Reviewerly/exHarmony huggingface dataset:
A Knowledge-Graph-Driven Method for Intelligent Decision Making on Power Communication Equipment Faults

The grid terminal deploys numerous types of communication equipment for the digital construction of the smart grid. Once communication equipment failure occurs, it might jeopardize the safety of the power grid. The massive amount of communication equipment leads to a dramatic increase in fault research and judgment data, making it difficult to locate fault information in equipment maintenance. Therefore, this paper designs a knowledge-graph-driven method for intelligent decision making on power communication equipment faults. The method consists of two parts: power knowledge extraction and user intent multi-feature learning recommendation. The power knowledge extraction model utilizes a multi-layer bidirectional encoder to capture the global features of the sentence and then characterizes the deep local semantics of the sentence through a convolutional pooling layer, which achieves the joint extraction and visual display of the fault entity relations. The user intent multi-feature learning recommendation model uses a graph convolutional neural network to aggregate the higher-order neighborhood information of faulty entities and then the cross-compression matrix to solve the feature interaction degree of the user and graph, which achieves accurate prediction of fault retrieval. The experimental results show that the method is optimal in knowledge extraction compared to classical models such as BERT-CRF, in which the F1 value reaches 81.7%, which can effectively extract fault knowledge. User intent multi-feature learning recommendation works best, with an F1 value of 87%. Compared with the classical models such as CKAN and KGCN, it is improved by 5%~11%, which can effectively solve the problem of insufficient mining of user retrieval intent. This method realizes accurate retrieval and personalized recommendation of fault information of electric power communication equipment.
Website blocking in the European Union: Network interference from the perspective of Open Internet

Abstract By establishing an infrastructure for monitoring and blocking networks in accordance with European Union (EU) law on preventive measures against the spread of information, EU member states have also made it easier to block websites and services and monitor information. While relevant studies have documented Internet censorship in non‐European countries, as well as the use of such infrastructures for political reasons, this study examines network interference practices such as website blocking against the backdrop of an almost complete lack of EU‐related research. Specifically, it performs and demonstrates an analysis for the total of 27 EU countries based on three different sources. They include first, tens of millions of historical network measurements collected in 2020 by Open Observatory of Network Interference volunteers from around the world; second, the publicly available blocking lists used by EU member states; and third, the reports issued by network regulators in each country from May 2020 to April 2021. Our results show that authorities issue multiple types of blocklists. Internet Service Providers limit access to different types and categories of websites and services. Such resources are sometimes blocked for unknown reasons and not included in any of the publicly available blocklists. The study concludes with the hurdles related to network measurements and the nontransparency from regulators regarding specifying website addresses in blocking activities.
Computer-aided diagnostic system for breast cancer detection based on optimized segmentation scheme and supervised algorithm

Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time.
Predicting Customer Intentions in Purchasing Property Units Using Deep Learning

The primary point of business is the offering stage, which is crucial for success. Property marketing during a pandemic presents unique challenges that require strategies to minimize the impact. To determine effective marketing policies and strategies, it is necessary to predict and understand customer desires. Predicting customer behavior when making purchases can have significant implications, including providing more targeted and efficient advertising to increase sales. This study aims to analyze the ability to predict prospective customers' purchasing behavior using deep learning and pre-trained models. The data for this analysis was collected through follow-up with prospective customers by sales personnel and processed using techniques such as text embedding with FastText and BERT, followed by training with FastText, BERT, and BiLSTM. The results showed that the FastText model achieved the highest performance, with an accuracy value of 94.19%, an AUC value of 92.03%, and 94.28% precision. The model was then implemented into a web application using the Flask framework.
Ponzi Scheme Detection Based on Control Flow Graph Feature Extraction

