Reviewerly/exHarmony huggingface dataset:
BSTProv: Blockchain-Based Secure and Trustworthy Data Provenance Sharing

In the Big Data era, data provenance has become an important concern for enhancing the trustworthiness of key data that are rapidly generated and shared across organizations. Prevailing solutions employ authoritative centers to efficiently manage and share massive data. They are not suitable for secure and trustworthy decentralized data provenance sharing due to the inevitable dishonesty or failure of trusted centers. With the advent of the blockchain technology, embedding data provenance in immutable blocks is believed to be a promising solution. However, a provenance file, usually a directed acyclic graph, cannot be embedded in blocks as a whole because its size may exceed the limit of a block, and may include various sensitive information that can be legally accessed by different users. To this end, this paper proposed the BSTProv, a blockchain-based system for secure and trustworthy decentralized data provenance sharing. It enables secure and trustworthy provenance sharing by partitioning a large provenance graph into multiple small subgraphs and embedding the encrypted subgraphs instead of raw subgraphs or their hash values into immutable blocks of a consortium blockchain; it enables decentralized and flexible authorization by allowing each peer to define appropriate permissions for selectively sharing some sets of subgraphs to specific requesters; and it enables efficient cross-domain provenance composition and tracing by maintaining a high-level dependency structure among provenance graphs from different domains in smart contracts, and by locally storing, decrypting, and composing subgraphs obtained from the blockchain. Finally, a prototype is implemented on top of an Ethereum-based consortium blockchain and experiment results show the advantages of our approach.
Semi-supervised cross-modal hashing with multi-view graph representation

Recently, significant progress has been made in graph-based hashing methods for the purpose of learning hash codes that can preserve semantic similarity. Many approaches have been formulated for supervised learning problems that require labels. However, large-scale labeled datasets are expensive to obtain, especially when the data are multimodal, thus imposing a restriction on the usage of such algorithms. In this study, a novel multi-view graph cross-modal hashing (MGCH) framework is proposed to generate hash codes in a semi-supervised manner using the outputs of multi-view graphs processed by a graph-reasoning module. In contrast to conventional graph-based hashing methods, MGCH adopts multi-view graphs as the only learning assistance to connect labeled and unlabeled data in the process of pursuing binary embeddings. Multiview graphs that filter the features of multidirectional data in multiple anchor sets are beneficial in refining features. As the core component of our MGCH, an intuitive graph reasoning network consisting of two graph convolutional layers and one graph attention layer is employed to simultaneously convolve anchor graphs and asymmetric graphs with input data. Comprehensive cross-modal hashing evaluations on the Wiki, MIRFlickr-25K, NUS-WIDE, and MSCOCO datasets demonstrate the superiority of MGCH over the latest methods for limited labeled data.
The devil is in the detail: Generating system call whitelist for Linux seccomp

The system calls provide the main interface for user processes to request the kernel services, however, for any specific process, most of them will not be needed. If a user process is compromised, those unnecessary system calls can be abused to attack the kernel and the other processes. To migrate this problem, the seccomp mechanism has been merged into the Linux kernel to limit the available system calls according to a system call whitelist. However, it is still a challenge to automatically and effectively generate a minimal but complete system call whitelist for a specific user process. In this paper, we develop a toolkit named TAILOR that mainly relies on the static analysis to generate a mapping table for the standard library from the library functions to their corresponding system calls based on the source code analysis. Then for any application that invokes system calls via the standard library, we can just compare the called library functions in the application with the mapping table to obtain required system calls. TAILOR solves the problems during source-level standard library analysis, which consist of the difficulty in macro function identification, unchained calling tree caused by macro aliases, and the difficulty in identifying the function scope via static analysis. Our experiments on 50 popular general terminal commands show that our tool can reduce 88% system calls for them and block about 74% potential vulnerabilities from malicious system calls.
Multi-head collaborative learning for graph neural networks

Recently, graph neural networks (GNNs) have been widely used for graph-structured data representation. However, existing GNNs generally either employ one classifier head which usually makes the model fall into local minima or multiple classifier heads independently which lack of exploiting the correlation of them. To overcome this issue, in this paper, we propose to employ Multi-head Collaborative Learning for Graph Neural Networks (MCL-GNNs). The MCL-GNNs provide a collaborative learning process via multiple classifiers to complement each other’s information. Experimental results on several benchmark data sets show that our method has better generality and stability than other comparison methods.
Information hiding in the sharing domain

