Reviewerly/exHarmony huggingface dataset:
Situational Understanding and Diagnostics

This chapter describes situational understanding and diagnostics for autonomous cyber-defense agents. It covers architectural patterns, functional aspects, and interfaces with other agent capabilities. It motivates the need for situational understanding and diagnostics, outlines the major challenges to be met, and considers several illustrative examples. The material centers on the core requirements of situational understanding: diagnosing the nature of a situation, projecting possible future states, assessing associated risks, and triggering responses when situations warrant them. From a functional standpoint, this chapter describes how an agent processes sensed information, continually updates its knowledge base, and assesses a given situation. It examines how these functions must consider adversarial presence, system vulnerabilities, potential adversarial movement, and cyber-to-mission dependencies. This chapter describes agent world models to be instantiated for a given situation, which span the agent, the defended system, perceived threats, and mission context. It also considers methods for managing model and algorithmic complexity and for adapting to new situations, along with practical concerns for agents deployed within operational environments.
Case Study C: Sentinels for Cyber Resilience

This chapter describes an approach to cyber resilience in which dedicated hardware/software subsystems called sentinels are used to monitor critical system functions for abnormal or unacceptable performance, as defined by the resilience engineer. The detection of abnormal or unacceptable function then provides the trigger for control actions, such as switching to redundant functions or components, that form the basis for recovery of system function. This chapter discusses how sentinels detect attacks, what they do after detection, and how to choose where to put them. The discussion covers several specific engineering patterns for sentinels and resilient models of operation, as well as more general topics including operational and life cycle issues in sentinel-based cyber resilience and the roles of humans vs. autonomy (e.g. manual, semi-automated, automated) in controlling sentinels and reconfiguration actions. The chapter concludes with a case study on a hypothetical weapons system.
Frequency hopping signal detection based on optimized generalized S transform and ResNet

The performance of traditional frequency hopping signal detection methods based on time frequency analysis is limited by the tradeoff of time-frequency resolution and spectrum leakage. Machine learning-based frequency hopping signal detection techniques have a high level of complexity. Therefore, this paper proposes a residual network and the optimized generalized S transform to detect frequency hopping signals. First, based on the time-frequency aggregation measure, the generalized S transform parameters $ \lambda $ and $ p $ are optimized using a multi-population genetic algorithm. Second, the optimized generalized S transform is used to determine a signal's time-frequency spectrum, which is then normalized to make this robust to noise power uncertainty. Finally, a residual network structure is designed which receives the time-frequency spectrum. To detect frequency hopping signals, the network automatically learns the time-frequency properties of signals and noise. Simulated findings indicate that the multi-population genetic algorithm not only increases optimization efficiency when compared to a regular genetic algorithm, but also has faster convergence and more stable optimization results. Compared with a hybrid convolutional network/recurrent neural network algorithm, the proposed technique is better at detection and has less computational and storage complexity.
Research on innovative cultivation mode of English and American literature translation talents based on internet environment

Abstract English and American literature translation can help readers appreciate foreign cultures and deepen their interpretation and knowledge of English and American literature. There are more relevant contents involved in the teaching of English and American literature translation. Translation is a very important task for different countries to communicate and must be given enough attention, and the translation of English and American literature is very important for readers to deeply appreciate the cultural style of foreign countries. In order to solve these problems and find the appropriate strategies for translating talents from English and American literature. In this paper, we will analyze this specifically, meet the requirements of training translation talents, combine the development of our country and our own background, and create a new training mode according to the Internet environment, so as to strengthen the training of talents. At the same time, the idea of constructing the teaching and composite innovative talents cultivation model of environmental ecology courses under the background of Internet big data is proposed, and the characteristics and functions of the innovative teaching and talents cultivation model are analyzed. The ultimate goal is to cultivate outstanding talents with a sense of family, global vision, innovation and practical ability, and to promote their application through teaching salons and symposiums. With the use of this model, the current environment of the Internet has been innovated in the innovative mode of talent training, and the corresponding training methods have created the best learning space for students and improved their ability to read original English and American texts and translate their works.
PERANCANGAN SISTEM INFORMASI PEMETAAN WILAYAH AIR BAKU KOTA TASIKMALAYA (SIPWARKOT) PADA UPTD PSDA WILAYAH SUNGAI CIWULAN-CILAKI