The blockchain ecosystem is expanding as a result of advancements in blockchain technology and the emergence of BaaS (Blockchain as a Service) platforms. Smart contracts are designed to carry out diverse business operations, but there is a risk of Ponzi schemes being concealed within them. These schemes masquerade as investment agreements and deceive users, resulting in substantial losses for the blockchain community. Detecting Ponzi schemes in smart contracts is crucial. This study introduces a machine learning approach to identify Ponzi schemes by extracting features from smart contracts using the control flow graph. During the construction of the control flow graph for the smart contract’s bytecode, elements unrelated to its functionality are identified and eliminated. We utilize the control flow graph to extract n-gram Term Frequency and n-gram Term Frequency-Inverse Document Frequency features. These features are respectively employed to construct a Random Forest model for Ponzi scheme detection. To address the issue of imbalanced samples, the SVM_SMOTE oversampling algorithm is applied to balance the number of positive and negative samples. The results from experiments conducted on a real-world dataset demonstrate the effectiveness of our approach. The feature extraction method based on the control flow graph outperforms the method based on continuous text. Additionally, the Random Forest model utilizing SVM_SMOTE outperforms four existing models.
Federated Learning-driven Trust Prediction for Mobile Edge Computing-based IoT Systems

We propose a federated learning-based data-driven trust prediction method to meet the demand of high-accuracy IoT service trustworthiness prediction in Mobile Edge Computing (MEC) with low convergence time. Our research focuses on the mixture distribution and heterogeneity features of IoT trust information in distributed MEC environments and formulates the task of distributed IoT trust prediction on top of MEC network topologies as a federated optimization problem. We then employ Federated Expectation-Maximization to mitigate the federated optimization problem by taking into account the data mixture distribution and heterogeneity. We conduct a series of experiments upon simulated MEC-based IoT environments crafted on top of a real-world IoT dataset. The experimental results show that our proposed methods can achieve better balance between prediction accuracy and model training efficiency than a state-of-the-art data-driven MEC-based IoT service trust prediction method and a Federated Averaging-based method.
Studying Developer Eye Movements to Measure Cognitive Workload and Visual Effort for Expertise Assessment

Eye movement data provides valuable insights that help test hypotheses about a software developer's comprehension process. The pupillary response is successfully used to assess mental processing effort and attentional focus. Relatively little is known about the impact of expertise level in cognitive effort during programming tasks. This paper presents a quantitative analysis that compares the eye movements of 207 experts and novices collected while solving program comprehension tasks. The goal is to examine changes of developers' eye movement metrics in accordance with their expertise. The results indicate significant increase in pupil size with the novice group compared to the experts, explaining higher cognitive effort for novices. Novices also tend to have a significant number of fixations and higher gaze time compared to experts when they comprehend code. Moreover, a correlation study found that programming experience is still a powerful indicator when explaining expertise in this eye-tracking dataset among other expertise variables.
Role of ML and DL in Detecting Fraudulent Transactions

Ever since the inception of online transactions, it has positively impacted the ease of business by making money transactions straightforward and secure, irrespective of location or amount of money. However, along with the increase in online transactions, the number of fraudulent transactions also increased. With the rapid growth in technology in the current environment, fraudsters are creating new methods to conduct these fraudulent transactions, which seems legitimate. Therefore, there is an ever-growing need to curb these incidents using real-time detection and reporting. This chapter explores the different techniques such as the ANNs or Artificial Neural Networks, CNNs or Convolutional neural networks, Rule-based methods(RBM), Hidden Markov Models(HMM), Autoencoders, and much more, in machine learning and deep learning. Most of the datasets for these models are not accessible to the public because of the privacy concerns of the financial institutes. We also assess various parameters like precision, accuracy, and recall of these solutions to make a comprehensive study. The chapter is concluded with the latest improvements and the future prospects to keep track of these fraudulent transactions.
Time Binning Method for Nonpulsed Sources Characterization With a Superconducting Photon Number Resolving Detector

Photon number resolving detectors find space in many fields, such as quantum optics, boson sampling and fluorescence spectroscopy. In particular, the reconstruction of the input photon distribution is essential in quantum communications to detect photon-number-splitting attacks. In this work, we discuss the operation configurations of a photon number resolving detector based on superconducting nanostrips at a wavelength of 1550 nm from a temporal point of view. We set a time binning and acquired the number of recorded pulses per bin by means of a time-to-digital converter. We studied the predictions of two theoretical models and compared them to the experimental data in order to analyse their operation regimes depending on the binwidth and to employ them for the reconstruction of the input photon distribution. We applied this method to a continuous-wave laser source, showing that the former can be used for the characterization of non-pulsed light sources, even with a photon emission rate so low that the dark count rate of a superconducting nanostrip is not negligible.
Fall Detection With a Nonintrusive and First-Person Vision Approach