Secret image sharing (SIS) can divide a secret image into several shadow images for protection. Information hiding in the sharing domain (IHSD) fuses SIS and information hiding (IH) to simultaneously share any secret image and hide any information, and this technique can be applied in cloud computing, law enforcement and medical diagnoses. IHSD not only marks shadow images with information to prevent malicious tampering and for convenient management, search and identification but also enhances the robustness of IH. In this paper, we first introduce a formal definition of IHSD. Then, we describe a general IHSD model and algorithms with a concrete example in detail. In IHSD, we design the random element utilization model to control the random pixels generated from SIS. Then, we obtain shadow images with hidden information to realize SIS and IH simultaneously. The inputs of SIS with secret images, steganography and extra information in algorithms are without any limitations. Theoretical analyses, experiments and comparisons are presented to prove the effectiveness and feasibility of IHSD. • IHSD is short for information hiding in the sharing domain. • IHSD fuses secret image sharing and information hiding for simultaneous realization. • IHSD is realized by the random element utilization model to control randomness. • IHSD is a general idea applicable to any secret image and any information.
A blockchain-driven data exchange model in multi-domain IoT with controllability and parallelity

With the extensive application of the Internet of Things (IoT), multi-domain IoT appears as the additional coordination and control ability of IoT. However, the emerging multi-domain IoT suffers from some security challenges, such as insider threats and cross-domain data exchange issues. In this paper, we propose a data exchange model for the multi-domain IoT environment driven by blockchain, addressing expensive cross-domain access control and low data exchange throughput issues. At the core of this model is a chaincode-based cross-domain access control scheme and a domain-as-a-shard (DaaS) high parallel throughput optimization technology. In particular, the access control scheme is maintained by multiple blockchain nodes to ensure access control strategy distributed storage and data cross-domain controllability. The data in this model cannot be exchanged across domains until all nodes jointly verify that the access control strategy is valid. Moreover, the throughput optimization technology sets the IoT domain as the blockchain shard to process data exchange in parallel and scale up the throughput dramatically. In each shard, data exchange is processed in the form of the blockchain transaction independently to improve the transaction throughput of our model. Finally, security analysis proves that our model successfully ensures access control strategy non-repudiation and cross-domain data controllability. Extensive experiments on Hyperledger Fabric show that the transaction throughput of our model is nearly three times that of the original Fabric v1.4.
Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person's abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.
Discrete Box-Constrained Minimax Classifier for Uncertain and Imbalanced Class Proportions

This paper aims to build a supervised classifier for dealing with imbalanced datasets, uncertain class proportions, dependencies between features, the presence of both numeric and categorical features, and arbitrary loss functions. The Bayes classifier suffers when prior probability shifts occur between the training and testing sets. A solution is to look for an equalizer decision rule whose class-conditional risks are equal. Such a classifier corresponds to a minimax classifier when it maximizes the Bayes risk. We develop a novel box-constrained minimax classifier which takes into account some constraints on the priors to control the risk maximization. We analyze the empirical Bayes risk with respect to the box-constrained priors for discrete inputs. We show that this risk is a concave non-differentiable multivariate piecewise affine function. A projected subgradient algorithm is derived to maximize this empirical Bayes risk over the box-constrained simplex. Its convergence is established and its speed is bounded. The optimization algorithm is scalable when the number of classes is large. The robustness of our classifier is studied on diverse databases. Our classifier, jointly applied with a clustering algorithm to process mixed attributes, tends to equalize the class-conditional risks while being not too pessimistic.
Generalized Deepfake Video Detection Through Time-Distribution and Metric Learning

