Reviewerly/exHarmony huggingface dataset:
Self-Supervised Learning for Solar Radio Spectrum Classification

Solar radio observation is an important way to study the Sun. Solar radio bursts contain important information about solar activity. Therefore, real-time automatic detection and classification of solar radio bursts are of great value for subsequent solar physics research and space weather warnings. Traditional image classification methods based on deep learning often require considerable training data. To address insufficient solar radio spectrum images, transfer learning is generally used. However, the large difference between natural images and solar spectrum images has a large impact on the transfer learning effect. In this paper, we propose a self-supervised learning method for solar radio spectrum classification. Our method uses self-supervised training with a self-masking approach in natural language processing. Self-supervised learning is more conducive to learning the essential information about images compared with supervised methods, and it is more suitable for transfer learning. First, the method pre-trains using a large amount of other existing data. Then, the trained model is fine-tuned on the solar radio spectrum dataset. Experiments show that the method achieves a classification accuracy similar to that of convolutional neural networks and Transformer networks with supervised training.
Context-based Sentiment analysis of Indian Marathi Text using Deep Learning

In Digital India, the Internet plays a crucial role in communication. The English language is widely used for such a process. The Internet has no language barrier. India is a multi-lingual country with boundless linguistic and social diversities. The most trending pattern observed in India is people intend to post their views, thoughts, feedback, and comments in their mother tongue over social media and blogs. Views posted by people is important for organization belonging to any category small, medium and large enterprises to improve their product or service. This data is hastily accumulated every day which should be necessary to identify and process. In terms of processing little work has been done for Indian languages where traditional approaches were used which are far away from the context of the text. In this research to perform sentiment analysis supervised algorithms that is Multinomial Naïve Bayes is implemented on the Marathi dataset. Along with this deep learning, Natural Language Processing approach Bidirectional Encoder Representations from Transformers (BERT) is utilized and fine-tuned for the specific work to evaluate more accuracy and State-of-the-Art results.
Using Multi-Label Multi-Class Support Vector Machines with Semantic and Lexical Features for Aspect Category Detection

In contrast to the aspects, aspect categories are often coarser and don't always appear as terms in sentences. Besides, the typical way to element the types associated with part is generally grainier concerning factors and doesn't exist within verdicts. The primary intent of the study is to investigate the efficacy of Lexicon, linguistic, vector-based, and features correlated to semantics within the aspect of the responsibility built with the finding of aspect category detection ACD). Semantic and emotional data are captured via vector-based features. Further, it examines vector-based feature superiority issues within the compression of features of text-based characteristics. Study purposes to the linguistic efficacy with the Lexicon, linguistic, and semantic features, also vector-based dependent to the system. Also, the information led with vector-based features that capture the semantic with sentimental analysis characteristics. With the experimental outcomes, the performance efficacy with the vector-based features outperformed text-based features. The methodologies associated with deep learning have generated features within the vector orientation relevant to the word-based structures. Therefore, the proposed method achieved effectiveness with the determined constraints by applying the metrics of precision, recall, and F1 scores. Correlating with the performance of ABSA's state-of-the-art techniques, the proposed research process gained superior outcomes.
KurdFace Morph Dataset Creation Using OpenCV

Automated facial recognition is rapidly being used to reliably identify the identities of individuals for a variety of applications, from automated border control to unlocking mobile phones. The attack of Morphing has presented a significant risk to the face recognition system (FRS) at automated border control. Face morphing is a technique for blending the facial images of two or more people such that the outcome looks like both of them. For example, a morphing attack may be used to get a fake passport by using a morphed image. This passport can be used by both the modified image contributors while crossing the border. Due to the publicly available digital altering tools that criminals may use to carry out face morphing attacks. Morph Attack Detection (MAD) systems have received a lot of attention in recent years. In the absence of automated morphing detection, Face Recognition Systems (FRS) are extremely susceptible to morphing attacks. Due to the limited number of publicly available face morph datasets to investigate, especially to our knowledge, there is no Kurdish morph dataset. In this work, we decided to generate a new face dataset, including morphed images which we named as "KurdFace" dataset. OpenCV was used to generate morphed images. Then we study the susceptibility of biometric systems to such morphed face attacks by designing and creating a Morph Attack Detection model to distinguish morphed images from genuine ones. To evaluate the robustness of our dataset regarding morphing attack detection, we compare it with the AMSL dataset to determine the classification error rate on both datasets to see how our dataset is different from others. Local Binary Pattern and Uniform Local Binary Pattern are used as feature extraction techniques, and as a classifier, SVM is utilized. The experimental result shows that our dataset is suitable for research purposes.
Ensemble of Networks for Multilabel Classification

Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the power of ensembles and deep learners. This work proposes merging both methods by combining a set of gated recurrent units, temporal convolutional neural networks, and long short-term memory networks trained with variants of the Adam optimization approach. We examine many Adam variants, each fundamentally based on the difference between present and past gradients, with step size adjusted for each parameter. We also combine Incorporating Multiple Clustering Centers and a bootstrap-aggregated decision trees ensemble, which is shown to further boost classification performance. In addition, we provide an ablation study for assessing the performance improvement that each module of our ensemble produces. Multiple experiments on a large set of datasets representing a wide variety of multilabel tasks demonstrate the robustness of our best ensemble, which is shown to outperform the state-of-the-art.
The Effect of Disaster Education of Increasing Earthquake Disaster Preparedness : A Narrative Review

Aims: Earthquake is a disaster that causes a lot of losses both physically, mentally and the highest cause of death compared to other disasters. This is because public knowledge about management and preparedness in dealing with disasters is lacking, so socialization related to education regarding disaster management due to earthquakes is crucial to be given to the community to minimize losses caused. So there is a need for a literature review related to the implementation of education in disaster management. Purpose: Conducted a narrative review to determine the effect of disaster education on increasing earthquake preparedness. Methods: The article search strategy used three databases (Google Scholar, EBSCO, Garuda) which were searched from 2014-2021 in the form of research reports and reviews that discussed the effect of disaster education on increasing earthquake preparedness. with the keywords Education, Earthquake Disaster, Preparedness, Education, Earthquake Disaster, Preparedness in a database search. Then the critical appraisal tools from JBI were carried out. There were six articles that met the inclusion and exclusion criteria set. Results: Shows that the provision of earthquake disaster preparedness education can be carried out by various methods, including the introduction of school-based disaster risk, with lectures, questions and answers, simulations, demonstrations, and audiovisual techniques. The several journals above, it shows a significant difference in the form of an increase in results before being given the intervention and after being given the intervention. Conclusion: Based on the results of the literature review is that there is the effect of disaster preparedness training earthquake with methods of educational disaster on community preparedness. And this educational method can be applied to the community to increase preparedness for earthquake disasters.
Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Precise traffic classification is essential to numerous network functionalities such as routing, network management, and resource allocation. Traditional classification techniques became insufficient due to the massive growth of network traffic that requires high computational costs. The arising model of software defined networking (SDN) has adjusted the network architecture to get a centralized controller that preserves a global view over the entire network. This paper proposes a model for SDN traffic classification based on machine learning (ML) using the Spark framework. The proposed model consists of two phases; learning and deployment. A ML pipeline is constructed in the learning phase, consisting of a set of stages combined as a single entity. Three ML models are built and evaluated; decision tree, random forest, and logistic regression, for classifying a well-known 75 applications, including Google and YouTube, accurately and in a short time scale. A dataset consisting of 3,577,296 flows with 87 features is used for training and testing the models. The decision tree model is elected for deployment according to the performance results, which indicate that it has the best accuracy with 0.98. The performance of the proposed model is compared with the state-of-the-art works, and better accuracy result is reported.
Dialect classification using acoustic and linguistic features in Arabic speech

