Reviewerly/exHarmony huggingface dataset:
Reinforcing Digital Forensics Through Intelligent Behavioural Evidence Analysis: Social Media Hate Speech Profiling

Cumulative boom in the use of social media for crime incidents creates a need to achieve improvement in overall efficiency by developing tools and techniques. The present manuscript discusses this issue by proposing a model which offer a feasible solution to escalate the criminal investigation by performing digital criminal profiling using social media hate speeches content. The proposed expert system will consider the hate speech content analysis along with other digital footprints of the suspects. The achieved analysis along with the proofs collected manually will be fed into the knowledge base of expert system. The system will automatically process the dataset to lower down the list of suspects using intelligent machine learning algorithms. Hate speech content analysis achieved using latest intelligent mechanisms, can perform in the boundaries of Behavioural Evidence Analysis-Standardised (BEA-S) model to create criminal profiling which again act as a base for future investigations. The whole concept and the model are discussed in the paper which surely will upgrade the current investigation process to an innovative stature of digital forensics.
Research on Intelligent Evaluation of English Diagnosis System Based on Fuzzy K-means Clustering

With the rapid popularization of computers and the development of Internet technology, it is possible for computer-assisted instruction to play an important role in English teaching. At present, English teaching in middle schools is still in the traditional teaching mode, lacking intelligent and information-based auxiliary means. Therefore, how to make full use of the existing computer and network resources to serve the teaching has become a difficult problem for teachers and students. Aiming at the problem of inaccurate classification of big data information in traditional intelligent evaluation algorithm of English diagnosis system, an intelligent evaluation algorithm of English diagnosis system based on big data fuzzy K-means clustering and information fusion is proposed. Firstly, the hierarchical method is used to cluster the data initially, and then the number of clusters is used as the K value in fuzzy K-means clustering to correct the clustering. The algorithm can analyze the teaching quality, at the same time, it can promote English teaching reform and quality education, and realize the real burden reduction for teachers and students.
Multi-domain battlefield threat assessment based on D-S evidence theory and conditional probability table

With the development of information technology, the scope of battlefield is more and more extensive, and multi-domain Defensive operations are getting more and more attention. In order to improve the accuracy of multi-domain battlefield threat assessment, so as to deploy troops scientifically for effective defense, the probabilities of attacks occurring in each regional battlefield are calculated by D-S evidence theory, and then the probabilities of attacks succeeding are calculated by using conditional probability table when attacks are occurring in corresponding area. The probabilities of attacks succeeding are used to assess regional battlefield threat. On the basis of regional battlefield threat, the entire battlefield threat is calculated by combining regional weight. Finally, the results of case show that the proposed method is in line with the actual situation and the assessment results are accurate and effective.
Evaluation Method of English Listening Distance Teaching Based on ID3 Algorithm

In the evaluation of English listening distance teaching effect, the traditional evaluation method has serious disturbance under the pressure of data, and its ability of resisting pressure needs to be improved. Aiming at this problem, this paper puts forward an evaluation method of English listening distance teaching based on ID3 algorithm. According to the different stages of distance teaching of English listening, the evaluation index system of teaching effect is constructed, and the consistency ratio is calculated. When the consistency condition is met, the ID3 algorithm is used to calculate the information entropy of the evaluation index, construct the decision tree, determine the evaluation rules, and obtain the comprehensive evaluation result of teaching effect through multi-layer compound calculation. Experimental results show that the proposed evaluation method based on ID3 algorithm can improve the ability of resisting pressure under the pressure of data, and the disturbance rate is small.
Efficient Instance Segmentation Method For High-Resolution SAR Imagery

Benefiting from the characteristic of synthetic aperture radar (SAR), ship detection plays a crucial role in military purposes recently. Meanwhile, the methods developed by convolutional neural networks are superior in precision, detection speed, and migration ability to traditional methods. However, bounding boxes used in ship detection merely perform instance-level detection which comprises ship and background in the detected result. In terms of this defect, we introduce instance segmentation in SAR imagery for extracting the contour of the ships. Compared to ship detection with bounding boxes, instance segmentation implements pixel-level prediction for the ships, and the predicted mask eliminates background information, which provides the contour and azimuth of the ships. Furthermore, we proposed DB loss and high-resolution representations for instance segmentation. Quantitative and visualized results demonstrate proposed method improves average precision (AP) and reduces the model size compared to Mask R-CNN. Besides, it can accurately segment the hard samples, e.g., offshore ships, small ships, and ships in the port, which has a broad application vision in interpreting SAR images.
Trajectory Optimization Using Learned Robot-Terrain Interaction Model in Exploration of Large Subterranean Environments

