Reviewerly/exHarmony huggingface dataset:
An Approach to Improving Homogeneous Cross-Project Defect Prediction by Jensen-Shannon Divergence and Relative Density

Homogeneous cross-project defect prediction (HCPDP) aims to apply a binary classification model built on source projects to a target project with the same metrics. However, there is still room for improvement in the performance of the existing HCPDP models. This study has proposed a novel approach, including one-to-one and many-to-one predictions. First, we apply the Jensen-Shannon divergence to select the most similar source project automatically. Second, relative density estimation is introduced to choose the suitable instance of the selected source project. Third, one-to-one and many-to-one prediction models are trained by the selected instances. Finally, two benchmark datasets are used to evaluate the proposed approach. Compared to the state-of-the-art methods, the experimental results demonstrated that the proposed approach could improve the prediction performance in the F1-score, AUC, and G-mean metrics and exhibit strong adaptability to the traditional classifiers.
HeteroTiC: A robust network flow watermarking based on heterogeneous time channels

Tracking back stealthy attackers is still a great challenge in face of complicated Internet environments and increased attacker capabilities. As a possible solution, network flow watermarking has been developed to embed attack labels actively in data flows, trying to identify the attack source accurately in time. However, due to the influences of uncertain network noises, the robustness of watermarking cannot be ensured efficiently in case of non-cooperative network scenarios currently. Meanwhile, it is difficult to enhance robustness and covertness simultaneously when suffering from severe network noises. Thus, a robust network flow watermarking based on Heterogeneous Time Channels (HeteroTiC) is proposed, for which packet order, packet timing and packet size constitute heterogeneous time channels to carry watermarks. The beginning positions are located by packet order to support accurate synchronization, the long watermark sequences are carried by inter-packet delays to improve capabilities against packet dropping and time jitter, the mapping relationships for the watermark sequences on heterogeneous time channels are mixed by packet size of the flow itself to enhance covertness. With multiple layers design of watermarking, HeteroTiC is designed to accomplish fast detection and accurate extraction on watermarks. By experiments, the average detection accuracy reaches 100% and the average extraction accuracy is validated with 94.2%. Furthermore, the same mapping pattern on multiple flows only accounts for 8.11%, which validates the covertness against multiple flow similarities. The extraction accuracy exceeds 9.76%∼18.1% in comparison with classical watermarking methods, which validates the robustness under network noises in Internet.
PCP-Ed: Parallel coordinate plots for ensemble data

The Parallel Coordinate Plot (PCP) is a complex visual design commonly used for the analysis of high-dimensional data. Increasing data size and complexity may make it challenging to decipher and uncover trends and outliers in a confined space. A dense PCP image resulting from overlapping edges may cause patterns to be covered. We develop techniques aimed at exploring the relationship between data dimensions to uncover trends in dense PCPs. We introduce correlation glyphs in the PCP view to reveal the strength of the correlation between adjacent axis pairs as well as an interactive glyph lens to uncover links between data dimensions by investigating dense areas of edge intersections. We also present a subtraction operator to identify differences between two similar multivariate data sets and relationship-guided dimensionality reduction by collapsing axis pairs. We finally present a case study of our techniques applied to ensemble data and provide feedback from a domain expert in epidemiology.
Pseudorandom vector generation using elliptic curves and applications to Wiener processes

In this paper we present, using the arithmetic of elliptic curves over finite fields, an algorithm for the efficient generation of a sequence of uniform pseudorandom vectors in high dimensions, that simulates a sample of a sequence of i.i.d. random variables, with values in the hypercube [0,1]d with uniform distribution. As an application, we obtain, in the discrete time simulation, an efficient algorithm to simulate, uniformly distributed sample path sequence of a sequence of independent standard Wiener processes. This could be employed for use, in the full history recursive multi-level Picard approximation method, for numerically solving the class of semilinear parabolic partial differential equations of the Kolmogorov type.
Guided Self-Training based Semi-Supervised Learning for Fraud Detection

Semi supervised learning has attracted attention of AI researchers in the recent past, especially after the advent of deep learning methods and their success in several real world applications. Most deep learning models require large amounts of labelled data, which is expensive to obtain. Fraud detection is a very important problem for several industries and large amount of data is often available. However, obtaining labelled data is cumbersome and hence semi-supervised learning is perfectly positioned to aid us in building robust and accurate supervised models. In this work, we consider different kinds of fraud detection paradigms and show that a self-training based semi-supervised learning approach can produce significant improvements over a model that has been training on a limited set of labelled data. We propose a novel self-training approach by using a guided sharpening technique using a pair of autoencoders which provide useful cues for incorporating unlabelled data in the training process. We conduct thorough experiments on three different real world databases and analysis to showcase the effectiveness of the approach. On the elliptic bitcoin fraud dataset, we show that utilizing unlabelled data improves the F1 score of the model trained on limited labelled data by around 10%.
Spherical Image Generation From a Few Normal-Field-of-View Images by Considering Scene Symmetry