&lt;span lang="EN-US"&gt;Speech dialects refer to linguistic and pronunciation variations in the speech of the same language. Automatic dialect classification requires considerable acoustic and linguistic differences between different dialect categories of speech. This paper proposes a classification model composed of a combination of classifiers for the Arabic dialects by utilizing both the acoustic and linguistic features of spontaneous speech. The acoustic classification comprises of an ensemble of classifiers focusing on different frequency ranges within the short-term spectral features, as well as a classifier utilizing the ‘i-vector’, whilst the linguistic classifiers use features extracted by transformer models pre-trained on large Arabic text datasets. It has been shown that the proposed fusion of multiple classifiers achieves a classification accuracy of 82.44% for the identification task of five Arabic dialects. This represents the highest accuracy reported on the dataset, despite the relative simplicity of the proposed model, and has shown its applicability and relevance for dialect identification tasks. &lt;/span&gt;
Blockchain Network Analysis: A Comparative Study of Decentralized Banks

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, and Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network.
Decentralized lending and its users: Insights from Compound

Permissionless blockchains offer an information environment where users can interact privately without fear of censorship. Financial services can be programmatically coded via smart contracts to automate transactions without the need for human intervention or knowing user identity. This new paradigm is known as decentralized finance (DeFi). We investigate Compound (a leading DeFi lending protocol) to show how it works in this novel information environment, who its users are, and what factors determine their participation. On-chain transaction data shows that loan durations are short (31 days on average), and many users borrow to support leveraged investment strategies (yield farming). We show that systemic risk in DeFi arises from concentration and interconnection, and how traditional risk management practices can be challenging for DeFi.
Rotational Differential-Linear Cryptanalysis Revisited

Abstract The differential-linear attack, combining the power of the two most effective techniques for symmetric-key cryptanalysis, was proposed by Langford and Hellman at CRYPTO 1994. From the exact formula for evaluating the bias of a differential-linear distinguisher (JoC 2017), to the differential-linear connectivity table technique for dealing with the dependencies in the switch between the differential and linear parts (EUROCRYPT 2019), and to the improvements in the context of cryptanalysis of ARX primitives (CRYPTO 2020, EUROCRYPT 2021), we have seen significant development of the differential-linear attack during the last four years. In this work, we further extend this framework by replacing the differential part of the attack by rotational-XOR differentials. Along the way, we establish the theoretical link between the rotational-XOR differential and linear approximations and derive the closed formula for the bias of rotational differential-linear distinguishers, completely generalizing the results on ordinary differential-linear distinguishers due to Blondeau, Leander, and Nyberg (JoC 2017) to the case of rotational differential-linear cryptanalysis. We then revisit the rotational cryptanalysis from the perspective of differential-linear cryptanalysis and generalize Morawiecki et al.’s technique for analyzing , which leads to a practical method for estimating the bias of a (rotational) differential-linear distinguisher in the special case where the output linear mask is a unit vector. Finally, we apply the rotational differential-linear technique to the cryptographic permutations involved in , , , and . This gives significant improvements over existing cryptanalytic results, or offers explanations for previous experimental distinguishers without a theoretical foundation. To confirm the validity of our analysis, all distinguishers with practical complexities are verified experimentally. Moreover, we discuss the possibility of applying the rotational differential-linear technique to S-box-based designs or keyed primitives, and propose some open problems for future research.
Industry-Specific Architecture Patterns

Every business domain has its own set of problems that need to be addressed with an enterprise software system. There are many industry-specific use cases, and software vendors have developed special software to cater to those use cases. As an architect, it is important to have a knowledge of the domain when designing software systems for a given enterprise. We have discussed many common architecture patterns in this book that can be utilized by enterprises regardless of their business domain.
Universal Global Cloning of Continuous Variables Entanglement