Geographic information systems or GIS are information systems related to geographical features and are used to manipulate and display information in a geographical area. GIS can also be used for mapping, particularly in raw water areas. At the UPTD PSDA Ciwulan-Cilaki River Area, there are problems in managing data on applications for the submission of raw water requests carried out by the community. The applicant was required to send a letter requesting the submission of raw water to the agency, which resulted in the piling up of letters in the archives. Another problem found is that the survey process feels very long and takes days. This aimed of this researched was to design a geographic information system to solve the problem. In this research, the researchers designed a geographic information system mapping the raw water area of Tasikmalaya City based on Android using the Prototype method. The stages in the Prototype method are Communication, Planning, Representation, and Construction. The benefits and results of this information system design were that it made it easier for applicants to apply for raw water along with mapping the raw water area and for application management agencies to be more structured. In addition, this android-based GIS could be accessed by applicants and agencies anytime and anywhere.
Contour detection network for zero-shot sketch-based image retrieval

Abstract Zero-shot sketch-based image retrieval (ZS-SBIR) is a challenging task that involves searching natural images related to a given hand-drawn sketch under the zero-shot scene. The previous approach projected image and sketch features into a low-dimensional common space for retrieval, and used semantic features to transfer the knowledge of seen to unseen classes. However, it is not effective enough to align multimodal features when projecting them into a common space, since the styles and contents of sketches and natural images are different and they are not one-to-one correspondence. To solve this problem, we propose a novel three-branch joint training network with contour detection network (called CDNNet) for the ZS-SBIR task, which uses contour maps as a bridge to align sketches and natural images to alleviate the domain gap. Specifically, we use semantic metrics to constrain the relationship between contour images and natural images and between contour images and sketches, so that natural image and sketch features can be aligned in the common space. Meanwhile, we further employ second-order attention to capture target subject information to increase the performance of retrieval descriptors. In addition, we use a teacher model and word embedding method to transfer the knowledge of the seen to the unseen classes. Extensive experiments on two large-scale datasets demonstrate that our proposed approach outperforms state-of-the-art CNN-based models: it improves by 2.6% on the Sketchy and 1.2% on TU-Berlin datasets in terms of mAP.
Transdisciplinarity based on Category Theory

Physical reality can be characterized by the theory of categories that brings to the fore transdisciplinarity through invariants and structural and management patterns common to many fields. Applying the model of thinking the universe of systems according to category theory, many opportunities open for their modeling, but also for their planning, management and evaluation procedures. Structures and concepts in physical reality, as well as the relationships between them, become objects and morphisms through category theory, thus being valid in many fields. Objects can be aggregated through the relationships between them, in increasingly complex objects that meet the requirements of users in the knowledge society. In this respect, category theory provides the working tools for transdisciplinary modeling of systems on different levels of increasingly sophisticated reality
Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

In the field of human–computer interaction (HCI), text entry methods can be evaluated through controlled user experiments or predictive modeling techniques. While the modeling approach requires a language model, the empirical approach necessitates representative text phrases for the experimental stimuli. In this context, finding a phrase set with the best language representativeness belongs to the class of optimization problems in which a solution is sought in a large search space. We propose a genetic algorithm (GA)-based method for extracting a target phrase set from the available text corpus, optimizing its language representativeness. Kullback–Leibler divergence is utilized to evaluate candidates, considering the digram probability distributions of both the source corpus and the target sample. The proposed method is highly customizable, outperforms typical random sampling, and exhibits language independence. The representative phrase sets generated by the proposed solution facilitate a more valid comparison of the results from different text entry studies. The open source implementation enables the easy customization of the GA-based sampling method, promotes its immediate utilization, and facilitates the reproducibility of this study. In addition, we provide heuristic guidelines for preparing the text entry experiments, which consider the experiment’s intended design and the phrase set to be generated with the proposed solution.
A multidimensional interpretation of Marxist globalization theory based on decentralized internet