Spherical images taken in all directions (360 degrees by 180 degrees) can represent an entire space including the subject, providing free direction viewing and an immersive experience to viewers. It is convenient and expands the usage scenarios to generate a spherical image from a few normal-field-of-view (NFOV) images, which are partial observations. The primary challenge is generating a plausible image and controlling the high degree of freedom involved in generating a wide area that includes all directions. We focus on scene symmetry, which is a basic property of the global structure of spherical images, such as the rotational and plane symmetries. We propose a method for generating a spherical image from a few NFOV images and controlling the generated regions using scene symmetry. We incorporate the intensity of the symmetry as a latent variable into conditional variational autoencoders to estimate the possible range of symmetry and decode a spherical image whose features are represented through a combination of symmetric transformations of the NFOV image features. Our experiments show that the proposed method can generate various plausible spherical images controlled from asymmetrically to symmetrically, and can reduce the reconstruction errors of the generated images based on the estimated symmetry.
Adversarial attack algorithm for object detection based on improved differential evolution

With the wide application of deep learning object detection model, its internal security vulnerability is also highlighted. In this paper, a black box adversarial attack algorithm SAD-DE based on improved differential evolution is proposed to reveal the possible security risks of the object detection model. Taking full advantage of the high optimization efficiency and simple parameter setting of differential evolution algorithm, multi mutation strategy is adopted, and the mutation rate and crossover rate are adaptively improved to effectively improve the optimization efficiency. In this paper, two public data sets are randomly exampled as test sets to counter attacks on YOLOv3 and Fast R-CNN respectively. Experimental results show that this method achieves a high fooling rate while maintaining a low anti disturbance, and achieves a maximum improvement of 33% compared with other black box attack algorithms.
Unsupervised Disentanglement for PostIdentification of GNSS Interference in the Wild

Interference signals (i.e., jammers) affect the processing chain of global navigation satellite system (GNSS) receivers, affecting or disrupting their accuracy. State-of-the-art supervised, learning-based methods detect and classify jammers at the highest accuracies. However, they require an elaborate labeling process that is scenario-specific and covers variations within each class. It is critical for practical applications to enable model training that covers environment and application-specific requirements. We propose a quasi-unsupervised framework that does not rely on prior knowledge and actively adapts to different environment-specific factors such as multipath, dynamics between participants, dynamics in the environment, and variations in signal strength or distance. Our experiments show that our pipeline is robust to six families and 21 sub-classes of different (un)-known jammers, multipath propagation, different jammer-receiver distances, and dynamics. Our results indicate that in 95% of all cases, the classification of known jammers is accurate at low uncertainty (SD±10%). In addition, even for unknown types of jammers, unsupervised almost error-free detection is possible (accuracy?90%, SD±10%). In addition, our disentanglement method allows our monitoring system to reliably interpret and identify outliers, i.e., unknown classes (V-measure?0.9; F2?95%, SD±5%). Experts may actively provide reference labels to consistently improve the performance of our surveillance system.
Logic Diagrams as Argument Maps in Eristic Dialectics

Abstract This paper analyses a hitherto unknown technique of using logic diagrams to create argument maps in eristic dialectics. The method was invented in the 1810s and -20s by Arthur Schopenhauer, who is considered the originator of modern eristic. This technique of Schopenhauer could be interesting for several branches of research in the field of argumentation: Firstly, for the field of argument mapping, since here a hitherto unknown diagrammatic technique is shown in order to visualise possible situations of arguments in a dialogical controversy. Secondly, the art of controversy or eristic, since the diagrams do not analyse the truth of judgements and the validity of inferences, but the persuasiveness of arguments in a dialogue.
A communication-efficient distributed machine learning scheme in vehicular network

For vehicular networks, while a federated learning (FL) approach can be effective in utilizing computing resources and data at vehicles, a large amount of communication resources are required to exchange learning model between vehicles and the central server. Therefore, it is particularly important to consider the communication overhead of FL. In this paper, we introduce knowledge distillation (KD) technology for FL to reduce communication overhead and improve communication efficiency in vehicular networks. The KD adopts the teacher-student pair method to train model. Specifically, we use KD technology to compress the teacher model into student model, where only the student model is sent to the central server, which can efficiently reduce the communication overhead. Further, considering different network environments and vehicle heterogeneity, we design a model switching algorithm to improve communication efficiency. Eventually, we perform some simulations for showing the advantages of proposed scheme over existing baselines.
Intelligent deep learning‐based hierarchical clustering for unstructured text data

