Reviewerly/exHarmony huggingface dataset:
“Help, I’ve Been Hacked!”: Insights from a Corpus of User-Reported Cyber Victimization Cases on Twitter

Understanding users is critical to develop secure IT systems and effective cybersecurity solutions. Unfortunately, research that analyzes user cybersecurity behaviors and experiences is limited. Motivated by work in public health that relies on Twitter user-reported health conditions, we explore using Twitter to understand user victimization experiences. Observing that users often self-report victimization (Help, I’ve been hacked!), we construct a corpus of 2,910 Tweets representing user reports of victimization experiences. We label these reports with information about the affected device or account and the associated consequences, as expressed by the users themselves. We begin to uncover trends in compromise in addition to startling attitudes and behaviors that security practitioners must contend with when developing cybersecurity solutions. To the best of our knowledge, this is one of the first papers to construct and analyze a dataset of cyber victimization user reports.
The Framework of Constructing University Comprehensive Evaluation System Based on Online Comments

Educational evaluation plays an important guiding role in education reform and development. Establishing a scientific and complete comprehensive evaluation system for colleges and universities is an important means to solve the “baton” problem of education evaluation in China at this stage. A large number of comments on university services published on the Internet, which implicated the potential demand of people and the evaluation of universities. Using big data to mine and analyze users' opinions and deep-seated needs is of great significance for universities to improve service quality and improve people's satisfaction with education. This paper proposes the idea of constructing a comprehensive evaluation system for universities based on knowledge graph and big data of online comments. First, through the web crawler technology to obtain online comments data from the online communities, forums and socialized Q&A websites and clean the data. Then, the text data is subjected to entity extraction, relationship extraction, and knowledge fusion to construct a domain knowledge graph of the universities. Finally, use the complex network analysis method to determine the comprehensive evaluation system of universities. This evaluation method is not only an important supplement to the existing university evaluation system, but also can provide new ideas for constructing scientific and objective educational evaluation by using new technology.
An Open-Source Speech Codec at 450 bit/s with Pseudo-Wideband Mode

Most modern digital speech codecs are proprietary and do not give insights into their implementations. “Codec 2” is an open-source codec and opens up the possibility of application-tailored adaptations. In this publication a new 450 bit/ s mode is introduced. It includes a pseudo-wideband mode that can be optionally enabled at the receiver without resulting in a higher bit rate. By sorting the codebook vectors a bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> is still intelligible. An encoded audio transmission was tested with two low-power Sub-GHz transceivers in the 70 cm amateur radio band achieving a sensitivity of -130 dBm. For proofing the concept, a field test was conducted and compared to a propagation simulation.
Segmentation of Irrelevant Regions Using Color Thresholding Method: Application in Breast Histopathology Images

Abstract Segmentation of irrelevant regions in the breast histopathology image is essentially performed in preliminary or post processing stages. This study presents a color thresholding method to segment the irrelevant regions, specifically blood cells and hemorrhage in the RGB breast histopathology images. The conventional gray level global thresholding method was extended to color images by modifying the thresholding algorithm. The color thresholding method was performed by decomposing the RGB input image into coherent regions based on similarity in color feature. Area Overlap Measure ( AOM ) and Combined Equal Importance ( CEI ) are two statistical metrics used to evaluate the performance of the color thresholding method. The obtained AOM and CEI for the overall dataset in blood cells are 0.92 and 0.89, respectively. The color thresholding method is found to be effective and is able to produce plausible thresholding results in segmentation of irrelevant regions in RGB breast histopathology images.
An Ensemble Model for Error Modeling with Pseudoinverse Learning Algorithm

In Bayesian theory, the maximum posterior estimator uses prior information to estimate the noise in the machine learning model by adding the regularization term. The regularization terms L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> correspond to Laplacian prior and Guassian prior, respectively. In existing deep learning models, in order to use the gradient descent optimization algorithm and achieve good results, most models take L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization as the regularization term of the network model to fit the complex Guassian noise. However in practice, the Laplace noise and the Guassian noise are both considered as data noise. For multi-layer perceptrons, the difficulty caused by adding L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> into the optimization function of the network is solved by proposing an ensemble model for error modeling through adopting the divide and conquer strategy. First, several base learners are trained to fit different noise distributions of data, then the final results can be obtained by taking the results of each base leaner as new data to train a meta leaner, and get the final results. Among them, coordinate regression method is used to solve L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> loss, while the pseudo-inverse learning algorithm is employed to solve L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> loss. Both methods are nongradient optimization algorithms. The comparison results of the model on several data sets show that the proposed ensemble model achieves better performance.
Research and design of education and teaching resource management system based on ASP.NET Technology