Abstract To achieve a multidimensional interpretation of Marxist globalization theory, this paper provides a fecundation network facility based on a decentralized internet, thus overcoming various drawbacks of the network where traditional spatial services are located. In the Marxist theory of globalization, the rise from concrete rigor to abstract rigor is an important dialectical transformation of materialist epistemology. It is implied that Marx, based on the principle of mathematical expression of intuition and detail, refined, and deepened the paradigm itself through mathematical tools and mathematical methods, and used mathematical ideas to condense the theory of political economy, making the property of precision reflected in the logic of the paradigm. The 3PL model is established and the basic principles are accurately understood and grasped from mathematical logic according to the labor theory of value, surplus value theory and reproduction theory in Marxism, which provides an important generative logic for constructing a mathematical paradigm of Marxist political economy. The results of the study show that the output efficiency and benefits of fully cooperative networks ( L = 0.92, Q = 281.5), after a long period of competition makes the output of the network less efficient, which in turn makes the network as a whole obtain lower returns ( L = 0.73, Q = 96.18). In a competitive coexistence network structure, although there are competitive relationships at the core firm level, the network is not greatly affected by this ( L = 0.83, Q = 202.92) instead, it will break the monopoly alliance and enliven the local economy. By constructing a rigorous and sophisticated mathematical and theoretical model, the theory of political economy can be made more abstract and scientific, and the analytical and explanatory power of the theory of political economy can be improved.
A Novel Methodology for Developing Troubleshooting Chatbots Applied to ATM Technical Maintenance Support

The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications.
RESEARCH ON IDENTIFICATION AND CLASSIFICATION METHOD OF IMBALANCED DATA SET OF PIG BEHAVIOR

To address the problem of the low accuracy and poor robustness of modeling methods for imbalanced data sets of pig behavior identification and classification, the three commonly used re-sampling methods of under-sampling, SMOTE and Borderline-SMOTE are compared, and an adaptive boundary data augmentation algorithm AD-BL-SMOTE is proposed. The activity of the pigs was measured using triaxial accelerometers, which were fixed on the backs of the pigs. A multilayer feed-forward neural network was trained and validated with 21 input features to classify four pig activities: lying, standing, walking, and exploring. The results showed that re-sampling methods are an effective way to improve the performance of pig behavior identification and classification. Moreover, AD-BL-SMOTE could yield greater improvements in classification performance than the other three methods for balancing the training data set. The overall major mean accuracy of lying, standing, walking, and exploring by pigs A, B and C was significantly improved by using AD-BL-SMOTE, reaching 91.8%, 93.0% and 96.0%, respectively.
Optimization of vocal singing training method based on intelligent big data technology

Abstract With the advancement of science and technology, the relationship between vocal singing training methods and big data technology is getting closer and closer. The application of training methods is a means of expressing vocal compositions. Emotional expression is the ultimate purpose and meaning of method optimization. Training methods and emotional expression are equally important in vocal singing, and both are indispensable. In this paper, a VSSA-separation model is established based on the Vocal Signal Separation Algorithm (VSSA). Focus on optimizing the training method of vocal singing. Through in-depth discussion of existing works, I try to find its inner influence and outer connection. In order to better understand the grasp and application of vocal singing method and emotional expression balance. It can be seen from the calculation that the image shaping in the song language accounts for 83% of the optimization of the training method. Inverse word processing in song language has a different proportion of the optimization of the training method. It accounts for 71% of English operas. It accounts for only 13% of Chinese operas. Artistic effects have the highest average optimization difficulty among the training methods. Its optimization difficulty is as high as 89%.
Predicting Phishing Victimization: Comparing Prior Victimization, Cognitive, and Emotional Styles, and Vulnerable or Protective E-mail Strategies

Phishing victimization is prevalent and results in theft of personal identifiable information (PII) or installing malware to steal PII. Drawing upon social psychological and criminological theories, we conducted a prospective study to assess three groups of predictors to being phished or not: a) prior victimization; b) protective or vulnerable habitual strategies, and c) emotional and cognitive decision-making styles. Students (N = 236) completed a survey assessing these predictors and then about 4 weeks later received a phishing e-mail using the university’s phishing testing system. The e-mail requested that they click on a link and enter their student ID to avoid having their account blocked. About half (50.8%) clicked on the link, and 81.6% of those phished entered their PII. Individuals who had low avoidant style and high generalized anxiety were four times more likely to be phished, after controlling for the significant effects of vulnerable habitual strategies and using dating apps. Machine learning analyses also found cognitive styles and generalized anxiety are the better predictors of getting phished compared to vulnerable and protective strategies and prior victimization. These findings suggest that cybersecurity training needs to be expanded to address the emotional and cognitive processing of deceptive appeals in e-mails.
Big data BPMN workflow resource optimization in the cloud

