Reviewerly/exHarmony huggingface dataset:
Event Prediction Technology Based on Graph Neural Network

Abstract With the rapid development of the mobile Internet and the continuous expansion of network scale, the network security situation is becoming increasingly severe, and the endless network security threats have put forward higher requirements for network security performance. Based on the above background, the purpose of this paper is to explore the event prediction technology based on graph neural network. Due to the slow convergence of the network event prediction and evaluation model, the untimely risk assessment and inaccurate safety prediction caused by the incomplete parameter setting of the prediction model have become prominent problems in this field. This paper proposes an event prediction technology based on graph neural network. This method first uses genetic algorithms to optimize the weights in the training process of the graph neural network, which overcomes the blindness of initial weight selection and improves the training efficiency of the graph neural network; the KDDCup99 data set is used to conduct experiments on the above two methods respectively. Verification and analysis. The simulation and comparison experiments respectively verify that the neural network-based network security situation assessment and prediction method proposed in this paper can realize the assessment and prediction of the network situation more efficiently and accurately.
Profit Sharing for Data Producer and Intermediate Parties in Data Trading over Pervasive Edge Computing Environments

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We also introduce a data relay process that can enhance data accessibility in wireless edge networks. We formulate a revenue sharing problem to maximize the profit of both the data producer and resellers/relayers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers/relayers. Extensive simulations show that with resellers and relayers, our mechanism can achieve up to 49.5 percent higher profit for the data producer and resellers/relayers.
Kinematic and Statistical Analysis of Fold Related Fractures in the Northeast of Kopet Dagh Folded Belt, Northeast Iran

This study provides a description and statistical analysis of fold-related fractures in the Khur fold, northeast of Kopet Dagh fold-and-thrust belt, Iran. Kopet Dagh fold-and-thrust belt, as a part of Alpine-Himalaya orogeny belt in west Asia, forms the northeastern boundary of Iran plateau. In this study, the remote sensing technique was utilized to map and statistically analyze the lineaments on the satellite images. Extraction of fractures is based on the processing of satellite images and completed by field observations. Based on their directions, fractures can be categorized into four sets including an axial and a cross-axial set that trend parallel and perpendicular to the fold axial trace, respectively, and two oblique sets that trend at moderate angles to the axial trace. The mean trends of the principal compressional stress, independently calculated for the fold-related fractures systems, are close to N41° ± 5°E and are perpendicular to the general NW-SE trend of the Kopet Dagh fold-and-thrust belt. The cross-axial set is interpreted as the oldest set. The major orientation of lineaments is NE-SW. Also, the length distribution of fractures shows a right-skewed distribution. To extract the underlying scaling parameters, the dataset was analyzed using Maximum Likelihood Estimators (MLEs). It was found that a lognormal distribution is the best representative statistic for fracture lengths in the study area. The combination of information from the prepared fracture map, the analysis and measurement of the fractal dimension, and calculations of variables such as density and intensity revealed that the high probability of permeability along with fractures with an NW-SE trend.
Chatty Goose: A Python Framework for Conversational Search

Chatty Goose is an open-source Python conversational search framework that provides strong, reproducible reranking pipelines built on recent advances in neural models. The framework comprises extensible modular components that integrate with popular libraries such as Transformers by HuggingFace and ParlAI by Facebook. Our aim is to lower the barrier of entry for research in conversational search by providing reproducible baselines that researchers can build on top of. We provide an overview of the framework and demonstrate how to instantiate a new system from scratch. Chatty Goose incorporates improvements to components that we introduced in the TREC 2019 Conversational Assistance Track (CAsT), where our submission represented the top-performing system. Using our framework, a comparable run can be reproduced with just a few lines of code.
Fuzzy Cognitive Maps analysis of Green Supply Chain Management: a case study approach

The Green Supply Chain has proposed to innovate industrial production by implementing a radical perspective change: reconcile economy and ecology. This study aims to contribute to the realisation of a new idea of eco-sustainable industrialisation. Anyone making decisions within any dynamic system faces serious difficulties. For this reason, the proposed study analyses this system using Fuzzy Cognitive Maps arriving at the formation of a real map of the causal relationships between the concepts identified, then divided by areas of membership. In doing so, the most relevant factors affecting the Green Supply Chain decision-making process have been identified and analysed.
Extracting image frames using image property matching technique

