Reviewerly/exHarmony huggingface dataset:
Research on Short Text Classification Based on Word2Vec Microblog

As a social product in the Internet age, Microblog has the characteristics of concise language and large amount of information. Mining the information in the Microblog text is of great significance to topic detection and personalized recommendation systems. Compared with traditional text classification, short text has the problems of dimensionality and sparseness. The article first uses the neural network's Word2Vec to get the word vector, and then introduces the category attribute into the word weight. The short text vector is obtained by the weighted summation method, and finally the text data is trained using the SVM classifier. Through the comparison of experimental results, the effectiveness of the method is verified.
Shape and Texture Features Extraction Using Segmented Histopathological Images

For women, breast cancer occupies the second position in causing the occurrence as well as mortality. Optimum segmentation and feature extraction play a crucial role while categorizing medical images. The proposed paper integrates marker-based watershed approach with K-means clustering data for optimum segmentation. It deals with detail component protection. The work focus on feature extraction from the segmented histopathological images. Feature selection is necessary for minimizing the redundant parameters. Optimum features necessary for image categorization were evaluated. The proposed work provides high accuracy during image classification.
Using OCR Framework and Information Extraction for Thai Documents Digitization

At present, digital transformation has taken into an account for many enterprises and businesses. The data has been stored and structured into usable format in order for analytics propose. However, some data has been storing in the format of hard copies, scanned document, images and PDFs which need to be transformed into digital form for future use. The objective of this paper is to propose the technique for recognizing the text from a physical document into digital format, by using Optical Character Recognition, also called OCR that attempts to extract all the text from photocopies into database structure. The experimental studies showed that the proposed technique makes the digitized documents completely searchable and editable with the average of accuracy performance around 75.38% for extracting attributes and 66.92% for extracting values from printed documents. This technology provides significant benefits to all businesses. Utilizing OCR helps businesses to easily seek for highly useful information throughout the document, and also there is a reduced amount of paper taking up space in the office.
Core-GAE: Toward Generation of IoT Networks

To realize simulation experiments in large-scale Internet of Things (IoT) networks, this work studies the utilization of deep graph generative models to generate IoT networks, which can provide an economic approach facilitating IoT to meet the requirements of real-time performance, interoperability, energy efficiency, and coexistence. In IoT, nodes have different attributes, different connection ways with surrounding nodes, and different compactness of the region, which pose great challenges for network generation. By leveraging the properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -core and variational autoencoder during network generation, we propose a variable graph autoencoder called Core-GAE incorporating the coreness of nodes. In contrast to previous graph generative models, Core-GAE can preserve the local proximity similarity and maintain the global structural features simultaneously when learning the structural features of graphs. All three of the tasks we experimented with on four data sets show that Core-GAE exhibits better performance than previous ones.
Classification of ancient inscription images on the basis of material of the inscriptions

Machine Learning and AI has allowed us to process images and help us solve vital problems. In this paper, we are classifying inscription images into three image inscription classes namely stone inscriptions, metal inscriptions and palm leaves inscriptions. Due to decaying materials of ancient inscriptions, the classification of such materials becomes challenging. To address this problem, we are using various feature extraction methods like GLCM, KAZE, BRISK to implement texture based feature detection and subsequently classifying them. Both linear and non-linear methods for feature extraction are being used. The paper is concluded by performing classification of images and also making comparisons between all the feature extraction methods in terms of memory required by the algorithm, time scalability and accuracy score of the method. The paper consists of rich information which would be useful in decision making in classification problems.
Performance Analysis of Big Data ETL Process over CPU-GPU Heterogeneous Architectures

While GPUs have been utilized in the analysis stage of big data processing, the demand for GPU use in the extract-transform-load (ETL) stage has recently been increasing. There have been several research efforts to use GPUs for query processing in database systems. However, most of them are mainly focused on batching input data as much as possible to increase the overall throughput with GPUs. Moreover, they try to execute a query plan by only using a single device (only CPU or only GPU), based on the assumption that the PCIe transfer overhead between CPU and GPU is significant. In contrast, this paper presents several interesting observations and reveals that it is better to use a CPU-GPU heterogeneous query plan in some cases. For example, the heterogeneous query plan proved to be efficient when the input data size is small. This is because the PCIe overhead is not as serious as expected for small-sized data and can be offset with performance gains by partially using the GPU. In addition, even when the data size is large, it is sometimes better to use the CPU-GPU heterogeneous query plan rather than only the GPU plan, since some relational operations prefer the CPU over the GPU. To demonstrate these uses, this paper presents several experimental results and their analyses with Spark SQL in a CPU-GPU heterogeneous computing environment.
On a Dirichlet Process Mixture Representation of Phase-Type Distributions

