Reviewerly/exHarmony huggingface dataset:
Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables

Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs.
Linking Comprehension and Production: Frequency Distribution of Chinese Relative Clauses in the Sinica Treebank

This chapter presents the distribution of Chinese relative clauses in the Sinica Treebank (Chen et al., Sinica corpus: Design methodology for balanced corpora, 1996; Huang et al., Mandarin Chinese words and parts of speech: A corpus-based study, Taylor & Francis, 2017). We extracted 3081 relative clauses from the treebank and classified the relative clauses into six types, including gapless relative clauses, possessive relative clauses, descriptive relative clauses, passive relative clauses, subject relative clauses, and object relative clauses. Each type of relative clause will be discussed regarding the length and syntactic complexity of prenominal clauses, the length and animacy/humanness of head nouns, the part-of-speech categories of embedded verbs, and the position of complex noun phrases in matrix clauses. The issues of the classifier phrase position in relation to relative clauses, the use of suo in object relative clauses, and cases where the head nouns are omitted will also be discussed. Based on the corpus distributions, we consider the implications for the comprehension of Chinese relative clauses.
A Study on the Semantic Interpretation of Chinese Noun Compounds

Noun compound interpretation is determined by implicit semantic relations encoded by constituent nouns. In this chapter, we will present preliminary research on the interpretation of Chinese noun compounds (NCs) using two different strategies. For the abstract relation strategy, we proposed a novel taxonomy of Chinese noun compounds and a method for interpreting Chinese NCs based on word similarity. For the verbal paraphrasing strategy, we proposed the simple dynamic approach of using paraphrasing verbs, which not only provided possible interpretations of noun compounds but also captured the subtle semantic differences of similar NCs. Both strategies can be applied in other fields, such as question answering, information retrieval, and lexicography.
Understanding Convolutional Neural Network With TensorFlow

In academia and business, deep-learning-based models have exhibited extraordinary performance over the last decade. The learning potential of Convolutional Neural Networks (CNNs) derives from a combination of several feature extraction levels that completely use a vast quantity of input. CNN is an important technique for tackling computer vision issues, although the theories behind its processing efficacy are not yet completely understood. CNN has achieved cutting-edge performance on a variety of datasets in computer vision applications like remote sensing, medical image categorization, facial detection, and object identification. This is due to the efficiency with which they process visual features. This chapter presents the most significant advancements in CNN for efficient processing in computer vision, including convolutional layer configurations, pooling layer approaches, network activation functions, loss functions, normalization approaches, and CNN optimization techniques.
Information support of e-learning: ukrainian challenges and cases during the war

Objectives The work is devoted to the description of the e-learning environment as an element of the information system for ensuring the activities of the university. The relationship between the processes of building a digital economy and digitalization of the educational system is shown. During 2019-2022 there was a certain evolution from classroom learning, to distance learning synchronous and asynchronous learning. Based on bibliometric analysis, it is possible to formulate the purpose of the article: a description of a student-oriented university information system and an asynchronous e-learning system. Material and methods The information base for this study is based on scientific articles, monographs by Ukrainian and foreign authors and the authors' own empirical research. The main method of the study was bibliometric analysis using the VOSviewer software tool to analyze the relationships between different categories and build maps to visualize the interconnectedness between them in publications indexed by the Scopus database. Results The information system for organizing the educational activities of the university includes autonomous blocks, which at the same time are important links in the process of ensuring the quality of e-learning. The e-learning environment, including asynchronous, requires not only content creation, but also organizational and technical support for online, blended, or remote learning. Conclusions An effectively organized e-learning system will create an effective environment in which the goals of sustainable development will be achieved through the provision of quality education and the achievement of economic growth. The use of various types of distance learning can be useful for other countries under certain circumstances regarding movement restrictions.
Survey on Computer Forensics and its most using Technique Steganography

A subfield of digital forensic science called computer forensics deals with evidence discovered on computers and digital storage devices. Computer forensics aims to detect, preserve, retrieve, analyse and communicate facts and views regarding the digital information by performing a forensically sound examination of digital media. The purpose of this study is to provide a brief discussion of computer forensics and related methods. Steganography is one of the most widely utilised of these approaches, and it will also be briefly discussed below.
The Indifferentiability of the Duplex and Its Practical Applications