Rapid advancements in the field of computer vision and AI have enabled the creation of synthesized images and videos known as deepfakes. Deepfakes are used as a source of spreading false news and misinformation. The constant evolution of generative models, used for creating deepfakes, makes it difficult and yet very important to find effective generalized solutions for such deepfake videos. We have designed a generalized deepfake detector by creating a two-stream network that uses CNN-LSTM as its backbone. Our contributions in this article are twofold: 1) using a time-distributed network to create representations using spatial and temporal information of a video, and 2) improving the discriminative ability of the extracted feature embeddings by using metric learning during training. Results gathered through extensive experiments show the effectiveness of our solution even on a cross-modal FaceForensic++ dataset proving the generalization ability of the solution.
An ensemble multilingual model for toxic comment classification

The online toxic comments cause enormous harm to the society, where toxicity is defined as anything rude, disrespectful or otherwise likely to make someone leave a discussion. To have a safer, more collaborative internet, grateful contributions are made by a main area of focus on machine learning models to identify toxicity in English, whereas part of misinformation disseminates in other languages. Over the past year, pretraining multilingual language models give rise to impressive gains for cross lingual toxicity classification. This paper presents an approach to build toxicity models applying the Jigsaw Multilingual Toxic Comment Classification dataset provided by Kaggle. We set our ensemble model in three parts based on Besides, we implement subsample, Pseudo-labeling with open-subtitles, translating non-English languages to English language, and Post Processing to improve the classification accuracy indispensably. Our final model achieved an AUC of 0.9469 for the training set and 0.9485 for the validation set, demonstrating the effectiveness of performance under cross-lingual toxicity detectors.
The runsort permuton

Suppose we choose a permutation π uniformly at random from Sn. Let runsort(π) be the permutation obtained by sorting the ascending runs of π into lexicographic order. Alexandersson and Nabawanda recently asked if the plot of runsort(π), when scaled to the unit square [0,1]2, converges to a limit shape as n→∞. We answer their question by showing that the measures corresponding to the scaled plots of these permutations runsort(π) converge with probability 1 to a permuton (limiting probability distribution) that we describe explicitly. In particular, the support of this permuton is {(x,y)∈[0,1]2:x≤ye1−y}.
Practical approximate quantum multipliers for NISQ devices

The landscape of quantum computing has changed with the introduction of quantum computers with dozens of qubits. While these computers enable deployment of quantum algorithms into real quantum machines, they suffer from decoherent errors which cause incorrect computation results. One of the factors that impacts noise level in quantum computers is the complexity of circuits. In particular, depth and T-count of quantum circuits are keys to the fidelity of quantum programs.
An Elliptic Curve Signcryption Scheme and Its Application

Two basic security requirements in communication are confidentiality and authentication. Signcryption is an ideal technique to transmit encrypted and authenticated data. In view of the shortcomings of existing signcryption schemes and the high security of elliptic curve cryptography (ECC), we design a ECC-based signcryption scheme and evaluate it in terms of security, computational overhead, and communication overhead. Finally, we consider the application of our secure and efficient signcryption scheme in the smart lock key management system and analyze the bit-oriented performance of the designed key management scheme.
Deformation estimation of a circular tunnel from a point cloud using elliptic Fourier analysis

In recent years, light detection and ranging technology has been applied to tunnel engineering. Although more accurate than traditional measurement techniques, estimating the tunnel deformation from the light detection and ranging point cloud remains challenging. This paper proposes a deformation estimation method using elliptic Fourier analysis, whereby the tunnel deformation is estimated from the difference in the Fourier series representations of the tunnel outline before and after deformation. The applicability of the proposed method for a circular tunnel cross-section is theoretically considered. Numerical results show that there is almost no error in the estimation of radial displacement, regardless of the measurement conditions. In contrast, there is an error in the estimation of circumferential displacement because this does not modify the circular tunnel outline. Therefore, tunnel deformation cannot be accurately estimated using point cloud data obtained only from distance measurements. As a special case, when the full-slip condition is imposed on the ground–lining interface and the estimated circumferential displacement of the inner surface is regarded as that of the middle surface, circumferential displacement can also be accurately estimated. Moreover, the difference in the deformation patterns under the full-slip and no-slip conditions decreases as the relative stiffness of the lining with respect to the ground increases. As a result, the deformation estimation accuracy is expected to be high for tunnels with a high possibility of deformation. When the distance measurement error is considered, the estimation accuracy of the displacement decreases, especially that of the circumferential displacement. Smoothed data significantly improve the accuracy, and high-precision estimation can be achieved when the measurement error is small compared with the tunnel deformation. However, small displacement errors produce significant stress errors in the higher-order terms of the Fourier series. Therefore, only the lower-order terms can be used for stress estimations.
An Exploratory Study of Mobile-Based Scenarios for Foreign Language Teaching in Early Childhood