Cloud computing is one of the critical technologies that meet the demand of various businesses for the high-capacity computational processing power needed to gain knowledge from their ever-growing business data. When utilizing cloud computing resources to deal with Big Data processing, companies face the challenge of determining the optimal use of resources within their business processes. The miscalculation of the necessary resources directly affects their budget and can cause delays in the cycle time of their key processes. This study investigates the simulation of cloud resource optimization for Big Data workflows modeled with the Business Process Modeling Notation (BPMN). To this end, a BPMN performance evaluation framework was developed. The framework’s capabilities were presented using real-world data science workflow and later evaluated on workflows consisting of 13, 52, and 104 tasks. The results show that the developed framework is adequate for estimating the overall run-time distribution and optimizing the cloud resource deployment and that the BPMN can be utilized for Big Data processing workflows. Therefore, this study contributes to BPMN practitioners by providing a tool to apply BPMN for their Big Data workflows and decision-makers by giving them critical insights into their key business processes. The framework source code is available at https://github.com/ntankovic/python-bpmn-engine.
Product Authentication and Traceability Using Blockchain

This article provides a comprehensive review on the use of blockchain technology in supply chain management, with a focus on product authentication and traceability. It then reviews the existing literature on the use of blockchain technology in supply chain management, including case studies and surveys. The product supply chain is a complex process that involves multiple stakeholders, including suppliers, manufacturers, distributors, retailers, and customers. Ensuring transparency, efficiency, and security throughout the supply chain is a challenging task. This paper proposes a decentralized blockchain-based product supply chain management system with QR code verification that can improve transparency, efficiency, and security. The system allows manufacturers to enter product details, including brand name, logo and generates a unique QR code for each product. The QR code can be used by buyers to verify the authenticity of the product and track its movement through the supply chain. The methodology used includes Ganache, IPFS, Blockchain, Ethereum and Truffle.
Effects of Language Ontology on Transatlantic Automatic Speech Understanding Research Collaboration in the Air Traffic Management Domain

As researchers around the globe develop applications for the use of Automatic Speech Recognition and Understanding (ASRU) in the Air Traffic Management (ATM) domain, Air Traffic Control (ATC) language ontologies will play a critical role in enabling research collaboration. The MITRE Corporation (MITRE) and the German Aerospace Center (DLR), having independently developed ATC language ontologies for specific applications, recently compared these ontologies to identify opportunities for improvement and harmonization. This paper extends the topic in two ways. First, this paper describes the specific ways in which ontologies facilitate the sharing of and collaboration on data, models, algorithms, metrics, and applications in the ATM domain. Second, this paper provides comparative analysis of word frequencies in ATC speech in the United States and Europe to illustrate that, whereas methods and tools for evaluating ASRU applications can be shared across researchers, the specific models would not work well between regions due to differences in the underlying corpus data.
Sign Language Recognition using Deep Learning

Most dumb and deaf (hard of hearing) people use sign language to communicate both inside and outside of their groups. They utilize hand gestures to communicate because they are mute and deaf. Sign Language Recognition (SLR) objective is to recognize and learn hand signals and to keep going until the corresponding hand movements can be translated into text or speech. We have utilized computer vision to recognize hand motions by creating Deep Neural Network Designs Using Con- volution Neural Network Architectures, where the model will learn to recognize the hand gesture photographs throughout an epoch. A text file in the English Language is created once the motion has been correctly recognized by the model and may then be converted to voice. The main Motive of the Project is to make Deaf and Dump people’s communication easier.Deaf (hard of hearing) and stupid individuals mostly utilise sign language to communicate inside and outside of their respective communities. Because they are unable to speak or hear, they use hand signals to communicate. The goal of sign language recognition (SLR) is to identify acquired hand motions and to continue until related hand gestures are translated into text or speech. Building Deep Neural Network designs (Convolution Neural Network Architectures), where the model will learn to recognise the hand gesture photos throughout an epoch, allows us to use computer vision to recognise the hand motions