Reviewerly/exHarmony huggingface dataset:
A fair experimental evaluation of distance correlation side-channel distinguisher

Side-channel attacks pose a severe threat to crypto-graphic implementations, allowing the attacker to recover secret information based on physical observations of the cryptographic device. Correlation Power Analysis is considered to be one of the most powerful attacks in the non-profiled scenario. In this paper, we consider the distance/Brownian correlation instead of the traditionally used Pearson coefficient. We give a fair comparison of our novel approach attacking AES on three different FPGA platforms and we discuss the distance correlation potential in the context of side-channel analysis.
On The Detection Of Adversarial Attacks Through Reliable AI

Adversarial machine learning manipulates datasets to mislead machine learning algorithm decisions. We propose a new approach able to detect adversarial attacks, based on eXplainable and Reliable AI. The results obtained show how canonical algorithms may have difficulty in identifying attacks, while the proposed approach is able to correctly identify different adversarial settings.
RSA Encryption and the Shor Factoring Algorithm

Abstract This chapter is the most formidable in the book, but it provides a deep and complete understanding of the classic Shor factoring algorithm. Alice and Bob set the context for the algorithm by explaining how the difficulty of factoring large numbers plays a critical role in securing encrypted communications channels, using RSA encryption as an example. The Shor algorithm, if implemented on a sufficiently large quantum computer, could defeat those encryption schemes by finding those factors relatively quickly, a task that would take years or more on even the fastest classical supercomputers. Alice and Bob lead us through some number theory to establish the connection between factoring and finding the period of a periodic function. They then show how the clever use of quantum algorithms—this is Shor’s main contribution—leads to a fast way to find the required period using quantum Fourier transforms.
NL2SQL Generation with Noise Labels based on Multi-task Learning

Abstract With the rapid development of artificial intelligence technology, semantic recognition technology is becoming more and more mature, providing the preconditions for the development of natural language to SQL (NL2SQL) technology. In the latest research on NL2SQL, the use of pre-trained models as feature extractors for natural language and table schema has led to a very significant improvement in the effectiveness of the models. However, the current models do not take into account the degradation of the noisy labels on the overall SQL statement generation. It is crucial to reduce the impact of noisy labels on the overall SQL generation task and to maximize the return of accurate answers. To address this issue, we propose a restrictive constraint-based approach to mitigate the impact of noise-labeled labels on other tasks. In addition, parameter sharing approach is used in noiseless-labeled labels to capture each part’s correlations and improve the robustness of the model. In addition, we propose to use Kullback-Leibler divergence to constrain the discrepancy between hard and soft constrained coding of noisy labels. Our model is compared with some recent state-of-the-art methods, and experimental results show a significant improvement over the approach in this paper.
ERNIE based intelligent triage system

With the development of Internet+medicine, online medical treatment has gradually become the new development direction of medical industry. Many hospitals provide online registration services to the public, and due to the lack of professional medical knowledge of patients, the problem of wrong registration often occurs. How to use deep learning technology to provide professional help to patients and reduce the waste of medical resources has become an urgent problem. To address the above problems, this paper proposes an ERNIE-based text classification model for intelligent triage. The model consists of two parts, ERNIE and BiGRU. The pre-training model ERNIE is used to extract the feature representation of the text, and then input to the BiGRU neural network to get the text classification results. Compared with different models on 2 datasets, the experimental results show that the model proposed in this paper has better accuracy and recall than other models.
The Online Course of School Students’ Training to Basic State Examination of Computer Studies as a Means of the Implementation of the Educational Activity in the Students’ Online Communities

The article deals with a number of issues of teaching school students in online communities. There is specified the definition of online communities, there is considered the classification of online communities. The authors describe the theoretical approaches to the development of the online course of Computer Studies in the context of the implementation of the Federal State Educational Standards and the digital transformation of education on the basis of the online course “Training school students to Basic State Examination of Computer Studies” implemented in Volgograd State Socio-Pedagogical University”.
The Concept of Creating an Institute of Tutoring in The System of Higher Education Through the Introduction of Interdisciplinary Modular Educational Technologies