We consider the task of active exploration of large subterranean environments with a ground mobile robot. Our goal is to autonomously explore a large unknown area and to obtain an accurate coverage and localization of objects of interest (artifacts). The exploration is constrained by the restricted operation time in rescue scenarios, as well as a hard rough terrain. To this end, we introduce a novel optimization strategy that respects these constraints by maximizing the environment coverage by onboard sensors while producing feasible trajectories with the help of a learned robot-terrain interaction model. The approach is evaluated in diverse subterranean simulated environments, showing the viability of traversability-aware exploration in challenging scenarios. In addition, we demonstrate that the local trajectory optimization improves global coverage of an environment as well as the overall object detection results.
Data Integrity Verification of Accounting Informatization Cloud Based on Stata in Sharing Mode

In order to promote the internal optimization and sustainable development of enterprises, a new innovative management mode, financial sharing mode, has gradually taken shape. To solve the problem of accounting information security under the financial sharing mode, this paper proposes an accounting information cloud data integrity verification algorithm based on Stata. Before verification, the third-party agent needs to send the evidence to the re-signing server for resigning, and then the third-party agent verifies the re-signed evidence with its own private key, and notifies the user when the verification fails. The biggest feature of this scheme is that it can strengthen the control of the third-party auditors, thus improving the data holder’s ability of self-verification, and at the same time, it can reduce the calculation time of the data holder. The analysis shows that the new scheme can provide the security requirements of robustness and privacy, and can provide the same communication and computing overhead as the schemes proposed in other literatures.
Failure Safety Analysis of Artificial Intelligence Systems for Smart/Autonomous Vehicles

Up to now failures in artificial intelligence systems, specifically machine learning algorithms which are their software components, are considered as systematic failures. The goal of this paper is to introduce a new concept of quantitative failure analysis for machine learning algorithms which can be used in smart/autonomous vehicles to guarantee sufficiently low risk of residual errors in this application. Firstly, a coincidence in evaluating impacts of unpredictable behaviours of machine learning algorithms and hardware components is introduced in order to statistically estimate failure rate based on a given number of data points. Next, a metric utilising this randomic failure rate is proposed to assess functional safety of smart and/or autonomous vehicles and evaluate their safeness according to ISO 26262:2018, and ISO/PAS 21448.
Development of Computer Skills to Draw in the LibreCad from Virtual Learning Environments

The theoretical foundations of the III Improvement of Basic Secondary Education in Cuba and the modifications of the Labor Education subject from it, demands to consider the use of computer tools for technical drawing. The specialized scientific literature highlights the need to promote learning from the mastery of computer skills with computer-aided drawing (CAD) applications. A descriptive exploratory study and theoretical systematization of the state of the art related to the subject under study was carried out, which allowed to establish the regularities on the internal structure and functional invariants of the computer skills to draw in LibreCad. The use of the Delphy method was used to obtain the opinions and criteria of 12 experts, who theoretically validated the proposal to use a system of didactic activities located in the Virtual Learning Environment (VLE) for the development of the skills under study, and thereby perfecting the proposed result before implementing it in educational practice. The study contributed to an approach to the theoretical-methodological foundations that support the internal structure of the computing skills to draw in the LibreCad tool with its system of functional invariants and proposes the development of the computing skills to draw in LibreCad in the students of the subject Labor Education in Basic Secondary Education in Cuba from the Virtual Learning Environment (VLE), which constitutes an ideal setting for the development of computer skills, making it possible to be a reference in other educational contexts.
Dynamic and Efficient Access Control Based On Function Encryption