Abstract Retrieve the needed content based on the users input query one of the challenging task today especially the input if it is image. This extraction procedure be different compare to the existing retrieve operation. In image data extraction user need to know the input field, based on his knowledge only the extraction take place because the complexity of the input data set. The given visual picture converted into meaningful static picture after eliminating the unwanted information from the data set then only user need to extract the needed information. This brings the huge demand in this field. In the proposed article focus the drawback on the existing technique and provide the efficient model for extracting the needed information. The investigation outcome verifies this statement.
Self-parameterized Swarm Intelligence Algorithms for Targets’ Detection in Complex and Unknown Environments

During the last decade, Genetic Algorithms have known an increasing interest, especially in Machine Learning and Deep Learning fields, used to accelerate the learning process of Artificial Neural Networks to solve a certain problem. Our contribution consists in using Genetic Algorithm to optimize the parameter tuning of the swarm intelligence-based algorithms. Then based on the best found parameters, we solve the target search problem to reach the best possible outcomes. This optimization has been tested on four different algorithms with numerous environments and the results are very promising.
Intelligent Chatbot Based on Emotional Model Including Newly-Coined Word and Emoticons

In this paper, we build a chatbot that identifies the user’s emotions, including newly-coined words and emoticons, for office workers. Chatbot categorizes emotions that appear in user input sentences into eight emotions. Because emotions are a very subjective area, interpretations of the same word can vary depending on the situation and person. In addition, the contextual characteristics of dialogue, not general sentences, can be taken into account to classify the correct emotions. To increase the accuracy of the emotional analysis, the subject category is determined and proceeded at the beginning of the conversation. User input sentences identify the intention of utterance through analysis of morphemes and sentence. To classify emotions of standard language by building emotional dictionaries based on the Korean language dictionary. For the emotional classification of newly-coined words and emoticons, use the newly-coined words and emoticons sentiment dictionaries. Use a model that shows high accuracy by comparing feature-based classification models with artificial neural network models. The emotional decision model anticipates the user’s emotional state by extracting mood values that represent the overall emotion of the conversation. Based on the expected user’s mood value, chatbot is personified by adding newly-coined words and emoticons to answers.
VideoLoc: Video-based Indoor Localization with Text Information

Indoor localization serves an important role in various scenarios such as navigation in shopping malls or hospitals. However, the existing technology is usually based on additional deployment and the signals suffer from strong environmental interference in the complex indoor environment. In this paper, we propose video-based indoor localization with text information (i.e. "VideoLoc") without the deployment of additional equipment. Videos taken by the phone carriers cover more critical information (e.g. logos in malls), while a single photo may fail to capture it. To reduce redundant information in the video, we propose key-frame selection based on deep learning model and clustering algorithm. Video frames are characterized with deep visual descriptors and the clustering algorithm efficiently clusters these descriptors into a set of non-overlapping snippets. We select keyframes from these non-overlapping snippets in terms of the cluster centroid that represents each snippet. Then, we propose text detection and recognition with the perspective transformation to make full use of stable and discriminative text information (e.g. logos or room numbers) in keyframes for localization. Finally, we obtain the location of the phone carrier via the triangulation algorithm. The experimental results show that VideoLoc achieves high precision of localization and is robust to dynamic environments.
Hybrid Video Watermarking Based on LWT, SVD and SWT Using Fused Images for Data Security

We propose a new scheme to protect IPR of owners and stop illegal modification and distribution of videos. We propose embedding of a fused watermark into videos to solve ownership disputes. The embedding watermark is an image that consists of two watermarks fused using SWT, so as to make the system more secure. Embedding and extraction are done using LWT and SVD. Experimental outcomes show that the extracted watermarks are stout against various noise attacks and are evaluated using correlation coefficient. The PSNR is also calculated to evaluate the perceptual video quality.
Predicting Psychological Pathologies from Electronic Medical Records