The Duplex construction, introduced by Bertoni et al. (SAC 2011), is the Swiss Army knife of permutation-based cryptography. It can be used to realise a variety of cryptographic objects—ranging from hash functions and MACs, to authenticated encryption and symmetric ratchets. Testament to this is the STROBE protocol framework which is a software cryptographic library based solely on the Duplex combined with a rich set of function calls. While prior works have typically focused their attention on specific uses of the Duplex, our focus here is its indifferentiability. More specifically, we consider the indifferentiability of the Duplex construction from an online random oracle—an idealisation which shares its same interface. As one of our main results we establish the indifferentiability of the Duplex from an online random oracle. However indifferentiability only holds for the standard Duplex construction and we show that the full-state variant of the Duplex cannot meet this notion. Our indifferentiability theorem provides the theoretical justification for the security of the Duplex in a variety of scenarios, amongst others, its use as a general-purpose cryptographic primitive in the STROBE framework. Next we move our attention to AEAD schemes based on the Duplex, namely SpongeWrap, which is the basis for NIST’s Lightweight Cryptography standard Ascon. We harness the power of indifferentiability by establishing that SpongeWrap offers security against key-dependent message inputs, related-key attacks, and is also committing.
Generic Security of the SAFE API and Its Applications

We provide security foundations for SAFE, a recently introduced API framework for sponge-based hash functions tailored to prime-field-based protocols. SAFE aims to provide a robust and foolproof interface, has been implemented in the Neptune hash framework and some zero-knowledge proof projects, but despite its usability and applicability it currently lacks any security proof. Such a proof would not be straightforward as SAFE abuses the inner part of the sponge and fills it with protocol-specific data. In this work we identify the SAFECore as versatile variant sponge construction underlying SAFE, we prove indifferentiability of SAFECore for all (binary and prime) fields up to around $$|\mathbb {F}_p |^{c/2}$$ queries, where $$\mathbb {F}_p $$ is the underlying field and c the capacity, and we apply this security result to various use cases. We show that the SAFE-based protocols of plain hashing, authenticated encryption, verifiable computation, non-interactive proofs, and commitment schemes are secure against a wide class of adversaries, including those dealing with multiple invocations of a sponge in a single application. Our results pave the way of using SAFE with the full taxonomy of hash functions, including SNARK-, lattice-, and x86-friendly hashes.
Impact of Secure Container Runtimes on File I/O Performance in Edge Computing

Containers enable high performance and easy deployment due to their lightweight architecture, thus facilitating resource utilization in edge computing nodes. Secure container runtimes have attracted significant attention because of the necessity for overcoming the security vulnerabilities of containers. As the runtimes adopt an additional layer such as virtual machines and user-space kernels to enforce isolation, the container performance can be degraded. Even though previous studies presented experimental results on performance evaluations of secure container runtimes, they lack a detailed analysis of the root causes that affect the performance of the runtimes. This paper explores the architecture of three secure container runtimes in detail: Kata containers, gVisor, and Firecracker. We focus on file I/O, which is one of the key aspects of container performance. In addition, we present the results of the user- and kernel-level profiling and reveal the major factors that impact the file I/O performance of the runtimes. As a result, we observe three key findings: (1) Firecracker shows the highest file I/O performance as it allows for utilizing the page cache inside VMs, and (2) Kata containers offer the lowest file I/O performance by consuming the largest amount of CPU resources. Also, we observe that gVisor scales well as the block size increases because the file I/O requests are mainly handled by the host OS similar to native applications.
Development of Essential Competences for the Success of Inclusive Quality Teaching–Learning Processes in Higher Education

Currently, the development of key competences has become a fundamental priority to ensure the success of inclusive quality teaching–learning processes at all levels of education. This research proposes a quantitative observational study that involved 446 Spanish, Chilean and Mexican individuals, using the INNOVAPRENDE questionnaire. Education is fundamental to provide citizens with the skills inside and outside the classroom. For this reason, the research focuses on the teachers’ perception of university students’ skills to develop Personal, Social and Learning to Learn (PSLL) competence: initiating learning, managing time, managing information and managing self-regulated learning. It was found that women (vs. men), older participants (vs. younger participants) and participants with 16–25 years of experience (vs. those with 0–5 years of experience) perceive that university students have greater knowledge and skills to deploy PSLL. The conclusion highlights the importance of teacher guidance and support, as well as expectations in relation to learners’ development of the skills that make up PSLL.
Leveraging Ensemble Learning with Generative Adversarial Networks for Imbalanced Software Defects Prediction