In terms of access control, no matter it is implemented as MAC (mandatory access control), RBAC (role-based access control), or ABE (traditional attribute-based encryption), a query table is inevitable which was proved not efficient enough to achieve real-time ownership certification. Moreover, security problems always accompany it such as authority management, user privacy, or table structure. Thereafter, we proposed a secure data access control strategy resorting to function encryption, exploiting inner product to actualize the proof of ownership. The most significant contribution of our scheme is to accomplish authentication and access control simultaneously employing computation instead of table query. Besides, the access control strategy can be more fine-grained with dynamic access rights, one-to-many mapping between the file and downloading rights, and also the functionality of downloading right transfer by dint of Rodrigues rotation transformation. Both the algorithm underpinning our scheme and the analysis of the correctness/sematic security will be completed depicted in the context. Experiment results illustrated that our strategy effectively improved the efficiency of ownership certification and reduced the overheads of both storage as well as communication for flexible access control.
Research on the Effective Transformation Path of Chinese IT Enterprises in the Internet + Era

The growth rate of information flow is very fast, and it is not changed by people's will. When the flow is increasing, it is mainly to see who has the ability to solve and dredge these flows. No enterprise can guarantee that it will not be surpassed by other enterprises in the future. In the Internet + era, China's IT enterprises should try their best to keep their employees vigour and maintain the ability to compete with new companies. This article mainly discusses the effective transformation path of China's IT enterprises in Internet + era to keep pace with the times and forge ahead.
Finding A Baseline Machine Learning Model in Sci-Kit Learn Package in Terms of Accuracy and Efficiency

In this research work we have established a baseline machine learning model using the Sci-Kit learn package, that can be used for classification, and would result into optimum accuracy and efficiency, i.e., prediction time. From the time when different models were found in machine learning, the major question was, which is the best suited model. In the modern times, with rise of demand of this field and with introduction of more and more complex datasets, this question remains an important one. The models which we have taken into consideration in this research for comparison are all in terms of classification. The datasets on which the models have been implemented belong to different fields, namely, banking sector, wine recognition, medical sector, environmental sector and occupancy detection. All of these datasets are chosen in such a way that they have different size of attributes, patterns and classes. We have generalized the baseline model selection by experimenting with various classification models and implementing them over a variety of datasets. This research work can be upscaled by considering more models and comparing with the existing ones or taking more datasets for implementation or by doing both.
VQ Based Comparative Analysis of MFCC and LPC Speaker Recognition System

Language is the foremost part of communication and speech is the main component of language. Speaker recognizers extract parameters from speech and characterize the speech signal for recognition. Speaker recognition is a significant part of digital signal processing but the speaker recognition system is greatly affected by noise. Noise degrades the performance of the system. With the rapid advancement of speech processing technology, the speaker recognition system has also improved to a great extent. Different feature extraction techniques have been proposed. This paper investigates the performance of the Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive coefficients (LPC) in speaker recognition using Vector Quantization (VQ) and proposed some optimization techniques for the best recognition system.
Detection of Phishing Websites through Computational Intelligence

Phishing is a technique used by hackers to fool internet users reveal their sensitive information like passwords, credit card numbers, contact information, and address, etc. Web phishing is carried out mostly by sending fake web links to the users through different communication means like Email, Facebook Messenger and WhatsApp, etc. Web phishing detection is significant for making internet browsing safe and secure for users. Different approaches were applied for the detection of fake websites. However, the most efficient method for detecting phishing websites is the one that is based on artificial intelligence and learning mechanism. In this research, an efficient and accurate method is proposed for the detection of phishing websites which is based on computational intelligence. Through the development of different computational models and rigorous testing, it was revealed that Extreme Gradient Boost (XGBoost) based model achieved the maximum scores in all the validation tests. This shows that the model is robust and accurate in terms of web-phishing detection.
Study of Deep Learning in Video Object Track

Video object tracking is widely used in video surveillance, robot, and so on. The era of big data provides a new opportunity for the research of video object tracking. The rapid development of deep learning methods is introduced with stacking self-encoders, convolutional neural networks, and other recent specific applications in video object tracking. We carried out an in-depth analysis and summary, too. Finally, the future application and development direction of deep learning methods in video object tracking are forecasted.
A Deep Learning Framework for Distributed Channel Selection in a Congested Uncooperative Spectrum

