Reviewerly/exHarmony huggingface dataset:
Cuckoo Search Augmented MapReduce for Predictive Scheduling With Big Stream Data

Handling an information stream is a basic report for streaming application. There were numerous strategies which help during Bigdata streaming, however it can't deal with the tremendous information. To advance the productivity with least time intricacy, a Cuckoo Search Augmented Map Reduce for Predictive Scheduling (CSA-MRPS) system is presented. This technique incorporates cycles in preprocessing and prescient booking for stream information examination. In preprocessing, nonstop information streams are discretized utilizing Khiops and it begins from the constant time spans, consolidates the closest time as indicated by the Chi-square worth with lesser time intricacy. MapReduce work is applied to discretized information for prescient investigation utilizing Multi-Objective Ranked Cuckoo Search Optimization (MRCSA). It characterize the target capacities for the handling units, for example, CPU time, memory utilization, transfer speed use and energy utilization. Thus, CSA-MRPS Mechanism predicts the asset upgraded preparing unit with high position through the planning system.
TIO - Secure Input/Output for Intel SGX Enclaves

The new trusted execution environments (TEE) integrated within CPUs (e.g., Intel Software Guard Extensions) enable isolation of security-critical applications from the rest of the software running on the system. However, they lack support for trusted I/O such as keyboard or display, making those technologies unreliable in the case of user-centric applications. In this paper we introduce TIO, a practical hardware module that creates a secure channel between any USB HID device (e.g., keyboard/mouse) and a TEE (e.g., Intel SGX) using only local authentication and attestation. We describe the architecture and implementation of our solution, as well as practical considerations for deploying it in a real scenario and different USB devices. We evaluate the benefits of using our hardware module and show that there is no lag or noticeable impact on the input while it enhances the security of several applications. Moreover, TIO can be extended to any other input/output devices.
Text Authorship Identification Based On Ensemble Learning and Genetic Algorithm Combination in Turkish Text

The easiness of reaching information through the internet and social media and the expansiveness of opportunities for searching, copying, and spreading data have caused some problems in identifying an author for a specific text. A text carries the characteristic features of the person who wrote it, and these features can be used to identify its author. For this study, we are offering a method that is based on an approach using ensemble learning algorithm (ELA) and genetic algorithm (GA) for author identification in Tur-kish texts. The raw data set, which includes 40 authors and 3269 texts, was created from Turkish news websites and analyzed in pre-processing step. After, syntactic and structural analyses were done on the data and, in total, 6 different data sets were created. Each of the data sets was subjected to the feature selection process by using GA and ELA approach together. Each of the obtained data sets from the previous step was classified by using the ELA's bagging method which contains 5 different classifiers, namely, Naive Bayes, K-Nearest Neighbor, Artificial Neural Networks, Support Vector Machine, and Decision Tree. After applying the aforementioned processes to the raw data, the author identification approach reached 89% accuracy. The combination of ELA and GA has a strong potential to identify the author of a text.
Evolution Process and Supply Chain Adaptation of Smart Contracts in Blockchain

Because of its unique decentralization, encryption, reliability, and tamper-proof, the block chain system makes smart contracts break through the shackles of the lack of trusted environment, and its application field keeps expanding. We read the source code and official documents of Bitcoin, Ethereum, and Hyperledger to explore the operation principle and implementation mode of smart contract. By analyzing the evolution process of smart contracts in blockchain and the sequence of its function expansion, according to the multirole business process of supply chain, we design a semipublic smart contract chain model based on Ethereum and Hyperledger in order to provide useful inspiration and help for the future research of smart contracts in blockchain applied in supply chain.
Natural Language Processing Based Approach to Overcome Arabizi and Code Switching in Social Media Moroccan Dialect

Over the past decade, the use of social networks has become more common and people have found a convenient place to share information and express opinions. The massive volume of data generated provides a good opportunity to extract valuable knowledge to reveal people's needs and behaviours. For this purpose, Sentiment Analysis techniques are widely used. The results are very accurate when they are applied to common languages, namely English, Spanish or French. However, these techniques are still at development stage for Modern Standard Arabic (MSA) and derived dialects. In the case of Moroccan Dialect used in Social Media, the main challenge is the phenomena of Code Switching; two or more languages appear in the same sentence (Arabic, Tamazight, French, English or Spanish). The second is the Arabizi of words using Latin script combined with numbers instead of Arabic characters. As a consequence, the preprocessing became one of the important steps of data analysis. This paper proposes a new method based on Natural Language Processing (NLP) to address the challenges of preprocessing text that contains Arabizi and Code Switching forms. We aim to build a multilingual corpus that includes linguistic features and reflects the structure of text written in Social Media Moroccan Dialect.
Focusing Attention across Multiple Images for Multimodal Event Detection