Recently, we have experienced a growth and a technological revolution in all areas such as industry, e-commerce, economics and health. To get more out of this digital revolution, we based our studies on statistics and in-depth research that we have carried out in the field of health in general and mental health in particular. In this context we have thought of improving the automation and personalization of the process of analysing electronic medical records in order to develop a decision support tool, for the benefit of psychologists and psychiatrists, so that we can predict in advance the most confronted psychological pathologies. To this end, we used the formalism of the languages previously proposed in this context, namely Machine Learning, Big data, the GATE development platform for Automatic Processing of Medical Language as well as the Medical Language Unification System “UMLS” for mapping and classification of pathologies and data from electronic medical records. To achieve these ends, we have developed the web platform “Medico-call”, which provides a service of psychological support and support. We then plan to add an additional boost by making a comparison between the prediction results psychological pathologies from consultations and clinical data as well as web data from our platform, this in the context of strengthening the performance as well as the quality of the tools and technologies described above.
Experimental Analysis on the Measurement of the Formation Time of Carbonless Copy Handwriting by “Fading” Gray Value Measurement Method

Abstract Carbon free carbon copy paper is a special writing material, which often appears in many commercial disputes and cases. The handwriting identification of carbon free carbon copy paper has often become one of the key factors in case adjudication. However, the existing handwriting inspection methods of carbon free carbon copy paper are not effective, and cannot analyze the law of the formation time of carbon free carbon copy handwriting. For this reason, this paper proposes an experimental analysis based on the “fading” gray value measurement method to determine the formation time of carbon free copy handwriting. The characteristics of solvents have an important impact on the formation of handwriting. Therefore, this paper analyzes the characteristics of the existing main pen solvents in terms of fading and copying ability. In this paper, according to the characteristics of carbonless copy characters, combined with the gray value measurement method, the calculation method is optimized. The optimized method is more suitable for the determination of the formation time of carbon free copy handwriting, and has the advantage of simplifying the calculation steps. In the specific test experiments, the relationship between extraction rate and time change, handwriting reproducibility and other items are tested. The experimental results show that the extraction rate of carbon free copy handwriting decreases rapidly with the increase of formation time, and gradually tends to be stable. In this paper, the characteristics of the solvents and colorants for pen and their influence on the formation of handwriting are analyzed. It is concluded that the types and use of solvents and colorants have an impact on the formation time of carbon free copy handwriting.
A Hybrid Approach to Design Automatic Spelling Corrector and Converter for Transliterated Bangla Words

In this paper, a spelling corrector and converter has been built for transliterated Bangla words. Transliterated Bangla users are increasing rapidly due to the convenience and general acceptability of this writing style in the internet platforms. For transliterated Bangla, there are no grammatical rules or obligations. Therefore, people generate deviated and ambiguous words. Moreover, people use personal and conventional writing styles in transliterated Bangla. Typographical errors and omission of characters especially vowels for making the words shorter make it more difficult to read transliterated Bangla. To find the solution, we propose a hybrid model that consists of three parts, error detection, error correction, and conversion of transliterated Bangla words. For error detection, we used a dictionary lookup approach and for correction, we used a combination of Damerau-Levenshtein minimum edit distance algorithm and linear search algorithm. Lastly, for conversion, we use the linear search algorithm. For finding out the suitable algorithm for error correction, we tried edit-based, token-based, and sequence-based approaches from text distance library. The model is then evaluated with a dataset comprised of words collected from various internet platforms. The performance of this model shows that it is possible to make an efficient spelling corrector and converter for transliterated Bangla.
Discrimination of human-written and human and machine written sentences using text consistency

The development of deep learning has made it possible to automatically generate sentences that could be misinterpreted as being written by humans in the field of natural language processing. As a result, the importance of the identity of the author of the text is beginning to be emphasized. In this paper, we propose a method to evaluate the consistency of sentences, which can distinguish between "sentences composed entirely of human-written texts" and "sentences with a mixture of human-written and machine-generated texts". In addition, we tested the consistency of the proposed method in an experiment, and confirmed that it was possible to discriminate two kinds of sentences in a mixed dataset of human written text and mixed text with higher accuracy than existing works. Furthermore, Kendall's rank correlation coefficient and Mann-Whitney's U-test in the sentence discrimination experiment confirmed that the proposed method showed a significant difference between the two types of sentences with a stronger correlation.
A Recognition Method for Radar Emitter Signals Based on Deep Belief Network and Ambiguity Function Matrix Singular Value Vectors