An explicit representation of phase-type distributions as an infinite mixture of Erlang distributions is introduced. The representation unveils a novel and useful connection between a class of Bayesian nonparametric mixture models and phase-type distributions. In particular, this sheds some light on two hot topics, estimation techniques for phase-type distributions, and the availability of closed-form expressions for some functionals related to Dirichlet process mixture models. The power of this connection is illustrated via a posterior inference algorithm to estimate phase-type distributions, avoiding some difficulties with the simulation of latent Markov jump processes, commonly encountered in phase-type Bayesian inference. On the other hand, closed-form expressions for functionals of Dirichlet process mixture models are illustrated with density and renewal function estimation, related to the optimal salmon weight distribution of an aquaculture study.
Improved Thyroid Disease Prediction Model Using Data Mining Techniques with Outlier Detection

Thyroid disease is one of the most common diseases worldwide. Thyroid gland in human body controls the metabolic activities of human body. Any malfunctioning in thyroid gland will lead to serious health issues. Diagnosis of thyroid is an important task. There has been enormous growth in thyroid cases worldwide and it needs serious attention as soon as possible. According to the survey conducted in India on thyroid disease, approximately 42 million people suffer from thyroid disease. This paper presents an extensive survey done in the past on the thyroid disease prediction using data mining techniques. In this paper, an extensive study on the various attributes used in thyroid prediction has been conducted. This paper studies various data mining techniques like Naïve Bayes, Decision Trees etc. for thyroid prediction. Amongst these various feature selection algorithms, serological tests and pathological observations of the thyroid disease were also surveyed. The forty-two machine learning algorithms are compared to find the top five best classifiers to predict whether a given patient is suffering from hypothyroidism, hyperthyroidism or is absolute normal. The data source has been taken from KEEL which contains 7200 instances with 22 attributes. The results of various machine learning algorithms have been compared in order to find most appropriate model for classifying whether a given patient is suffering from thyroid disease or not. The best classifier found as bagging gives an overall accuracy of 99.70 percent. Another experiment is also conducted to identify and remove outliers. With outliers' removal, a comparison of forty-two machine learning algorithms with the previous results has been performed. Here, a slight improvement in results is observed and this time the best classifier found, achieved an accuracy of 99.95 percent with logic and analysis of data (LAD) tree.
Fusion of linear base classifiers in geometric space

Ensembles of classifiers deserve attention because their stability and accuracy are usually superior compared to the single classifier. One of the aspects regarding the construction of multiple classifier systems is the fusion of each base model output. The state-of-the-art fusion of base classifiers approaches uses class labels, a rank array, or a score function to determine the classifier ensemble’s final decision. On the other hand, in this study, we use the base classifiers’ decision boundaries in the fusion process. Therefore the integration process occurs in a geometric space. In this paper, a new definition of the function that measures the central tendency has been proposed. This function allows integrating any number of linear base classifiers in the geometry space, removing the limit on the number of these classifiers in the ensemble. The limit on the number of base classifiers is noticeable in our earlier works. The proposal was compared with other fusion approaches to base classifiers outputs. The experiments on multiple binary datasets from UCI and KEEL datasets repositories demonstrate the effectiveness of our proposal of the fusion process in the geometric space. To discuss the results of our experiments, we use standard and imbalanced datasets separately.
Toward Stopping Incel Rebellion: Detecting Incels in Social Media Using Sentiment Analysis

Incels, which stand for involuntary celibates, refer to online community members who identify themselves as individuals that women are not attracted to them. They are usually involved in misogyny and hateful conversations on social networks, leading to several terrorist attacks in recent years, also known as incel rebellion. In order to stop terrorist acts like this, the first step is to detect incels members in social networks. To this end, user-generated data can give us insights. In previous attempts to identifying incels in social media, users' likes and fuzzy likes data were considered. However, another piece of information that can be helpful to identify such social network members is users' comments. In this study, for the first time, we have considered users' comments to identify incels in the social networks. Accordingly, an algorithm using sentiment analysis was proposed. Study results show that by implementing the proposed method on social media users' comments, incel members can be identified in social networks with an accuracy of 78.8%, which outperforms the previous work in this field by 10.05%.
A metaheuristic approach of hybrid bee colony and simulated annealing combined with fuzzy model: Prediction of conversion and selectivity in C8H16 dimerization