Abstract It is impossible to perfectly duplicate an unknown entangled state while preserving inseparability, which is known as the entanglement no‐cloning principle. Nevertheless, approximate cloning of entanglement is allowed by quantum mechanics. A universal entanglement cloning machine (UECM) duplicates an entangled state such that the quality of its entanglement replicas does not depend on the input. To duplicate entanglement shared between two parties, 1‐to‐ N universal local entanglement cloning machine (ULECM) has already been proposed (Weedbrook, et al., Phys. Rev. A, 77, 052313 (2008)), which employs two local UECMs to copy each party of the entangled state. However, the ULECM can never preserve the inseparability in its replicas. Here, a 1‐to‐ N universal global entanglement cloning machine (UGECM) that takes the entire entangled state as the input and then globally clone it to produce replicas is proposed. It is demonstrated that the UGECM outperforms the ULECM both in terms of the fidelity and the inseparability preservation. In addition, the UGECM is of more simple and easy structure, compared with the UGECM. Such a UGECM may find its new applications in quantum entanglement broadcasting.
An Experimental Study on Checklist-Based and Perspective-Based Requirements Reading Techniques Using E-Review Tool

Software Requirements Review (SRR) is a formal review process in which several reviewers read all or parts of the Software Requirements Specification (SRS) to look for defects in the requirements. During requirements review sessions, reviewers may employ various reading techniques to ensure that the requirements have been completely and clearly specified. To review an SRS document, the review leader must organise a review session, the reviewers must physically meet and provide their review feedback during the session. In these situations, the review leader must schedule the review session based on the reviewers' availability, which can be laborious and time-consuming to arrange. Additionally, the review leader needs to manually consolidate all the outcomes of the review session, which could also require a considerable amount of effort and time. However, there has been an insufficient research to identify the effectiveness of reading techniques for requirement reviews by employing a dedicated tool support for requirements review. Using a web-based application, called e-Review, the aim of this study is to experiment the effectiveness of Checklist-based Reading (CBR) and Perspective-based Reading (PBR) techniques during requirements review session.
PERANAN PERANGKAT TURNITIN DALAM MENDORONG KARYA ILMIAH BERKUALITAS

The purpose of scientific writing is to explain the role of the Turnitin tool to detect plagiarism in a scientific work and also to encourage quality scientific work. To suppress plagiarism and encourage quality scientific work, it is necessary to have a detailed and intense mastery of scientific work material so as to produce scientific writings that produce writings that have high originality. To suppress plagiarism in scientific writing, you can also use a paraphrase strategy and process words and sentences manually. Although the Turnitin tool is currently the best tool for detecting plagiarism, it still has a weakness, namely that it only detects the similarity or similarity of words and sentences. For this reason, there needs to be collaboration between students, lecturers and universities in suppressing thesis plagiarism and utilizing the role of the Turnitin tool in encouraging quality scientific works or thesis..
Reanimator: from Test Data to Code and Back

State-of-the-art automatic testing tools can efficiently detect various kinds of errors in software projects; however, these errors often cannot be automatically extracted to a standalone reproducing test case, a feature valuable for the purposes of further software maintenance. Objects present the main difficulty, as generating code to construct an object given its state is hard, due to the encapsulation principle. In this paper we present an approach called Reanimator that, given an object representation, is able to generate a valid code snippet which constructs this object using its publicly available API. Reanimator can be applied to any automatic testing tool to help it generate reproducing test cases for detected failures. We implemented our approach as a part of an automatic testing tool called Kex and evaluated it on a number of open-source projects from GitHub. Evaluation results showed that our approach is compatible with the state of the art techniques.
Research on Transformer Oil Level Monitoring System Based on Inspection Robot

Abstract Because the traditional oil level gauge transformer relies on manual regular patrol detection, it is not easy to know the state change of the oil level in real time. In recent years, the rapid development of AR/VR technology, the positioning, and the construction of maps of robots have also received widespread attention, and the oil level inspection task has gradually evolved in the direction of intelligent machine inspection. Based on this background, this paper studies the Cartographer algorithm, according to the mapping results for path planning, to achieve robot autonomous navigation of the designated location. After arriving at the destination, the user obtains the oil level by the depth camera, and uses the YOLOv4 to design an intelligent detection algorithm for oil level detection, using neural networks to learn the oil level state in various weather, and train a dedicated model. Through experimental verification, the inspection robot can complete the construction of the map and realize autonomous path navigation according to the target point, and the oil level detection system based on the YOLOv4 algorithm can achieve an accuracy of more than 90% for the recognition of oil level information, which has good applicability.