Abstract The informatization of career education is an important foundation and part of the reform of career education. The construction of professional education resource library is one of the important contents of professional education informatization construction. There is a lack of unified planning for the construction of teaching resource pool in higher vocational colleges in China. The system software construction investment is less, the content is not system; Teacher education information technology and teaching strategy application level is not high, slow development and other problems. This topic elaborates how to use the base. NET architecture, and SQL Server as the background database system, the development of teaching resource library platform design and implementation process. Based on the actual situation of zaozhuang vocational college of science and technology, this paper makes a comprehensive analysis of the demand of teaching resource management system. Establish a set of teaching resource management system for education teaching in our college, and the informatization level of education teaching. The research content of this paper is as follows: analyze the situation and existing problems of the construction of education teaching resource library in China. Research related technologies required for system development, including Ajax technologies. NET technology, ADO. NET technology. The demand analysis of the system, the realization of the structural model and functional module are described in detail. The system is used for testing in the department of information engineering of the college. Its functions are normal, and it basically meets the needs of normal teaching and course construction.
Methodology of Complex Objects Structural Dynamics Proactive Management and Control Theory and Its Application

Methodological and methodical fundamentals of the complex objects (CO) proactive management and control theory based on the fundamental results obtained in the interdisciplinary field of system knowledge are proposed. The paper provides information on the developed innovative multiple-model complexes, combined methods, algorithms and techniques for solving various classes of problems of operational, structural and functional synthesis and management of the development of the regarded classes of CO. The tasks of controlling the structural dynamics of CO belong to the structural and functional synthesis class of problems and the formation of appropriate programs for managing and control of their development. The main difficulty and a special feature of the solution of the regarded problems is as follows. Determination of optimal control programs for the basic elements and subsystems of CO can be performed only after all functions and algorithms of information processing and control that should be implemented in these elements and subsystems are known. In its turn, the distribution of functions and algorithms by the elements and subsystems of CO depends on the structure and parameters of the control laws of these elements and subsystems. The difficulty of resolving this controversial situation is ex-acerbated by the fact that under the influence of various reasons, the composition and structure of the CO at different stages of their lifecycle changes over time. The given examples of solving practical problems for such subject areas as spacecrafts, logistics, and industrial production.
Research on rapid mapping technology in the field of unmanned aerial vehicle (UAV) aerial survey

Through the development of computer image processing technology and UAV technology, after installing high-definition cameras on UAVs and taking high-resolution images, the technology of directly generating DOM images in the surveyed areas using software has been well-developed. However, under the emergency conditions such as dealing with disasters, the long-term processing time required for aerial surveys of UAVs has become a major efficiency constraint to surveying and mapping. This paper focuses on the rapid mapping and mosaicking of survey images from unmanned aerial vehicles under emergency conditions, and elaborates current research status, difficulties and resolvents of the rapid mapping of existing UAVs.
PGL(2,Fq) acting on Fq(x)

Let Fq(x) be the field of rational functions over Fq and treat PGL(2,Fq) as the group of degree one rational functions in Fq(x) equipped with composition. PGL(2,Fq) acts on Fq(x) from the right through composition. The Galois correspondence and Lüroth's theorem imply that every subgroup H of PGL(2,Fq) is the stabilizer of some rational function πH(x)∈Fq(x) with deg πH=|H| under this action, where πH(x) is uniquely determined by H up to a left composition by an element of PGL(2,Fq). In this article, we determine the rational function πH(x) explicitly for every H<PGL(2,Fq).
Poster Abstract: A Novel and Efficient Approach to Evaluate Biometric Features for User Identification

Classifications based on biometric features are widely used in modern healthcare applications, including user identification, authentication, and tracking. The complexity and accuracy of classification algorithms largely depend on the size and the quality of the feature set used to build classifiers. Feature evaluation and selection are critical steps to decide a small set of high-quality features to build accurate and efficient classifiers. This paper proposes a novel and efficient approach to evaluate and select biometric features for user identification applications based on activity sensor data collected from the user's wrists. For each feature, we first generate an NRMSD matrix, each entry of which represents the similarity level of any two users. Then, we define a heuristic, the Farness value to evaluate the quality of the feature based on the NRMSD matrix of the feature. Finally, we select a set of high-quality features whose Farness value is higher than 0.10.
ARPANET 50th Anniversary Marked at 2019 AAAS Annual Meeting