In the present study, the metaheuristic approaches based on hybrid bee colony (BC) and simulated annealing (SA) algorithms have been developed to improve the fuzzy model performance based on the application of the optimum membership function parameters in 1-octene dimerization process. For this purpose, simulated annealing is exerted through the scout bee search process. In addition, the parameters of the proposed optimization methods are properly tuned to significantly improve the established model. In the present study, the effects of temperature, Weight Hourly Space Velocity (WHSV) and Time On Stream (TOS) on the conversion and dimer selectivity have been studied based on the proposed hybrid BCSA fuzzy model. The performance of the established model is compared with that of three proposed models (i.e. fuzzy model, conventional BC and SA fuzzy model). The results significantly reveal that the proposed hybrid BCSA fuzzy model has more accurate prediction ability with R2 of over 0.91.
Modelling of power consumption for Advanced Encryption Standard and PRESENT ciphers

Abstract Paper presents a result of modelling by ELMO tool and measuring on hardware stand power consumption traces for encryption algorithms Advanced Encryption Standard and PRESENT. Power consumption traces for different version of AES (basic one-round AES algorithm are compared with AESMasked_R1 and MBedAES versions) and PRESENT (with various round numbers) are generated. Assessment of instructions with leaks were made by comparison power consumption traces for fixed-input and power consumption traces for random-inputs (total quantity of traces is 10 4 ). Numbers of total and potentially leakage instructions for each examined version of AES and PRESENT ciphers implementation are estimated.
Finite-Frequency $\mathcal {H}_{-}/\mathcal {H}_{\infty }$ Memory Fault Detection Filtering Design for Uncertain Takagi–Sugeno Fuzzy Affine Systems

This article is concerned with the finite-frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> memory fault detection filtering problem for discrete-time Takagi–Sugeno fuzzy affine systems with norm-bounded uncertainties. The objective is to design a piecewise affine memory filter by using system historical information such that the resulting closed-loop filtering error system is asymptotically stable with the prescribed finite-frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> performance. Based on the generalized Kalman–Yakubovič–Popov lemma combined with the celebrated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula> -procedure, new sufficient conditions for the fuzzy affine filtering error system to have the finite-frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> performance are given at first. By further using piecewise fuzzy quadratic Lyapunov functions and Projection lemma, the filtering analysis results for the filtering error system to be asymptotically stable with the prescribed finite-frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> performance are obtained. Then, the filtering synthesis is carried out with the aid of matrix inequality convexification techniques, and the synthesis results are described in terms of linear matrix inequalities. It is further shown that a better filtering performance can be achieved by using more system historical information. Finally, simulation is provided to verify the effectiveness of the proposed approach.
Multi-view Correlation based Black-box Adversarial Attack for 3D Object Detection

Deep neural networks have made tremendous progress in 3D object detection, which is an important task especially in autonomous driving scenarios. Benefited from the breakthroughs in deep learning and sensor technologies, 3D object detection methods based on different sensors, such as camera and LiDAR, have developed rapidly. Meanwhile, more and more researches notice that the abundant information contained in the multi-view data can be used to obtain more accurate understanding of the 3D surrounding environment. Therefore, many sensor-fusion 3D object detection methods have been proposed. As safety is critical in autonomous driving and the deep neural networks are known to be vulnerable to adversarial examples with visually imperceptible perturbations, it is significant to investigate adversarial attacks for 3D object detection. Recent works have shown that both image-based and LiDAR-based networks can be attacked by the adversarial examples while the attacks to the sensor-fusion models, which tend to be more robust, haven't been studied. To this end, we propose a simple multi-view correlation based adversarial attack method for the camera-LiDAR fusion 3D object detection models and focus on the black-box attack setting which is more practical in real-world systems. Specifically, we first design a generative network to generate image adversarial examples based on an auxiliary image semantic segmentation network. Then, we develop a cross-view perturbation projection method by exploiting the camera-LiDAR correlations to map each image adversarial example to the space of the point cloud data to form the point cloud adversarial examples in the LiDAR view. Extensive experiments on the KITTI dataset demonstrate the effectiveness of the proposed method.
SPeCiaL: Self-supervised Pretraining for Continual Learning