Summary Document clustering is a technique used to split the collection of textual content into clusters or groups. In modern days, generally, the spectral clustering is utilized in machine learning domain. By using a selection of text mining algorithms, the diverse features of unstructured content is captured for ensuing in rich descriptions. The main aim of this article is to enhance a novel unstructured text data clustering by a developed natural language processing technique. The proposed model will undergo three stages, namely, preprocessing, features extraction, and clustering. Initially, the unstructured data is preprocessed by the techniques such as punctuation and stop word removal, stemming, and tokenization. Then, the features are extracted by the word2vector using continuous Bag of Words model and term frequency‐inverse document frequency. Then, unstructured features are performed by the hierarchical clustering using the optimizing the cut‐off distance by the improved sensing area‐based electric fish optimization (FISA‐EFO). Tuned deep neural network is used for improving the clustering model, which is proposed by same algorithm. Thus, the results reveal that the model provides better clustering accuracy than other clustering techniques while handling the unstructured text data.
CovidBChain: Framework for access‐control, authentication, and integrity of Covid‐19 data

In the Covid-19 pandemic, information about the medical equipment such as personal protective equipment, ventilators, testing kits, oxygen cylinders, ICU beds, and patient diagnostic status is a black box for the patients. This article proposes a blockchain-assisted Covid-19 big data chain (CovidBChain) framework to handle the Covid-19 data, which is of colossal size (volume), coming from different sources (variety) and generated at every time instance (velocity). CovidBChain is proposed to protect electronic health records and Covid-19 equipment's information from illegal modification. CovidBChain provides transparency, access control, and integrity to Covid-19 data. The status of critical equipment like ventilator, Covid-19 beds, oxygen cylinder, and ICU status each such operation is integrated into the CovidBChain as a transaction. A prototype has been simulated using Ganache, Metamask, InterPlanetary File System, and Reactjs. The comparative assessment using proof-of-work (PoW) and proof-of-authority (PoA) deduces that the upload and retrieval time in PoA is less than PoW, while the transaction cost is more in PoW. The overhead of message exchange communication is reduced by a factor of 4.3× in PoA as compared to the PoW approach. CovidBChain has been tested on the Ethereum official test network Ropsten for PoW and Goerli for PoA.
The Development of Integrative Qualities of Primary School Teachers in the Context of Distance Learning

The paper focuses on the need to facilitate the rapid development of integrative qualities of primary school teachers who work remotely. The authors examine the mechanism of methodological support for the development of integrative qualities of primary school teachers in the context of distance and blended learning. The review of scientific literature, detailed information on integrative qualities of the teacher, and the formation process design created the opportunity for successful development of integrative characteristics of teachers using a well-tired mechanism to support their professional training in the distance and blended learning modes. The development of integrative qualities, ensuring the successful implementation of modern educational principles, becomes an essential task of education and a key component of pedagogical skills. Professional activities of teachers require them to react quickly to situations and implement their actions in a timely manner. Every day, teachers are involved in a variety of activities determined by their integrative qualities. Teaching is governed by the uncertainty and dynamics of the learning process, which cannot be fixed. The educational process, as a living organism, can be constantly transformed. Thereby, it maintains its viability. The study aims to identify and theoretically justify the mechanism of methodological support for the development of integrative qualities of primary school teachers working in the distance and blended learning modes. To address the research questions, we opted to apply questionnaires, semi-structured interviews, comparative analysis, and pedagogical modeling. The paper presents the results of surveys and interviews with primary school teachers of rural settlements, urban-type villages and cities in the Republic of Tatarstan. Based on the analysis of obtained data, the authors identified the following components of the studied mechanism: organizational (structural); content-related; information and educational; analytical (reflexive). The researchers outlined the functions of the proposed mechanism and its requirements for success. The study also revealed some interesting facts and observations from the experience of primary school teachers involved in the distance learning environment.
Eigenvector-based Graph Neural Network Embeddings and Trust Rating Prediction in Bitcoin Networks

Given their strong performance on a variety of graph learning tasks, Graph Neural Networks (GNNs) are increasingly used to model financial networks. Traditional GNNs, however, are not able to capture higher-order topological information, and their performance is known to degrade with the presence of negative edges that may arise in many common financial applications. Considering the rich semantic inference of negative edges, excluding them as an obvious solution is not elegant. Alternatively, another basic approach is to apply positive normalization, however, this also may lead to information loss. Our work proposes a simple yet effective solution to overcome these two challenges by employing the eigenvectors with top-k largest eigenvalues of the raw adjacency matrix for pre-embeddings. These pre-embeddings contain high-order topological knowledge together with the information on negative edges, which are then fed into a GNN with a positively normalized adjacency matrix to compensate for its shortcomings. Through comprehensive experiments and analysis, we empirically demonstrate the superiority of our proposed solution in a Bitcoin user reputation score prediction task.
Association rule mining using new discrete elephant swarm approaches

