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We give an overview of our critiques of "proofs" of security and a guide to our papers on the subject that have appeared over the past decade and a half. We also provide numerous additional examples and a few updates and errata.

In this paper, the differences between the virtual laboratories and the bench laboratory were analyzed and the advantages and disadvantages of the virtual laboratory were established. Search was based on the three laboratory works were developed and carried out through approbation. There were two focus groups of students for analyzing the level of students after their work with the bench and virtual laboratories. As the result the advantages and disadvantages of the virtual laboratory are cleared out. The disadvantage is in having not of opportunity of students to work by hand. Advantages: during work, if the student was unable to complete the work, he can finish it another time. With the help of a virtual program the work is done very quickly. This program does not consume much memory in the computer. Providing the сabinet with bench equipment is more expensive. And the testing shows that if a student additionally works with a virtual program, his level of education increases. Virtual laboratory work are very important and the level of knowledge of students has increased.

In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise.

Numerical tables are widely used to present experimental results in scientific papers. For table understanding, a metric-type is essential to discriminate numbers in the tables. We introduce a new information extraction task, metric-type identification from multi-level header numerical tables, and provide a dataset extracted from scientific papers consisting of header tables, captions, and metric-types. We then propose two joint-learning neural classification and generation schemes featuring pointer-generator-based and BERT-based models. Our results show that the joint models can handle both in-header and out-of-header metric-type identification problems., To appear at EACL 2021

This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends. An increasing number of MT evaluations exclusively rely on differences between BLEU scores to draw conclusions, without performing any kind of statistical significance testing nor human evaluation, while at least 108 metrics claiming to be better than BLEU have been proposed. MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable. Furthermore, tools for reporting standardized metric scores are still far from being widely adopted by the MT community. After showing how the accumulation of these pitfalls leads to dubious evaluation, we propose a guideline to encourage better automatic MT evaluation along with a simple meta-evaluation scoring method to assess its credibility., Comment: Camera-ready for ACL2021

Coreference resolution is essential for automatic text understanding to facilitate high-level information retrieval tasks such as text summarisation or question answering. Previous work indicates that the performance of state-of-the-art approaches (e.g. based on BERT) noticeably declines when applied to scientific papers. In this paper, we investigate the task of coreference resolution in research papers and subsequent knowledge graph population. We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55,485 papers in 10 STM domains. Comprehensive experiments show the usefulness of the proposed approach. Our transfer learning approach considerably outperforms state-of-the-art baselines on our corpus with an F1 score of 61.4 (+11.0), while the evaluation against a gold standard KG shows that coreference resolution improves the quality of the populated KG significantly with an F1 score of 63.5 (+21.8).

Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent “best practices” around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a “ground truth” or “gold standard” of training data is available, it is especially relevant to discuss issues around the equally important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise.

In order to effectively utilize the dielectric response characteristics of transformers to diagnose the insulation state, this paper proposes a two-level hybrid optimization method for analyzing time-domain dielectric response characteristics. The optimization algorithm is based on the combined statistical indicators (CSI) and random forest (RF) theory. The initial feature space set is formed with 23 time-domain characteristics. In the first-level stage, statistical indices correlation, distance, and information indicator are integrated to assess the synthesis score of the characteristics, while highly redundant and low-class discrimination characteristics are eliminated from the initial space set. In the second-level stage, Random Forest based outside bagging data theory is introduced to evaluate the least important characteristics, and the characteristics with low importance indices are excluded to obtain the final optimal feature space set. The proposed method is carried out on 82 sets of data from actual dielectric response tests on oil-paper insulation transformers. Finally, the final optimal feature space set, along with several other data sets, is tested via different diagnosis methods. The results show that the optimal feature space set obtained via the proposed method outperforms other feature space sets in terms of better adaptability and diagnosis accuracy.

Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections. In this paper, we present KPRank, an unsupervised graph-based algorithm for keyphrase extraction that exploits both positional information and contextual word embeddings into a biased PageRank. Our experimental results on five benchmark datasets show that KPRank that uses contextual word embeddings with additional position signal outperforms previous approaches and strong baselines for this task.