This paper presents SPeCiaL: a method for unsupervised pretraining of representations tailored for continual learning. Our approach devises a meta-learning objective that differentiates through a sequential learning process. Specifically, we train a linear model over the representations to match different augmented views of the same image together, each view presented sequentially. The linear model is then evaluated on both its ability to classify images it just saw, and also on images from previous iterations. This gives rise to representations that favor quick knowledge retention with minimal forgetting. We evaluate SPeCiaL in the Continual Few-Shot Learning setting, and show that it can match or outperform other supervised pretraining approaches.
Combined cause inference: Definition, model and performance

In recent years, many methods have been developed for discovering causal relationships from observed data. However, as an important kind of causes existing in many causal systems, combined causes (e.g. multi-factor causes consisting of two or more component variables that individually might not be a cause) have not received enough attention. The existing approach includes both individual and combined variables in the causal discovery process using constraint-based methods, can neither distinguish a set of Markov equivalence classes nor identify a combined cause containing one (or more) individual cause(s), therefore can output only some combined causes, instead of all combined causes. In this paper, we first subsume all possible combined causes into three types and give them formal definitions, then extend the additive noise model (ANM) to infer combined causes. We show that if a candidate variable set X w.r.t. a target Y satisfies: (1) allowing ANM for only the forward direction X→Y, and (2) no disturbance variable is contained in X, i.e., removing any component of X will weaken the causal relationship between X and Y, then X forms a combined cause. Based on this finding, we develop an efficient method to discover combined causes. Furthermore, we also conduct extensive experiments to validate the proposed method on both synthetic and real-world data sets.
Real Time Face Identification for Capturing the Class Attendance Using Convolutional Neural Networks

Facial recognition is emerging as the torrid research topic these days. The very big process for the lecturers in the schools and colleges is student attendance, which helps them to know who are present in the class. So, a system is designed to automatically mark the attendance of the students by decreasing the load on the staff members. In our campus there is a biometric process for the staff but for student's facial recognition is considered as the best solution than biometric as the count of students is more than teachers. Many projects have are developed for detecting the faces but in our project, there is one step more that is recognition of the detected faces. This is done with the help of convolutional neural network algorithm, which takes the input in the form of video or photos through secret camera that is fixed in the classrooms.
TC-MIMONet: A Learning-based Transceiver for MIMO Systems with Temporal Correlations

Data-driven approaches have recently emerged as promising remedies for communication system designs, which leverage deep learning techniques for automated development and optimization. In this paper, we revisit the designs of multi-input multi-output (MIMO) wireless systems and investigate the end-to-end learning for MIMO systems with temporal correlations. Our objective is to develop a MIMO transceiver to improve the communication performance by making fully use of the available temporal information. Although the end-to-end learning framework has been applied to various communication systems, existing designs largely rely on memoryless autoencoders (AEs) and overlook the time dependency. To overcome this issue, we propose a novel learning-based MIMO transceiver, namely, the TC-MIMONet, which extends the conventional memoryless AE-based transceivers by customizing two neural network components with memory. In particular, a long short-term memory (LSTM)-based CSI predictor is adopted at the transmitter, while a two-timescale LSTM-based decoder is developed for the receiver. Simulation results show that TC-MIMONet achieves significant block error rate reduction compared to two baseline schemes without utilizing the available temporal information.
Coin-Based Multi-party Fair Exchange

Multi-party fair exchange (MFE) considers scenarios where fairness means that either all exchanges as agreed upon between multiple parties take place, or no item changes hands. The two-party case was widely studied starting with the seminal work of Asokan et al. in ACM CCS 1998. The state-of-the-art MFE protocol was shown by Kılınç and Küpçü in CT-RSA 2015. Unfortunately, it only works on items that can be efficiently verifiably encrypted, which, in particular, means that it cannot efficiently handle exchange of large files in a peer-to-peer file sharing scenario. In this work, first, we extend the optimistic two-party fair computation definition of Cachin and Camenisch in CRYPTO 2000 for the MFE setting, and prove the security of our protocol with ideal-real simulation. Secondly, we extend the CT-RSA 2015 solution of Kılınç and Küpçü in a way that our protocol enables parties to exchange any item, be it a large file. While doing so, we employ electronic payments, where if a party does not obtain the desired item at the end of the protocol, the payment of the item's owner will be obtained instead. Third, we achieve asymptotic optimality with O(1) rounds and \( O(n^2) \) messages, where n is the number of participating parties. Finally, we also provide experimental results from our prototype code.