Abstract In this paper, we are proposing two novel swarm intelligence approaches for solving discrete problems. Two different continuous evolutionary algorithms inspired by the behaviour of elephants, namely elephant herding optimization and elephant swarm water search algorithm, are studied and analysed in order to propose discrete versions of the latter called discrete elephant herding optimization and discrete elephant swarm water search algorithm. As an illustration of how our proposals can work on discrete problems, a case study on association rule mining is carried out where the proposed discrete algorithms are modelled and applied on the problem in order to extract interesting association rules from large‐scale databases. Extensive experiments on eight different real‐life and relevant datasets with various sizes showed that both our proposals yield good results that compete and sometimes outperform state of the art algorithms.
Performance evaluation of invariant-based inverse engineering by quantum speed limits

We study the performance of approximate invariant-based inverse engineering, in which we design approximate dynamics so that it realizes a target process, determine its parameter schedule by using invariant-based inverse engineering, and control a true system according to it. We find that the worst-case performance of this control scheme can be evaluated by using the designed dynamics. By using a quantum speed limit formula, we derive a lower bound for overlap between the true dynamics and approximate dynamics. Notably, it can be calculated without knowledge of the true dynamics. We apply this bound to the above control scheme. We also find that the present result may be used for control scheduling.Received 8 December 2021Accepted 6 October 2022DOI:https://doi.org/10.1103/PhysRevA.106.L040401©2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum state transferGeneral Physics
SmarTxT: A Natural Language Processing Approach for Efficient Vehicle Defect Investigation

The investigation of vehicle defects, which is generally led by the National Highway Traffic Safety Administration (NHTSA) in the U.S., is critical to the continued trust of the general public in the safety of vehicles. NHTSA routinely receives millions of reports of potential defects, complaints, recalls, and manufacturer communications, which may provide evidence of a new vehicle defect. However, the large quantity and text-based communication make efficiently identifying defect trends difficult for analysts. To accelerate the investigation of defect reports, we introduce a natural language processing (NLP) application that identifies key topics and similar defect reports to assist analysts and investigators. Further, our application is built to provide users with a web interface for interacting with the NLP models. The integration of NLP with current NHTSA datasets provides a method for quickly identifying defect trends in large text-based datasets. To demonstrate the effectiveness of our method, we apply our approach to two publicly available NHTSA datasets, namely the Technical Service Bulletins and Recalls Dataset.
Block ciphers identification scheme based on randomness test

In real scenarios, cryptanalysts can only obtain ciphertext of unknown cryptographic algorithms to conduct ciphertext only attacks. Cryptosystem identification is a prerequisite for further cryptanalysis. This paper proposes a new method of division for ciphertext sequence based on the four widely used cryptographic algorithms DES, 3DES, AES, and Blowfish, which have high security. We redesigned the ciphertext features using five detection methods in the NIST randomness test standard. In the case of using two working modes (ECB and CBC) of block ciphers, the proposed method in this paper obtains lower feature dimensionality and higher final recognition accuracy compared to some previous research works. This study has certain reference significance for the fast and accurate identification of large volume of ciphertext.
Hyperbolic Directed Hypergraph-Based Reasoning for Multi-Hop KBQA

The target of the multi-hop knowledge base question-answering task is to find answers of some factoid questions by reasoning across multiple knowledge triples in the knowledge base. Most of the existing methods for multi-hop knowledge base question answering based on a general knowledge graph ignore the semantic relationship between each hop. However, modeling the knowledge base as a directed hypergraph has the problems of sparse incidence matrices and asymmetric Laplacian matrices. To make up for the deficiency, we propose a directed hypergraph convolutional network modeled on hyperbolic space, which can better deal with the sparse structure, and effectively adapt to the problem of an asymmetric incidence matrix of directed hypergraphs modeled on a knowledge base. We propose an interpretable KBQA model based on the hyperbolic directed hypergraph convolutional neural network named HDH-GCN which can update relation semantic information hop-by-hop and pays attention to different relations at different hops. The model can improve the accuracy of the multi-hop knowledge base question-answering task, and has application value in text question answering, human–computer interactions and other fields. Extensive experiments on benchmarks—PQL, MetaQA—demonstrate the effectiveness and universality of our HDH-GCN model, leading to state-of-the-art performance.