Multimodal social event detection has been attracting tremendous research attention in recent years, due to that it provides comprehensive and complementary understanding of social events and is important to public security and administration. Most existing works have been focusing on the fusion of multimodal information, especially for single image and text fusion. Such single image-text pair processing breaks the correlations between images of the same post and may affect the accuracy of event detection. In this work, we propose to focus attention across multiple images for multimodal event detection, which is also more reasonable for tweets with short text and multiple images. Towards this end, we elaborate a novel Multi-Image Focusing Network (MIFN) to connect text content with visual aspects in multiple images. Our MIFN consists of a feature extractor, a multi-focal network and an event classifier. The multi-focal network implements a focal attention across all the images, and fuses the most related regions with texts as multimodal representation. The event classifier finally predict the social event class based on the multimodal representations. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments on a commonly-used disaster dataset. The experimental results demonstrate that, in both humanitarian event detection task and its variant of hurricane disaster, the proposed MIFN outperforms all the baselines. The ablation studies also exhibit the ability to filter the irrelevant regions across images which results in improving the accuracy of multimodal event detection.
Joint Modulation and Coding Recognition Using Deep Learning

Blind identification of modulation and channel coding parameters is a very important research topic in civil-military communication systems. The traditional algorithm is mainly implemented in the way of hierarchical recognition, that is, modulation recognition of the signal first, then demodulation of the signal, and finally coding type recognition and parameter estimation of the demodulated information stream, so as to realize the joint recognition of modulation and coding. In this paper, we propose a deep learning (DL)-based joint recognition algorithm for modulation and coding, which can achieve the recognition of modulation type and coding parameters simultaneously without using additional demodulation algorithms. Simulation results show that the proposed method performs well for the recognition of various modulation and coding types under high signal-to-noise ratio (SNR) conditions.
A Stereo Matching Algorithm for High‐Precision Guidance in a Weakly Textured Industrial Robot Environment Dominated by Planar Facets

Abstract Although many algorithms perform very well on certain datasets, existing stereo matching algorithms still fail to obtain ideal disparity images with high precision in practical robotic applications with weak or untextured objects. This greatly limits the application of binocular vision for robotic arm guidance. Traditional stereo matching algorithms suffer from disparity loss, dilation and other problems, and deep learning algorithms have weakly generalization ability, making high‐accuracy results impossible with non‐training images. We propose an algorithm that uses segments and edges as matching units. We find the mapping relationship between two‐dimensional images and three‐dimensional scenes using segments. The algorithm obtains highly accurate results in industrial robotic applications with mainly planar facets. We combine it with a deep learning algorithm to obtain very good high‐accuracy results in both general scenes and applications of industrial robots. The algorithm effectively improves the non‐linear optimization ability of traditional algorithms and generalization ability of deep learning, and provides an effective method for the binocular vision guidance of industrial robotic scenes. We used the algorithm to guide the robot arm for threading with a success rate of 70%.
Reduced and Distributed Estimation in Sensor and Actuator Networks - Automated Design Based on Controllability and Observability

To reduce memory and computational effort, large-scale systems are often modeled globally and divided into local models with lower system order. In previous papers, local sensor-based systems are mostly manually distributed. In our contribution, the controllability and observability of the global system is considered and an extension from sensor nodes to sensor and actuator networks is performed. Therefore, the inputs and outputs of the global system are allocated to local systems. Here, the local systems are reduced by model order reduction methods. To further reduce the memory and computational effort, the estimation method is distributed in sensor and actuator nodes. When the sensor and actuator exchange state vectors in a fully connected communication network, the local systems represent the global system. Finally, the global system is reconstructed by a central fusion center. These methods are experimentally evaluated on a demonstrator.
Machine Learning Approaches for Authorship Attribution using Source Code Stylometry