In today’s world, the ability to communicate in a foreign language is more highly prized than ever by prospective employers, which results in more options and possibilities for students, both academically and professionally. As a result of this tendency and the need for new communication methods, language instructors are driven to include cutting-edge language teaching approaches, resources, and materials in their classroom instruction, such as using ICT, or information, communication and ubiquitous technologies. In this paper, we introduce learning scenarios based on two mobile learning apps that facilitate language learning through interesting, interactive settings in a more personalized way based on children’s age. The writers’ emphasis will be on demonstrating interactive activities devised in their classrooms and on providing examples of student work in two languages, English and Spanish. Through this paper, we examine a range of educational tools and determine that Mondly Kids and Language Drops-Kahoot are the best acceptable teaching materials. On the basis of this assumption, we created three distinct groups of students, and the outcomes from the assessment technique show that mobile language learning enhances children’s experiences and increases their willingness to learn a new language. Additionally, students can use mobile applications to improve their speaking abilities and critical thinking skills throughout a language learning session.
On the limits of Conditional Generative Adversarial Neural Networks to reconstruct the identification of inhabitants from IoT low-resolution thermal sensors

One of the main objectives of smart homes is to facilitate daily life by increasing user comfort, with the potential to play a key role in revolutionizing healthcare for the elderly, the disabled and people with functional limitations. To achieve this end, smart homes will have to be able to distinguish the identity of users, their location and the activities they are performing, while also being implemented in a non-invasive way that protects the privacy of these users. Computer vision is one of the main technologies included in smart homes. However, there are drawbacks to traditional cameras, given their dependence on light and privacy-related concerns. Thermal cameras provide a solution, as they operate regardless of light conditions (e.g. at night) while respecting users’ privacy. In this work, image reconstruction and identification of inhabitants from facial images collected by low-resolution thermal sensors has been carried out by using Conditional Generative Adversarial Neural Networks (CGANs). The system has been implemented through an IoT device with raspberry Pi and dual-vision thermal and visible-spectrum sensors installed in a real smart home to automatically collect paired visible-spectrum and thermal images. Thus, different configurations of CGANs have been implemented and analyzed to achieve the following outcomes: (1) inhabitant identification (normal and masked face) with enhanced user privacy and (2) transfer from thermal to color images in the visible spectrum. Results show that the proposed CGAN achieves a recognition rate of 95% and 94% for uncovered and masked faces. This enables user identification without registering accurate facial expressions in the color image reconstruction, protecting user privacy. Furthermore, the developed system outperformed similar approaches using low-resolution datasets and has demonstrated that the accuracy of image reconstruction depends on the resolution of the input visible-spectrum images. In addition, a contribution to high-performance computing has been made by designing a CGAN that runs efficiently on multiple GPUs, achieving increased performance and response speed of the network, as well as applicability to larger problems.
Static data-flow analysis for software product lines in C

Abstract Many critical codebases are written in C, and most of them use preprocessor directives to encode variability, effectively encoding software product lines. These preprocessor directives, however, challenge any static code analysis. SPLlift, a previously presented approach for analyzing software product lines, is limited to Java programs that use a rather simple feature encoding and to analysis problems with a finite and ideally small domain. Other approaches that allow the analysis of real-world C software product lines use special-purpose analyses, preventing the reuse of existing analysis infrastructures and ignoring the progress made by the static analysis community. This work presents VarAlyzer , a novel static analysis approach for software product lines. VarAlyzer first transforms preprocessor constructs to plain C while preserving their variability and semantics. It then solves any given distributive analysis problem on transformed product lines in a variability-aware manner. VarAlyzer ’s analysis results are annotated with feature constraints that encode in which configurations each result holds. Our experiments with 95 compilation units of OpenSSL show that applying VarAlyzer enables one to conduct inter-procedural, flow-, field- and context-sensitive data-flow analyses on entire product lines for the first time, outperforming the product-based approach for highly-configurable systems.