In this paper, a deep learning framework for distributed interference assessment and channel selection in a congested, uncooperative spectrum is proposed. We present a deep learning based spectrum sensing model that is trained to estimate channel quality. Such training is conducted offline in a supervised manner using labeled channel energy captures and their associated packet error rate measurements. Packet error rate estimates are derived from numerical simulations of the system model. These estimates can be used to take action on network channel selection. Distributed application of our model is efficiently realized through online training at the level of edge devices and then the trained local models are periodically aggregated using federated learning. The model views packet error rate assessment as a classification problem, where the packet error rate is assigned a class based on a set of classes with upper and lower bounds. The trained models are tested against two metrics: classification accuracy and the mean ratio of error rate in the optimal operating channel to that in a channel selected based on classification inference. In the simulation scenarios, the model when trained in a centralized manner has a classification accuracy of 91.9&#x0025; and a mean selection error rate ratio 91.3&#x0025;, averaged over all scenarios. When trained in a distributed manner, the model has a classification accuracy of 83.3&#x0025; and a mean selection error rate ratio of 88.6&#x0025;. The distributed approach is estimated to reduce the communications overhead by two orders of magnitude to approximately 34 MB per edge device.
Denoising and Restoring of Infrared Image of Power Equipment Based on l2-relaxed l0 Sparse Analysis Priors

Background: The infrared image of electrical equipment often contains snow or is blurred, which makes it difficult to detect and analyze its state. Methods: A prior infrared image denoising and restoring method based on L2-relaxation L0 analysis is proposed. Through the prior image estimation, the problem of image de-blurring and denoising is transformed into the problem of solving the maximum entropy of a posteriori probability, and then the parameters are jointly optimized to widely degrade the image, so that the image is locally sparse from the strip and edge to the linear predictable texture, and the target object to be extracted is obtained by using the alternative iterative solution, to achieve the purpose of denoising and restoring of the original fuzzy infrared image with noise. Two kinds of infrared images with different brightness levels in a 220kV booster station are used for the experiments. Results: Compared with BM3D, TwIST, TVL1C, TVL2C, the experimental results show that the denoising and restoration effect of the proposed method is clearly better than the four methods. The PSNR, ISNR, and SSIM of the proposed method are greater than the others, and the calculation time is shorter. Conclusion: This method can not only enhance the sparsity of the infrared image target and improve the estimation accuracy, but also has the advantages of minimum image distortion, fast convergence speed, and preserving the target detail edge. This method can provide a new idea for other types of infrared image denoising and restoration.
Comparative Study of Extractive Text Summarization Techniques

Text Summarization is the process of generating a concise and meaningful summary of a text. To better help identify relevant information and consume relevant information faster, automatic text summarizing methods are needed to address the growing amount of text data available online. Text Summarization techniques are classified into abstractive and extractive summarization. The extractive summarization technique focuses on important information like sentences or phrases which are extracted from a given text file or original document and stack them together to create a summary. In this study, we review and compare the performance of three extraction-based summarization techniques which are Conceptual method, Text Rank and Sentence Scoring. Furthermore, we evaluate the quality of summarization by comparing individual methods on unsummarized text with their corresponding human made gold standard summaries.
The Image of the Social Reality of Women’s Lives in Love Songs

Love songs in Slovakia have always been among the most popular and remain part of the live vocal repertoire even today. In love songs, we can distinguish several themes that accentuate the various phases of love from first love, to dating, to fulfilled or disappointed love. In this type of song, where the central theme is love, other motifs are also expressed that testify to the patriarchal society and its system of values, preferences, and the power of the authorities that influenced the life of the partner and the choice of the spouse. Songs of this type do not avoid the negative elements either — they include themes such as the struggle of marriage or alcoholism for men, whereas for women the most common themes are infidelity or the loss of virginity. Folk songs not only describe these situations in emotional, characteristic ways, but also take an attitude towards them that is consistent with the attitudes and laws of the society from which they originate. Therefore, we feel in them not only happiness and unhappiness, but also resignation and reconciliation with the situation, which is set up and supported by the system. The traditional patriarchal society in Slovakia, in which the songs originated and survived, has left its mark on love songs in an unmistakable way. Although indirect naming, euphemism, and aestheticization of negative phenomena are often used in accordance with the poetics of a love song, the informative value of the songs is obvious. While the understanding of the content of the lyrics of existing songs cannot be left unnoticed, the fact that songs about conjugal love in the Slovak environment are only recorded to a minimal extent is also significant. The content analysis of the songs, taking into account the wider social and historical context, provides insight not only about the reality of life, but also about the emotional attitude of the people who lived in the system that determined their fundamental life decisions.