Software defect prediction is an active research area. Researchers have proposed many approaches to overcome the imbalanced defect problem and build highly effective machine learning models that are not biased towards the majority class. Generative adversarial networks (GAN) are one of the state-of-the-art techniques that can be used to generate synthetic samples of the minority class and produce a balanced dataset. However, it was not investigated thoroughly in the area of imbalanced defect prediction. In this paper, we proposed to combine GAN-based methods with boosting ensembles to yield robust defect prediction models. GAN-based methods were used to balance the defect datasets, and the AdaBoost ensemble was employed to classify the modules into defective and non-defective modules. Our proposed approach was investigated within the context of 10 software defect datasets with different imbalance ratios. Wilcoxon effect size and Scott–Knott effect size difference tests were used as statistical tests to quantify the model’s performance differences statistically. Empirical results indicated that GAN-based methods need hyperparameter optimization when used for imbalanced software defect prediction. In comparison to the traditional sampling techniques, GAN methods outperformed all traditional techniques when used for imbalanced defect prediction. Lastly, results demonstrated that GAN-based methods should not be combined with undersampling to handle imbalance problems.
Minimal Linear Codes Constructed from Sunflowers

Sunflower in coding theory is a class of important subspace codes and can be used to construct linear codes. In this paper, we study the minimality of linear codes over Fq constructed from sunflowers of size s in all cases. For any sunflower, the corresponding linear code is minimal if s≥q+1, and not minimal if 2≤s≤3≤q. In the case where 3<s≤q, for some sunflowers, the corresponding linear codes are minimal, whereas for some other sunflowers, the corresponding linear codes are not minimal.
Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation

We present our work to develop digital objects to represent and convey a specific category of scientific knowledge: computable biomedical knowledge (CBK). Properly developed, validated, implemented and stewarded, CBK has the potential to accelerate the translation of actionable knowledge from scientific discovery to clinical application. Our research takes an infrastructural approach to CBK, initially by focusing on the creation of a conceptual model for packaging computable biomedical knowledge - the Knowledge Object (KO) - and on corresponding efforts to create an architecture for KO management and implementation. Additionally, our work is grounded in the FAIR principles, such that KO artefacts should be findable, accessible, interoperable and reusable and we are exploring aligning KOs with emerging best practices for FAIR Digital Objects (FDO). The outcomes of this work resonate in clinical contexts, health professions education, healthcare quality improvement, biomedical and translational research and population care. Our KO model is also of interest to researchers and practitioners interested in knowledge science, including those working with semantic technologies and other forms of digital objects.
Improved Jaya Algorithm with Inertia Weight Factor

The newly established Jaya algorithm has a simple structure and just requires a small set of control parameters to be optimized. Though it has scope of improvement in terms of outcome accuracy with a fast convergence speed, intensification (exploitation), and diversification (exploration). This work suggests an enhanced variant of the Jaya algorithm, named “W-Jaya,” by setting up a time-varying inertia weight factor with it. This method offers high-quality solutions in a reasonable timeframe. Further, W-Jaya tested with 10 constrained and 20 unconstrained test problems taken from the literature. Finally, the comparison and results show that the proposed W-Jaya has obtained desire goals successfully in an effective manner.
Analisis Jalur Terdekat Menuju Bangunan Shelter Evakuasi Tsunami di Kota Padang Menggunakan Network Analyst