Falls have been widely recognized as one of the most dangerous incidents for the elderly and other people with mobility limitations. This problem has attracted wide scientific interest, which has led to several investigations based on nonvision wearable sensors and static cameras. We investigate the challenge of fall detection and recognition using egocentric wearable cameras, which, besides portability and affordability, capture visual information that can be further leveraged for a broad set of lifelogging applications. In this work, five volunteers were equipped with two cameras each, one attached to the neck and the other to the waist. They were asked to simulate four kinds of falls and nine types of nonfalls. The newly collected dataset consists of 5858 short video clips, which we make available online. The proposed approach is a late fusion methodology that combines spatial and motion descriptors along with deep features extracted by a pretrained convolutional neural network. For the spatial and deep features, we consider the similarity of such features between frames in regular intervals of a given time window. In this way, it is the transition between such frames that are encoded in our approach, while the actual scene content does not play a role. We design several experiments to investigate the best camera location and performance for indoor and outdoor activities and employ leave-one-subject-out cross-validation to test the generalization ability of our approach. For the fall detection (i.e., two-class) problem, our approach achieves 91.8% accuracy, 93.6% sensitivity, and 89.2% specificity.
The CGAS Deep Learning Algorithm for P-Wave Arrival Time Picking of Mining Microseismic Events

In light of the inadequacies of traditional P-wave arrival picking algorithms using long and short windows, which exhibit poor anti-noise ability and do not meet the requirements for efficient and accurate P-wave arrival picking, this study proposes a CGAS deep learning algorithm for P-wave arrival picking in mine micro-seismic events. The algorithm constructs the depth characteristics of microseismic waveform through deep learning training and converts the problem of picking up the microseismic waveform into a classification problem by employing data set segmentation and classification. This allows the algorithm to pick up the characteristic information of microseismic waveform and improve the accuracy of P-wave picking. The algorithm was applied to pick up the three-component micro-seismic waveforms obtained from the microseismic monitoring system in a mining area in Liaoning from 2019 to 2020. Through ablation experiments, it was found that adding each structure to the pickup model improved the relevant indexes to some extent. When all network structures were used, the accuracy of the P-wave pick-up model reached 98.61%, ensuring the accuracy of P-wave pickup upon arrival and demonstrating the effectiveness of each layer structure in the classification of P-wave arrival. Compared to other P-wave arrival picking algorithms such as STA/LTA, U-Net++, Dpick, PhaseNet and EQTransformer, the algorithm proposed in this paper exhibited high precision in P-wave arrival time picking, providing a new research idea and technical means for efficiently determining the P-wave arrival of events.
Fine-Grained Species Recognition With Privileged Pooling: Better Sample Efficiency Through Supervised Attention

We propose a scheme for supervised image classification that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets. Our main motivation is the recognition of animal species for ecological applications such as biodiversity modelling, which is challenging because of long-tailed species distributions due to rare species, and strong dataset biases such as repetitive scene background in camera traps. To counteract these challenges, we propose a visual attention mechanism that is supervised via keypoint annotations that highlight important object parts. This privileged information, implemented as a novel privileged pooling operation, is only required during training and helps the model to focus on regions that are discriminative. In experiments with three different animal species datasets, we show that deep networks with privileged pooling can use small training sets more efficiently and generalize better.
Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multisource, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative, and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the “single” disturbance can be either rejected or attenuated. Since the pioneering work in 2012 [42], a novel state estimation methodology called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">composite disturbance filtering</i> (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances.
Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation

In this article, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagation (BP). The proposed filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the PMB mixture (PMBM) posterior after the update step is approximated as a PMB. The efficient PMB approximation relies on several important theoretical contributions. First, we present a PMBM conjugate prior on the posterior of sets of trajectories for a generalized measurement model, in which each object generates an independent set of measurements. The PMBM density is a conjugate prior in the sense that both the prediction and the update steps preserve the PMBM form of the density. Second, we present a factor graph representation of the joint posterior of the PMBM set of trajectories and association variables for the Poisson spatial measurement model. Importantly, leveraging the PMBM conjugacy and the factor graph formulation enables an elegant treatment on undetected objects via a Poisson point process and efficient inference on sets of trajectories using BP, where the approximate marginal densities in the PMB approximation can be obtained without enumeration of different data association hypotheses. To achieve this, we present a particle-based implementation of the proposed filter, where smoothed trajectory estimates, if desired, can be obtained via single-object particle smoothing methods, and its performance for EOT with ellipsoidal shapes is evaluated in a simulation study.
PAR Contest 2023: Pedestrian Attributes Recognition with Multi-task Learning