Identification of source code authorship is vital for attribution. In this work, a machine learning framework is described to identify source code authorship. The framework integrates the features extracted using natural language processing based approaches and abstract syntax tree of the code. We evaluate the methodology on Google Code Jam dataset. We present the performance measures of the logistic regression and deep learning on the dataset.
Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution

COVID-19 cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients were enrolled in our observational study. Anagraphical and anamnestic data, COVID-19 signs and symptoms, COVID-19 therapy, hematochemical test results, and prior therapies administered to patients are used as predictors. A set of 55 features, all of which can be taken in the first hours of the patient's hospitalization, was considered. Different solutions were compared achieving the best performance with a sequential convolutional neural network-based model merged in an ensemble with two different meta-learners linked in cascade. We obtained a median absolute error of 2.7 days (IQR = 3.0) in predicting the duration of the infection; the error was equally distributed in the infection duration range. This tool could preemptively give an outlook of the COVID-19 patients' expected path and the associated hospitalization effort. The proposed solution could be viable in tackling the huge burden and the logistics complexity of hospitals or rehabilitation centers during the pandemic waves. With data taken ad admission, entering a PCA-based feature selection, a k-fold cross-validated CNN-based model was implemented. After external texting, a median absolute error of 2.7 days [IQR = 3 days].
Multiple Imputation by Generative Adversarial Networks for Classification with Incomplete Data

Missing values present as the most common problem in real-world data science. Inadequate treatment of missing values could often result in mass errors. Hence missing values should be managed conscientiously for classification. Generative Adversarial Networks (GANs) have been applied for imputing missing values in most recent years. This paper proposes a multiple imputation method to estimate missing values for classification through the integration of GAN and ensemble learning. Our propose method MIGAN utilises GAN to generate different training observations which are then used to conduct ensemble classifiers for classification with missing data. We conducted our experiments examine MIGAN on various data sets as well as comparing MIGAN with the state-of-the-art imputation methods. The experimental results show significant results, which highlights the accuracy of MIGAN in classifying the missing data.
An Overview of Improved ViBe Algorithms

The ViBe (Visual Background extractor) algorithm is a moving object detection algorithm, which has good adaptability and real-time performance. Although its performance is excellent, the ViBe algorithm also has some disadvantages, such as easy to introduce ghost, insensitive to the detection of static targets, and easy to detect the shadow area as the foreground. In order to improve its performance, various improved ViBe algorithms emerged recently. In this paper, we firstly introduce the ViBe algorithm in detail, including background modeling, foreground detection and background model updating; Secondly, we introduce and analyze the improved algorithms to address its each shortcoming and to enhance the robustness in time order; Finally, we summarize and prospect the improved ViBe algorithms.
A Holistic View of Cybersecurity Education Requirements

This chapter sets the scene for the book as a whole, establishing the need for cybersecurity awareness, training, and education in order to enable us to understand and meet our security obligations. It begins by illustrating key elements that ought to form part of cybersecurity literacy and the questions to be asked when addressing the issue. It then examines the problems that have traditionally existed in terms of achieving awareness and education, both at the user level (in terms of lack of support) and the practitioner level (in terms of a skills shortage). The discussion highlights the importance of a holistic approach, covering both personal and workplace use, and addressing the spectrum from end-users through to cybersecurity specialists.
Key-Based Input Transformation Defense Against Adversarial Examples

Machine learning models are vulnerable to adversarial examples which are generated by adding perturbations to clean images. Input transformation defenses were proposed to defend against adversarial examples at test time. However, existing input transformation defenses are demonstrated to be bypassable by advanced attacks. To improve the robustness of input transformation defenses, we identify three essential properties of robust input transformations. Moreover, we propose a key-based input transformation defense which satisfies the three properties. We evaluate the key-based input transformation defense in three experimental settings including black-box, gray-box and white-box based on attackers’ knowledge about the target model and input transformation. We demonstrate that our key-based input transformation is effective against adversarial examples and difficult to be bypassed.
SAR and Optical Image Matching Eased on Phase Congruency and Template Matching