The article describes the concept of creating a tutoring institution in the system of higher education through the introduction of interdisciplinary modular educational technologies. The main directions of creating a tutoring institute are revealed: theoretical and methodological, legal, organizational substantiation of the concept of creating a tutoring institute in the higher education system through the introduction of interdisciplinary modular educational technologies, development of a model for creating a tutoring institute in the higher education system through the introduction of interdisciplinary modular educational technologies.
Improved stereo matching framework with embedded multilevel attention

The recent advent of deep convolutional neural networks (CNNs) in stereo matching has led to significant improvements. However, current CNN methods still face challenges in incorporating hierarchical context information with global dependencies and lacking the discriminative ability of feature representation to resolve matching ambiguities in ill-conditioned regions. To address the aforementioned problems, we propose an improved stereo matching framework that joins a stereo backbone network and an embedded independent multilevel attention subnetwork in an end-to-end trainable pipeline. The stereo backbone network applies a residual atrous spatial pyramid pooling integrated with channelwise attention to capture richer multiscale contextual information and selectively enhance discriminative features. This is followed by unary feature concatenation to construct cost volume for disparity prediction. To further improve performance, the embedded multilevel attention subnetwork learns global coherent contextual information to generate three attention streams, which are used to boost the unary feature representations with spatial encoding, enhance the quality of cost volume, and refine the disparity map, respectively. We show that appending the proposed multilevel attention subnetwork to the stereo backbone network produces significant improvements in matching accuracy. The experimental results on Scene Flow and KITTI 2012/2015 demonstrate that our method can achieve competitive performance in stereo matching.
Research and Performance Optimization of Visualization of Panoramic Monitoring in Responsive Distribution Network

Abstract As the scale of the power grid continues to expand, the number of power grid devices in the national power system is increasing, the structure of the power grid is becoming more and more complex, and the information construction of the distribution network is improving, but the visualization is relatively low in real time. In terms of informationization, the current information presentation and interaction mode cannot adapt to the rapid expansion of power supply scale and daily distribution network operation and maintenance management. In this paper, we study GIS-based grid panoramic visualization display technology and responsive visualization solutions for multiple terminals. These solutions not only improve the monitoring efficiency of the distribution network, but also reduce the time of finding and overhauling when problems occur. It also enables visualization of topological data and timely access to the required information. The experiments in this paper use clustering methods to optimize the visualization elements as well as to optimize the GIS rendering, which effectively improves the visualization efficiency.
Efficient mobility modeling of autonomous movement planning trajectories using AI algorithms

The path planning plays an important role for autonomous systems. Efficient comprehension of the surrounding environment and the effective generation of an optimal collision-free path are two essential elements to resolve a path planning problem. Artificial intelligence permits solving issues related to path planning, where several algorithms are currently implemented for this purpose. In this work, we will consider analytically and theoretically four AI algorithms, namely: RRT, RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , Q-Learning and GAN. We will demonstrate the different parameters affecting each algorithm to finally perform a performance analysis for various optimization metrics like execution time through simulation based experiments. Besides implementing each algorithm, we present a reliable contribution of parameters by exploring new environments to give a mobile node fixed trajectories for independent and autonomous mobilities.
Machine and deep learning approaches for modeling global horizontal irradiation dynamics: a case study

Global Horizontal Irradiation (GHI) is the most critical parameter in the design of solar energy plants. More than being relatively expensive, GHI measurement stations require frequent maintenance and their accuracies are still questionable. Thus, one of the frequent practices to cope with such difficulties is the dynamic prediction of GHI. Several techniques have been already utilized for forecasting GHI including time-series, swarm intelligence and many empirical equations. In the last few years, machine learning (ML) has emerged as a strong tool for modeling complex dynamic systems. Due to the non-availability of accurate meteorological data known to affect the GHI, this paper aims to investigate machine/deep learning algorithms for predicting GHI dynamics. The proposed models used previous lags of the GHI itself as unique explanatory variables. Based on the King Abdullah Center for Atomic and Renewable Energy (KACARE) records collected from Hail, Saudi Arabia, and statistical performance indicators, several dynamic models are investigated, tested and compared. The obtained results have shown the superiority of deep-learning artificial neural networks (DL- ANNs) with small numbers of neurons in 5-hidden layers structure trained using resilient-propagation fast algorithm. The best performances obtained for the testing dataset were a MAPE of 5.3219%, an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.7993 and an RMSE of 5.8418 wh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /day.
Self-supervised Domain Adaptation Model Based on Contrastive Learning