Reports on the celebration of the ARPANET 50th Anniversary that was recognized at the AAAS Annual Meeting.
Hermes, A Proposed Lightweight Block Cipher Used for Limited Resource Devices

The rapid development of the Internet of Things has allowed millions of devices to connect to the Internet and their number is growing exponentially. Most of them will be limited resources devices, like microcontrollers, sensors and RFID. These IoT devices are vulnerable to different kind of attacks so, to counteract the potential threats are needed some techniques that mitigate the danger are needed.In this paper we propose an algorithm to supply security for these cases in which severe implementation constraints are imposed. The algorithm is intended for applications that have stringent requirements on code size, energy consumption, and speed. The importance of these block algorithms that encrypts small data blocks compared to classical ones such as AES, is proven by NIST’s competition that aims to standardize an lightweight block algorithm for several emerging areas (e.g. sensor networks, healthcare, distributed control systems, the Internet of Things, cyber physical systems).
Knowledge Graph Construction for Intelligent Analysis of Social Networking User Opinion

Microblogging is a popular social networking tool on which people tend to express their views and opinions. As such, the massive data on microblogging platforms mean abundant research value to social science researchers. To help them better analyze these data, a framework for understanding diverse user opinions and identifying complex relationships in the form of knowledge graphs is proposed in this paper. The two main tasks in the framework are sentiment analysis and knowledge graph construction. In the first task, the Skip-gram model is employed to obtain the word embedding matrix and the Bi-LSTM model is adopted to perform stance classification. It is found in this paper that Bi-LSTM showed better performance in classifying different sentiments, compared with Naive Bayes and SnowNLP. In the second task, relations between different users are extracted from their micro-blogs through recognizing specific strings, and on this basis user attitudes are integrated into the knowledge extracted. A knowledge graph of user opinions is constructed with the Neo4J tool. With the knowledge extracted by this framework, social science researchers can more easily observe rules of perspective communication and perform further analysis of the data.
Multi-parameters Model Selection for Network Inference

Network inference is the reverse-engineering problem of inferring graphs from data. With the always increasing availability of data, methods based on probability assumptions that infer multiple intertwined networks have been proposed in literature. These methods, while being extremely flexible, have the major drawback of presenting a high number of hyper-parameters that need to be tuned. The tuning of hyper-parameters, in unsupervised settings, can be performed through criteria based on likelihood or stability. Likelihood-based scores can be easily generalised to the multi hyper-parameters setting, but their computation is feasible only under certain probability assumptions. Differently, stability-based methods are of general application and, on single hyper-parameter, they have been proved to outperform likelihood-based scores. In this work we present a multi-parameters extension to stability-based methods that can be easily applied on complex models. We extensively compared this extension with likelihood-based scores on synthetic Gaussian data. Experiments show that our extension provides a better estimate of models of increasing complexity providing a valuable alternative of existing likelihood-based model selection methods.
Attention-Based Deep Neural Network and Its Application to Scene Text Recognition

Recognize text in natural scenes is a challenging task. We proposed an attention-based deep neural network architecture for scene text recognition, which integrates feature extraction, feature attention, feature labeling and transcription into a unified framework. The primary advantages of the proposed model are: (1) it is an end-to-end model, does not require any segmentation of the input image. Convolutional neural network (CNN) is used as encoder to extract features, recurrent neural network (RNN) is used as decoder based on its characteristics of predict sequence, which composed a encoder-decoder architecture; (2) Soft Attention mechanism is introduced in, to further extract features in the input image, and allowing for end-to-end training within a standard back propagation framework; (3) Experiments are performed on several challenging scene text datasets, including IIIT5K, Street View Text, ICDAR2003 and ICDAR2013. Results of the experiments show that the proposed model is comparable or better than other models, which demonstrate the superiority of the proposed algorithm.
A Novel Method for Night-Time Single Image Dehazing