This White Paper explores the legal dimensions of the European Union (EU)’s value-driven cybersecurity by investigating the notions of ‘value-driven’ and ‘cybersecurity’ from the perspective of EU law. It starts with a general overview of legal issues in current value-driven cybersecurity debates (Chapter 2), showing how values embedded within the framework of EU governing treaties have evolved during the integration process, and the important role they play in the cybersecurity regulation at EU level. Chapter 3 of the White Paper is devoted to the main critical challenges in this area: 1) the varied and sometimes unclear uses of the term ‘cybersecurity’, 2) the roles of stakeholders and the cooperation between them, and the 3) securitization of EU values and interests through cybersecurity rules. Chapter 4 points out and describes specific controversies concerning cybersecurity regulation in the EU. Ten disputed issues are given particular attention: 1) the functioning of human rights as drivers for EU regulation, 2) the regulation of risks to society through individual risk identification and proactive action, 3) the attribution of roles to different stakeholders, 4) how individuals are being awarded with more rights, 5) controllership of data, 6) copyright protection, 7) regulation of online content, 8) the use of encryption, 9) permissibility of massive and generalised surveillance of individuals and 10) counterterrorism measures. Chapter 5 summarises the main findings of the literature review. The White Paper recognises that legislative and policy measures within the cybersecurity domain challenge EU fundamental rights and principles, stemming from EU values. The White Paper concludes that with the constantly growing number of EU measures governing the cybersecurity domain, the embedment of EU values enshrined in the EU Charter within these measures take place both on an ex ante and an ex post basis.

Paper-based microfluidic devices have many applications in biomedical and environmental analysis. However, the process of prototyping device designs can be tedious, error-prone, and time-consuming. Here, we present a cross-platform, open-source software tool—AutoPAD—developed to quickly create and modify device designs and provide a free alternative to commercial design software. The capabilities that we designed to be inherent to AutoPAD (e.g., automatic zone alignment and design refactoring) highlight its potential use in nearly any paper-based microfluidic device application and for creating nearly any desired design, which we demonstrate through the recreation of numerous device designs from the literature.

The Panama Papers is a collection of 11.5 million leaked records that contain information for more than 214,488 offshore entities. This collection is growing rapidly as more leaked records become available online. In this paper, we present a work in progress on a web browser plugin that detects company names from the Panama Papers and alerts the user by means of unobtrusive visual cues. We matched a random sample of company names from the Public Works and Government Services Canada registry against the Panama Papers using three different string matching techniques. Monge-Elkan is found to provide the best matching results but at increased computational cost. Levenshtein-based approach is found to provide the best tradeoff between matching and computational cost, while Jacquard index like approach is found to be less sensitive to slight textual change.

This White Paper summarises currently available empirical data about attitudes and opinions of citizens and state actors regarding cybersecurity. The data emerges from reports of EU projects, Eurobarometer surveys, policy documents of state actors and additional scientific papers. It describes what these stakeholders generally think, what they feel, and what they do about cyber threats and security (counter)measures. For citizens’ perspectives, three social spheres of particular interest are examined: 1) health, 2) business, 3) police and national security.

The authors of this paper examined how paper-based vs. computer-based modes of learning affected student performance on TOEIC vocabulary tests for known and unknown words. Japanese university students were tested on two different TOEIC vocabulary registers to see how the mode of learning affected student performance on vocabulary tests. The results show all students, including higher ability students, perform better on paper-based over computer-based tests and this performance is carried through all levels of ability until students become familiar with the vocabulary, and their cognitive load is decreased. This indicates that all students when challenged by new and unknown data would initially perform better with paper-based modes of learning. This has important implications for the teaching of new TOEIC vocabulary both at the institutional and classroom level.

This White Paper outlines how the ethical discourse on cybersecurity has developed in the scientific literature, which ethical issues gained interest, which value conflicts are discussed, and where the “blind spots” in the current ethical discourse on cybersecurity are located. The White Paper is based on an extensive literature with a focus on three reference domains with unique types of value conflicts: health, business/finance and national security. For each domain, a systematic literature search has been performed and the identified papers have been analysed using qualitative and quantitative methods. An important observation is that the ethics of cybersecurity not an established subject. In all domains, cybersecurity is recognized as being an instrumental value, not an end in itself, which opens up the possibility of trade-offs with different values in different spheres. The most prominent common theme is the existence of trade-offs and even conflicts between reasonable goals, for example between usability and security, accessibility and security, privacy and convenience. Other prominent common themes are the importance of cybersecurity to sustain trust (in institutions), and the harmful effect of any loss of control over data.

Recently an unlinkable version of the U-Prove attribute-based credential scheme was proposed at Financial Crypto’14 [9]. Unfortunately, the new scheme is forgeable: if sufficiently many users work together then they can construct new credentials, containing any set of attributes of their choice, without any involvement of the issuer. In this note we show how they can achieve this and we point out the error in the unforgeability proof.

In this paper, we discuss the toilet paper automatic exchanging system that we have developed and improvement. This system can exchange toilet paper in automatically. The system measures color of the toilet paper and its core by using the color sensor. The system judges the toilet paper is already used up or not based on measured RGB value. If the system judged the toilet paper is already used up, the system exchanges toilet paper. This system also can stock some spare toilet paper in the back of the system. The system measures the number of spare toilet paper and sends its data to web server. The toilet administrator or cleaning company can check the rest of toilet spare toilet paper through web browser. Therefore, the administrator can provide toilet paper in efficiently. By using developed system, toilet administrator or cleaning company replenishes toilet paper only and user who uses the restroom doesn't need to exchange toilet paper.