Contrastive learning is a typical discriminative self-supervised learning method, which can learn knowledge from unlabeled data. Unsupervised domain adaptation (UDA) aims to predict unlabeled target domain data. In this paper, we propose a self-supervised domain adaptation model based on contrastive learning, which applies the idea of contrastive learning to UDA, named siam-DAN. In this model, we first use the clustering method to obtain the pseudo-labels of the target domain data, then combine the labeled source domain data to construct the positive and negative examples required for contrastive learning to train the model, so that makes the distribution of samples of the same class in the representation space overlap as much as possible and finally enable the model to learn domain-invariant features. We evaluate the performance of our proposed model on three public benchmarks: Office-31, Office-Home, and VisDA-2017, and achieve relatively competitive results.
Features Engineering and Features Extraction of Volcano-Tectonic (VT) Earthquake

Abstract A volcano-Tectonic earthquake, commonly referred to as VT, is an earthquake caused by magma intrusion that increases the pressure below the volcano’s surface. The accumulation of stress that continuously affects the elasticity of rocks causes fractures when the elasticity limit of rocks is exceeded. VT is one of the earthquakes used as a parameter to decide the level of volcanic activity. To understand the characteristics of VT, it is necessary to do features engineering, which is a process of extracting features so that the characteristics of VT are obtained. The data used in this study was the VT earthquake when Agung was in crisis in 2017. The extraction process is conducted by performing statistics calculations in temporal and spectral domains. The waveform of VT is univariate time series data, and to perform the extraction of features, this study uses changes in amplitude value to the time taken from the waveform. Features that were successfully extracted from this study are as many as 48 features. The result of the extraction of these features can be used as input parameters in performing auto-classification of VT using machine learning.
Spherical Tree Structured Self-Organizing Map

research-article Share on Spherical Tree Structured Self-Organizing Map Authors: Hiroshi Dozono Faculty of Science and Engineering, Saga University, Japan Faculty of Science and Engineering, Saga University, JapanSearch about this author , Koki Yoshioka Faculty of Science and Engineering, Saga University, Japan Faculty of Science and Engineering, Saga University, JapanSearch about this author , GEN Niina R&D center Saga Densan Center. 1 Hyogo, Japan R&D center Saga Densan Center. 1 Hyogo, JapanSearch about this author Authors Info & Claims ICMLC 2022: 2022 14th International Conference on Machine Learning and Computing (ICMLC)February 2022 Pages 495–500https://doi.org/10.1145/3529836.3529900Online:21 June 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
When blockchain meets artificial intelligence: An application to cancer histopathology

A recent study by Saldanha et al. demonstrates that blockchain-based models outcompeted local models and performed similarly with merged models to predict molecular features from cancer histopathology images. The results reveal the capability of decentralized models in molecular diagnosis of cancer.
Tweet Sentiment Extraction Using Byte Level Pretrained Language Model∗

Research on sentiment analysis developed rapidly in recent years, and twitter sentiment analysis is one of the most popular topics. Besides classifying the sentiment, it is also important to find out the decisive phrases or words of the text to the classified sentimental category. In this paper, we proposed and developed byte-level pretained RoBERTa models, they are designed to extract phrases from tweet data with sentiment labels. We compared RoBERTa model and its’ variants, including RoBERTa-base, RoBERTa-large, XLM-RoBERTa-base, and RoBERTa-large-mnli. We build the model with RoBERTa model and CNN, then train the model with given tweet text and sentiment labels so that the deciding phrases of sentiments can be predicted. Our results show that RoBERTa-base obtains Jaccard score of 0.712 and training time of 240 minutes in total, which is the best performance among all the models.
Unsupervised Machine Learning for Drone Forensics through Flight Path Analysis

Drones have become prevalent for the sustenance of routine services including the delivery of goods, premise surveillance and for carrying out observation and reporting of phenomena, such as weather patterns. The vulnerability of a drone to a cyber attack is significant. The compromise of a drone in flight may cause flight path alteration, a crash and sabotage of sensitive captured data. In the event of such compromise, the process of investigating a captured and/or crashed drone as part of a digital forensic investigation could be tedious, due to data type, volume and availability. We propose an unsupervised machine learning-based approach for extracting forensically sound evidence from such drones and test its efficacy for a specific drone type, namely, DJI Phantom P4.
Chinese NER with High-Level Features in Specific Domain