PAR Contest 2023 is a competition, organized within CAIP 2023 conference, among methods based on multi-task learning, aimed at the recognition of binary and multi-class pedestrian attributes from images. This topic is recently attracting a great interest of various research groups due to the variety of applications in the field of forensics, digital signage, social robotics, people tracking and multi-camera person re-identification. Multi-task learning allows to solve the multi-class recognition problem with a single multi-task neural network, with a learning procedure that exploits the interdependencies between different tasks to produce an efficient and effective model. To this aim, we make available for the participants the MIVIA PAR Dataset, consisting of 105,244 pedestrian images, already divided in training and validation sets, partially annotated with 5 attributes: upper clothes and lower clothes color, gender, bag, hat. The submitted methods will be evaluated in terms of mean accuracy over a private test set, including more than 20,000 images without overlaps in terms of subjects and scenarios with respect to training and validation sets. The baseline results, reported in this paper, demonstrate that the contest is challenging and that by participating to the competition it is possible to advance the state of the art in pedestrian attributes recognition.
Algorithms and Hardness for the Longest Common Subsequence of Three Strings and Related Problems

A string is called a square (resp. cube) if it is in the form of $$XX=X^2$$ (resp. $$XXX=X^3$$ ). Given a sequence S of length n, a fundamental problem studied in the literature is the problem of computing a longest subsequence of S which is a square or cube (i.e., the longest square/cubic subsequence problem). While the longest square subsequence (LSS) can be computed in $$O(n^2)$$ time, the longest cubic subsequence (LCubS) is only known to be solvable in $$O(n^5)$$ time, using the longest common subsequence of three strings (LCS-3) as a subroutine (which was much less studied compared with LCS for two strings, or LCS-2). To improve the running time for LCubS, we look at its complementary version and also investigate LCS-3 for three strings $$S_1,S_2,S_3$$ , with input lengths $$m\le n_1\le n_2$$ respectively. Firstly, we generalize an algorithm by Nakatsu et al. for LCS-2 to have an $$O(n_1n_2\delta )$$ algorithm for computing LCS-3, where $$\delta $$ is the minimum number of letters to be deleted in $$S_1$$ to have an LCS-3 solution for $$S_1$$ , $$S_2$$ and $$S_3$$ . This results in an $$O(k^3n^2)$$ algorithm for LCubS, where k is the minimum number of letters deleted in S to have a feasible solution. Then, let $$\mathcal{R}$$ be the number of triples (i, j, k) that match in the input, i.e., $$S_1[i]=S_2[j]=S_3[k]$$ , we show that LCS-3 can be computed in $$O(n+\mathcal{R}\log \log n+\mathcal{R}^2)$$ time (n is the maximum length of the three input strings). Finally, we define the t-pseudo-subsequence of S under an integer parameter t, which is a string Z containing a subsequence $$S'$$ of S such that $$S'$$ can be obtained from Z by deleting at most t letters. Subsequently, we study the longest majority t-pseudo-subsequence (LMtPS) of $$S_i, i=1..3$$ , which is a t-pseudo-subsequence $$T=t_1t_2\cdots t_K$$ of $$S_i, i=1..3$$ , with the maximum length K; moreover, when T is aligned with some subsequence $$S'_i$$ ’s of length K in $$S_i, i=1..3$$ , each $$t_j$$ matches at least two letters with $$S'_i, i=1..3$$ . We show that LMtPS of three strings $$S_1,S_2$$ and $$S_3$$ is polynomially solvable, while if we require additionally that all letters in $$\varSigma $$ appear in the solution T then it becomes NP-complete, via a reduction to a new SAT instance called Even-(3,B2)-SAT.