users, items and KG entities, 2) confounding KG signals with CF signals and 3) completely neglecting the effects of edges, which is vital for graph information propagation. In this paper, we propose a quad-channel graph model (X-2ch) to tackle these problems. First, rather than lodging KG entities on graph as nodes, X-2ch distills KG information and embeds them as edge attributes in a bi-directional manner to model the natural user-item interaction process. Second, X-2ch introduces a novel quad-channel learning scheme, including a collaborative user-item update and a CF-KG attentive propagation, to holistically capture the interconnectivity of users and items while preserving their distinct properties. Experiments on two real-world benchmarks show substantial improvement over the state-of-the-art baselines.
Probabilistic Feature Selection for Interpretable Random Forest Model

It is important to understand the reason behind the machine learning model predictions. In this paper we introduce a new model using random forest technique called Interpretable Random Forests. In this, we make some changes in feature selection technique for some business problems. The probabilistic method chosen to select each feature at every node along with the normal feature selection criteria will give us an Interpretable random forest model which will be compared to the general random forest model that gives an idea of how feature selection impacts the output from a model. Our experimental results shows IRF model has more or same accuracy than RF model and also interpretable.