In recent years, the character-word lattice structure has achieved good performance in Chinese named entity recognition (NER). However, in some specific domain, like marine industry domain, there are many specialized words that are hard to be segmented to utilize. Facing this challenge, it is necessary to employ a method to better identify the domain-specific entities with advanced features. In this paper, we develop a new method based on multivariate data embedding which further extracts higher-level character features in the embedding layer. Specifically, we extract higher-level character features by CNN and integrate them with the lattice representation to obtain enhanced embedding representation. Our model exploits the character information that can better capture the morphological and semantic information of characters to provide information support for NER. Experimental results on our marine industry dataset demonstrate the superiority of our approach. Specially, it outperforms the most previous model. And the ablation study validates the effect of the advanced features extraction.
Report on the 1st International Workshop on Learning to Quantify (LQ 2021)

The 1st International Workshop on Learning to Quantify (LQ 2021 - https://cikmlq2021.github.io/), organized as a satellite event of the 30th ACM International Conference on Knowledge Management (CIKM 2021), took place on two separate days, November 1 and 5, 2021. As the main CIKM 2021 conference, the workshop was held entirely online, due to the COVID-19 pandemic. This report presents a summary of each keynote speech and contributed paper presented in this event, and discusses the issues that were raised during the workshop.
Self-supervised Depth Estimation with High Resolution Features and Non-local Information

Depth estimation is one of the most challenging tasks in computer vision, especially in self-supervised learning ways without restrictions of high-cost labels. Self-supervised depth estimation aims to infer three-dimensional space structures from two-dimensional planar images, only taking image pairs or sequences as supervision. Most existing methods adopt the encoder-decoder framework with skip-connection and recover the high-resolution depth maps from high-resolution low-level and low-resolution high-level feature maps. However, it is proved that high-resolution high-level feature maps, which are sensitive to illumination, color, texture, etc., are necessary for depth estimation. In this paper, we present a novel approach to extract high-level feature maps at all scales and introduce a self-attention mechanism to consider non-local features. The main improvements of our proposed method are two-fold:1) we combined the high-resolution feature extraction sub-network and extract high-resolution high-level features by connecting the high-to-low resolution convolution streams in parallel; 2) we embed the self-attention module with the features pyramid module(FPA) to obtain general context at large-scale features. The experiments evaluated on the KITTI benchmark have demonstrated that our network outperforms most existing methods and produces more accurate depth maps.
Volumes of parent Hamiltonians for benchmarking quantum simulators

We investigate the relative volume of parent Hamiltonians having a target ground state up to some fixed error $\ensuremath{\epsilon}$, a quantity which sets a benchmark on the performance of quantum simulators. For vanishing error, this relative volume is of measure zero, whereas for a generic $\ensuremath{\epsilon}$ we show that it increases with the dimension of the Hilbert space. We also address the volume of parent Hamiltonians when they are restricted to be local. For translationally invariant Hamiltonians, we provide an upper bound to their relative volume. Finally, we estimate numerically the relative volume of parent Hamiltonians when the target state is the ground state of the Ising chain in a transverse field.
SKEM++: SEMANTIC KEYWORD EXTRACTION MODEL USING COLLECTIVE CENTRALITY MEASURE ON BIG SOCIAL DATA

In recent times, Online Social Network (OSN) has accumulated a massive volume of user-generated data available in an unstructured format. It consists of user ideas, responses, and opinions on various topics. It extracts essential keywords in OSN, which is endowed with many exciting applications such as information recommendation or viral marketing. This paper emphasizes the importance of semantic graph-based methods for extracting vital keywords experimentally using a novel SKEM++ method. It is an innovative method for keyword extraction from OSN based on centrality measures. It utilizes a distributed computing approach to calculate the network Collective Centrality Measure (CCM) for each node and improve the semantics of keywords. The distributed approach is more scalable and computationally efficient than the conventional system, making it more suitable for large-scale real-time data sets such as the OSN. Experimental outcomes on the real-time Twitter Data set to infer the dominance of the proposed Collective Centrality Measure(CCM) method in evaluation with contemporary schemes in terms of F-score by 81% and recall by 80% and precision by 80% using Semantic Analysis.