Owing to the different imaging principles, there are more differences between synthetic aperture radar (SAR) images and optical images. Matching SAR and optical image is always a difficult task. In the face of this intricate problem, we propose a new method that SAR and optical image matching based on phase congruency and template matching. For feature saliency, phase congruency in algorithm is a non-dimensional metric. It does not pay attention to the differences in light and dark changes in SAR and optical images, but only pays attention to the phase congruency itself. The image information obtained through phase congruency is suitable for matching SAR and Optical image. The template matching algorithm can get the most comprehensive and accurate result by allowing template image to find the most similar area in the entire image to be matched. The experimental result shows that the proposed SAR and optical image matching algorithm in view of phase congruency and template matching can efficiently and accurately complete the matching task of SAR and optical image and contribute new method for different source-common struct remote sensing image matching.
Improve Imbalanced Multiclass Classification Based on Modified SMOTE and Feature Selection for Student Grade Prediction

In higher education institutions (HEI), the ability to predict student grades as an early warning system is one of the important areas that gained attention to improve educational outcomes. Over the years, machine learning techniques have facilitated and successfully addressed student grade prediction for identifying the potentially weak students in a particular course. However, dealing with an imbalanced multiclass classification dataset is challenging due to biased results towards predicting the minority class. Therefore, this chapter proposes a method that can increase the classification performance by using a modified synthetic minority oversampling technique and feature selection (MSMOTE-FS). The experiments tested the proposed method's effectiveness by utilizing four oversampling techniques and six standard classification algorithms. This finding indicated that the proposed method gives promising results to improve the accuracy in multiclass classification of student grade prediction.
Twitter Sentiment Analysis of the 2019 Indian Election

In 2019, the Indian Loksabha Election saw around 360 million tweets on Twitter, giving their opinions and showing their sentiment toward the political leaders and their parties. The political parties have used this technique to run their campaigns and understand the opinions of the public; this also enables them to modify their campaigns accordingly. We performed text mining on approximately 2 million tweets collected over four months that referenced four national political parties in India during the campaigning period for the Loksabha election in 2019. We have identified the sentiment of Twitter users toward each of the considered Indian political parties (Congress, BJP, AAP and BSP) using Valence Aware Dictionary and sentiment Reasoner (VADER).
Selection of a Higher Educational Institution by Foreign Students: Motives, Factors and Problems (Based on the Results of Sociological Studies)

The article presents the results of two sociological studies carried out for the purpose of research of motivation of obtaining a higher education and factors of selection of a higher educational institution by foreign students, revelation of problems and elaboration of recommendations for their solution. It was revealed that foreign students in general praise obtaining a higher education, and that motivational postulates of obtaining education include not only commercial goals but cultural as well. It was established that Russian education is praised by foreign students in terms of quality, and that foreign students seek Russian education purposefully. It was found that Russia is a financially attractive country for foreign students, including employment. The study demonstrated a great significance of the website and social media of a higher educational institute for making decision on obtaining education in this higher educational institute. The results of the study showed that the level of competence in higher educational institutes of the city conforms to the requirements of foreign students and satisfies their demands maximally. At the same time several problems were revealed relating to training of foreign students: language problems; problems in training; communicational problems; social and cultural problems and social and everyday problems. Several recommendations were made to solve those problems.
Combining a Novel Scoring Approach with Arabic Stemming Techniques for Arabic Chatbots Conversation Engine

Arabic is recognized as one of the main languages around the world. Many attempts and efforts have been done to provide computing solutions to support the language. Developing Arabic chatbots is still an evolving research field and requires extra efforts due to the nature of the language. One of the common tasks of any natural language processing application is the stemming step. It is important for developing chatbots, since it helps with pre-processing the input data and it can be involved with different phases of the chatbot development process. The aim of this article is to combine a scoring approach with Arabic stemming techniques for developing an Arabic chatbot conversation engine. Two experiments are conducted to evaluate the proposed solution. The first experiment is to select which stemmer is more accurate when applying our solution, since our algorithm can support various stemmers. The second experiment was conducted to evaluate our proposed approach against various machine learning models. The results show that the ISRIS stemming algorithm is the best fit for our solution with accuracy 78.06%. The results also indicate that our novel solution achieved an F1 score of 65.5%, while the other machine learning models achieved slightly lower scores. Our study presents a novel technique by combining scoring mechanisms with stemming processes to produce the best answer for every query sent by chatbots users compared to other approaches. This can be helpful for developing Arabic chatbot and can support many domains such as education, business, and health. This technique is among the first techniques that developed purposefully to serve the development of Arabic chatbots conversation engine.