West Sumatra is one of the provinces in Indonesia that is prone to earthquakes due to the Great Sumatran Fault and the convergence of the Indo-Australian Plate and the Eurasian Plate beneath the sea to the west of the island of Sumatra. Padang is a city with earthquake potential due to the Semangko Fault, and one of the potential consequences is the occurrence of tsunamis. To minimize these impacts in Padang, tsunami evacuation buildings, known as shelters, have been established. Therefore, it is crucial for the community to be aware of the nearest routes to these evacuation shelters in Padang. One method employed is using Network Analyst. Network Analyst is a tool within the ArcGIS software, which is a Geographic Information System (GIS) developed by ESRI to manage, analyze, and visualize geographical data. By utilizing Network Analyst, the closest routes to the researched areas are generated. Network Analyst employs the Dijkstra's algorithm to solve routing problems and can be generated based on two criteria: distance and time. The result of the nearest route to the tsunami evacuation shelter in Batang Arau Subdistrict, South Padang District, is from SDN 29 Pebayan Penggalangan to Nurul Iman Mosque Padang, spanning a distance of 1585 meters. The analysis of the nearest route using Network Analyst is then compared with manually calculated results using the Dijkstra's algorithm. The difference between manual calculations and Network Analyst results ranges from 1 to 4 meters, approaching reality. This information can serve as a reference for determining the nearest routes to evacuation shelters in the event of a tsunami in Padang.
Comparison of Learning Content Representations to Improve L2 Vocabulary Acquisition Using m-learning

Previous works reveal that there is potential in the use of mobile devices as a useful tool to help learn new vocabulary and it allows the learning materials can be displayed with different Learning Content Representation (LCR) types. Nevertheless, there are no conclusive results about which LCR type is better to improve L2 vocabulary acquisition using m-learning. The purpose of this study was to explore the effects of improving the students’ academic performance using two LCR types that promote different learning strategies in a vocabulary learning task. A mobile application with two LCR types was implemented and tested: the SRL type based on a self-regulated learning strategy, and the NSRL type based on a non-self-regulated learning strategy. Quantitative analyses indicated that there is a relationship between the LCR types and the word recall on vocabulary learning. The SRL type seems to be effective in improving the students’ learning abilities and students in this experimental group exhibited significantly better academic performance. The results are meant to draw that LCR-SRL types must be used in m-learning to L2 vocabulary acquisition. Future studies should focus on how to integrate these strategies in m-learning and understand their effects on improving students’ vocabulary acquisition.
Digital Rage: Testing “the Obama Effect” on Internet-Based Expressions of Racism

The concept, “White Rage,” has previously been used to describe the way Whites have historically responded to Black advancement with policies and practices designed to quietly disrupt the progress Blacks had been making. White rage is typically subtle, masking its true intent. In contrast, recent research has found that the covert, subtle expressions of racism that are so normal in most mainstream spaces may be less common in internet-based communication. The extent to which online racism is connected to real-world racist attitudes, behaviors, and events, however, is unclear. In this article, we test the effects of real-world racialized events on explicit expressions of racism in online spaces using days that Obama gave speeches as our treatment effect and explicit usage of the “n-word” on the social media platform X (formerly Twitter) as our measurable outcome. Does usage of the n-word, a racial slur, increase in the days following speeches made by President Obama? Our results of over 9 years and more than 2.9 million tweets demonstrate a statistically significant increase of racist speech in response to those speech cycles, which are further placed in contrast to the speeches of other political actors, including President Trump.
Antithesis of Human Rater: Psychometric Responding to Shifts Competency Test Assessment Using Automation (AES System)

This research is part of proof tests to a combination of statistical processing methods, collecting assessment rubrics in vocational education by comparing two systems, automated essay scoring and human rater. It aims to analyze the final assessment score of essays in Akademi Komunitas Negeri (AKN) Pacitan (Pacitan’s State Community College) and Akademi Komunitas Negeri (AKN) Blitar (Blitar’s State Community College) in East Java, Indonesia. The provisional assumption is that the results show an antithesis to the assessment of human feedback with an automated system due to the conversion of scores between the rubric and the algorithm design. As the hypothesis, algorithm-based score conversion affects automated essay scoring and human rater methods, which led to antithesis feedback. The validity and reliability of the measurement maintain the scoring consistency between the two methods and the accuracy of the answers. The novelty of this article is comparing between AES system and Human Rater using statistical methods. The research shows that there is a similar result using the psychometrics approach, which indicates different metaphor expressions and language systems. Thus, the objective of this study is to provide assistance in the advancement of an information technology system that utilizes a scoring mechanism merging computer and human evaluations, employing a psychological approach known as psychometric leads.