Images acquired under deprived weather environment are frequently corrupted due to the presence of haze, mist, fog or other aerosols in a form of noise.Haze elimination is essential in computer vision and computational photography applications.Generally, there is the existence of numerous approaches towards haze removal which are mostly meant for hazy images under daytime environments.Although the potency of these proposed approaches has been comprehensively established on daylight hazy images.However these procedures inherit significant limitations on images influenced by night-time hazy environments.Since night time haze removal dehazing remains an ill-posed problem, we proposed a novel method for night-time single image dehazing which is efficient under night-time environments.The proposed scheme is a dark channel-based local image dehazing procedure that locally estimates the atmospheric intensity for each selected mask on a corrupted image independently and not the entire image.This is done in order to overcome the challenge of night-scenes that are exposed to multiple/artificial lights source and spatially non-uniform environmental illumination.We performed an adaptive filtering on the combined dehazed masks to improve the degraded image.We validated the supremacy of the proposed approach in terms of speed and robustness through computer-based experiments.Conclusively, we displayed comparison results with state-of-the-art and extensively emphasized the comparative advantage of our scheme.
Vessel Segmentation of Liver CT Images by Hessian-Based Enhancement

The structure and morphology of hepatic vessels play an important role in the discovery and diagnosis of liver lesions. In this paper, a vascular segmentation method for liver ct image based on Hessian enhancement algorithm is proposed, and the hepatic vessels in CT image are segmented and extracted more accurately. Firstly, the improved Hessian filtering algorithm is used to enhance the structure of vascular branch. The other two classical algorithms are compared in the aspects of algorithm running efficiency, signal-to-noise ratio and image quality, and it is proved that the improved Hessian filtering algorithm in scripture is preprocessed. High image quality. Then the level set method based on threshold velocity function is used to remove the non-vascular region with low gray value by adding threshold constraints on the basis of the velocity function of the general level set method, which can effectively reduce the phenomenon of over-segmentation. The experimental results show that compared with the other three classical algorithms, the proposed method has better results in segmentation accuracy, over-segmentation rate, under-segmentation rate, sensitivity and specificity.
Sentiment Analysis of Financial News Using Unsupervised and Supervised Approach

Sentiment Analysis aims to extract sentiments from a piece of text. In addition to numeric data, sentiments are being increasingly favored as inputs to decision making process. However extracting meaning automatically from unstructured textual inputs involves a lot of complexities. These often depend on the domain from which the text was taken. Our work focuses particularly on extracting sentiments from financial news. We have proposed and implemented a framework using unsupervised and supervised techniques. We have proposed a hybrid approach of using seed sets for calculating the semantic orientation of news articles in a semi-automatic way. This approach produces better results than the standard techniques used in unsupervised sentiment analysis. Then we performed the experiment using the supervised approach with machine learning classifier Support Vector Machine (SVM). We compared the results with those produced from the standard unigram and unigram + bigram approaches and found that the proposed approach produces better precision.
Automatic Extraction of Product Information from Multiple e-Commerce Web Sites

With the growth of e-commerce, shopping online has now become a part and parcel of every one’s life. The advantage of e-commerce Web sites is that they can reach to a very large number of customers despite of distance and time limitations. The main aim of this paper is to extract the product information from various e-commerce sites. Extraction of such information can help the business organizations to fetch and attract the large number of customers to their Web site and increase profit. So, in this paper, we propose a fully automatic method which will extract and integrate information from multiple e-commerce Web sites in order to improve business decision making. The proposed method is also comparatively better at precision and recall than other methods.
WaveRange: wavelet-based data compression for three-dimensional numerical simulations on regular grids

A wavelet-based method for compression of three-dimensional simulation data is presented and its software framework is described. It uses wavelet decomposition and subsequent range coding with quantization suitable for floating-point data. The effectiveness of this method is demonstrated by applying it to example numerical tests, ranging from idealized configurations to realistic global-scale simulations.
Elementary nets (carpets) over a discrete valuation ring

Elementary net (carpet) σ = ( σij) is called closed (admissible) if the elementary net (carpet) group E(σ ) does not contain a new elementary transvections. The work is related to the question of V. M. Levchuk 15.46 from the Kourovka notebook( closedness (admissibility) of the elementary net (carpet)over a field). Let R be a discrete valuation ring, K be the field of fractions of R, σ = (σ ij) be an elementary net of order n over R, ω = (ωij) be a derivative net for , and ωij is ideals of the ring R. It is proved that if K is a field of odd characteristic, then for the closedness (admissibility) of the net , the closedness (admissibility) of each pair (σ ij ; σ ji) is sufficient for all i ̸= j.