Aiming at the weak representation ability under low signal noise ratio (SNR) in radar emitter signals recognition based on ambiguity function main ridge or contour lines, etc. A recognition method based on Deep Belief Network (DBN) and ambiguity function matrix singular value vectors (SVV) is proposed. First, the appropriate window parameter of one-dimensional median filtering is selected to denoise the time domain signals and ambiguity function matrix singular value vectors datasets are created in different SNR. Then, a deep network based on DBN model is established to complete the training of labeled data. Finally, the network is used to achieve radar emitter signals recognition. The simulated experiments show that the average recognition accuracy rate of six kinds of complex modulated signals, i.e., BPSK, BFSK, FMCW, QPSK, MSEQ, and LFM-BC, by proposed method keeps above 99.17% in fixed SNR environment above -10dB with good generalization ability and strong robustness.
Extracting Latent Information from Datasets in CONSTRAINT 2021 Shared Task

This paper introduces the result of Team Grenzlinie’s experiment in CONSTRAINT 2021 shared task. This task has two subtasks. Subtask1 is the COVID-19 Fake News Detection task in English, a binary classification task. This paper chooses RoBERTa as the pre-trained model, and tries to build a graph from news datasets. Finally, our system achieves an accuracy of 98.64% and an F1-score of 98.64% on the test dataset. Subtask2 is a Hostile Post Detection task in Hindi, a multi-labels task. In this task, XLM-RoBERTa is chosen as the pre-trained model. The adapted threshold is adopted to solve the data unbalanced problem, and then Bi-LSTM, LEAM, LaSO approaches are adopted to obtain more abundant semantic information. The final approach achieves the accuracy of 74.11% and weight F1-score of 81.77% on the test dataset.
A Sum-of-Ratios Multi-Dimensional-Knapsack Decomposition for DNN Resource Scheduling

In recent years, to sustain the resource-intensive computational needs for training deep neural networks (DNNs), it is widely accepted that exploiting the parallelism in large-scale computing clusters is critical for the efficient deployments of DNN training jobs. However, existing resource schedulers for traditional computing clusters are not well suited for DNN training, which results in unsatisfactory job completion time performance. The limitations of these resource scheduling schemes motivate us to propose a new computing cluster resource scheduling framework that is able to leverage the special layered structure of DNN jobs and significantly improve their job completion times. Our contributions in this paper are three-fold: i) We develop a new resource scheduling analytical model by considering DNN's layered structure, which enables us to analytically formulate the resource scheduling optimization problem for DNN training in computing clusters; ii) Based on the proposed performance analytical model, we then develop an efficient resource scheduling algorithm based on the widely adopted parameter-server architecture using a sum-of-ratios multi-dimensional-knapsack decomposition (SMD) method to offer strong performance guarantee; iii) We conduct extensive numerical experiments to demonstrate the effectiveness of the proposed schedule algorithm and its superior performance over the state of the art.
Education of the Digital Generation: Relying on Traditions and Taking into Account Modern Trends (on the Example of Russia and Kazakhstan)

The article presents the results of the analysis of the formation of moral norms in older adolescents in Russia and Kazakhstan. The authors posed the following questions: do the modern system of education in the digital society of Russia and Kazakhstan have prerequisites based on the historical traditions of our countries? What are the current trends in the modernization of the system of education in vocational education, characteristic of the digital society? Are there any justifications for the feasibility of forming a system of education of modern professional personnel for the digital economy based on the traditional principles of the system of education of professional personnel in Russia and Kazakhstan? It is shown that in adolescence it is a moral act that characterizes the formation of moral consciousness. The actions of a teenager are determined during this period by the recognition of responsibility, and not by the expectation of approval, which was typical for an earlier age period. Many adolescents have a high situational variability, adherence to diametrically opposite positions. Here is the violation of principles, and intolerance to the opinions of other people, and the demonstration of passivity in the violation of the rights of others, and the justification of immoral actions by profit. The authors proceed from the understanding that now teachers are educating those young people who have a completely different perception of reality, their thinking, moral norms, a new culture, and ways of communication. At the same time, no one removes from the modern teacher the task of transmitting to the young the existing moral and ethical norms that originate in the previous period of education.
Expanding Space with Augmented Reality

Replacing real life with the virtual space has long ceased to be a theory. Among the whole variety of visualization, systems that allow projecting non-existent objects into real-world space are especially distinguished. Thus, augmented reality technology has found its application in many different fields. The article discusses the general concepts and principles of building augmented reality systems.