The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinical practices. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional “map” called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding ofthe field.

This paper reflects the technology-induced novelty of translation, which is perceived as a bridge between languages and cultures. We debate the extent to which the translation process maintains its specificity in the light of the new technology-enhanced working methods ensured by a large variety of Computer-Assisted Translation (CAT) and Machine Translation (MT) tools that aim to enhance the process, which includes the translation itself, the translator, the translation project manager, the linguist, the terminologist, the reviewer, and the client. This paper also hints at the topic from the perspective of the translation teacher, who needs to provide students with transversal competencies that are suitable for the digital area, supported by the ability to tackle Cloud-based translation tools, in view of Industry 4.0 requirements.

This paper presents an attack and defence modelling framework for conceptualising the security of the speech interface. The modelling framework is based on the Observe-Orient-Decide-Act (OODA) loop model, which has been used to analyse adversarial interactions in a number of other areas. We map the different types of attacks that may be executed via the speech interface to the modelling framework, and present a critical analysis of the currently available defences for countering such attacks, with reference to the modelling framework. The paper then presents proposals for the development of new defence mechanisms that are grounded in the critical analysis of current defences. These proposals envisage a defence capability that would enable voice-controlled systems to detect potential attacks as part of their dialogue management functionality. In accordance with this high-level defence concept, the paper presents two specific proposals for defence mechanisms to be implemented as part of dialogue management functionality to counter attacks that exploit unintended functionality in speech recognition functionality and natural language understanding functionality. These defence mechanisms are based on the novel application of two existing technologies for security purposes. The specific proposals include the results of two feasibility tests that investigate the effectiveness of the proposed mechanisms in defending against the relevant type of attack.

The omnipresent pervasiveness of personal devices will expand the applicability of the Dynamic Data Driven Application Systems (DDDAS) paradigm in innumerable ways. While every single smartphone or wearable device is potentially a sensor with powerful computing and data capabilities, privacy and security in the context of human participants must be addressed to leverage the infinite possibilities of dynamic data driven application systems. We propose a security and privacy preserving framework for next generation systems that harness the full power of the DDDAS paradigm while (1) ensuring provable privacy guarantees for sensitive data; (2) enabling field-level, intermediate, and central hierarchical feedback-driven analysis for both data volume mitigation and security; and (3) intrinsically addressing uncertainty caused either by measurement error or security-driven data perturbation. These thrusts will form the foundation for secure and private deployments of large scale hybrid participant-sensor DDDAS systems of the future.

Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces. Most of the studies have been related to use of a single coating material over a single substrate or using a single technique. The degree of hydrophobicity is highly dependent on fabrication processes as well as materials being coated and as such warrants a high-level study using experimental optimization leading to the evaluation of the parametric behavior of coatings and their application techniques. Also, a single platform or system which can predict the required set of parameters for generating hydrophobic surface of required nature for given substrate is of requirement. This work applies the powerful machine learning algorithms (Levenberg Marquardt using Gauss Newton and Gradient methods) to evaluate the various processes affecting the anti-wetting behavior of coated printable paper substrates with the capability to predict the most optimized method of coating and materials that may lead to a desirable surface contact angle. The major application techniques used for this study pertain to dip coating, spray coating, spin coating and inkjet printing and silane and sol–gel base coating materials.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

Using machine learning (ML) services, both service customers and providers need to monitor complex contractual constraints of ML service that are strongly related to ML models and data. Therefore, establishing and monitoring comprehensive ML contracts are crucial in ML serving. This paper demonstrates a set of features and utilities of the QoA4ML framework for ML contracts.

—Industries have a significant amount of data in semi-structured and unstructured formats which are typically captured in text documents, spreadsheets, images, etc. This is especially the case with the software description documents used by domain experts in the automotive domain to perform tasks at various phases of the Software Development Life Cycle (SDLC). In this paper, we propose an end-to-end pipeline to extract an Automotive Knowledge Graph (AutoKG) from textual data using Natural Language Processing (NLP) techniques with the application of automatic test case generation. The proposed pipeline primarily consists of the following components: 1) AutoOntology, an ontology that has been derived by analyzing several industry scale automotive domain software systems, 2) AutoRE, a Relation Extraction (RE) model to extract triplets from various sentence types typically found in the automotive domain, and 3) AutoVec, a neural embedding based algorithm for triplet matching and context-based search. We demonstrate the pipeline with an application of automatic test case generation from requirements using AutoKG.

Autism spectrum conditions (ASC) develop early in life and are thought to last a lifetime. However, ASC research has two major knowledge gaps that hinder progression in understanding the concept of ASC and in providing proper support for autistic adults: 1) the majority of knowledge about ASC mainly stems from childhood studies so little is known about older autistic adults; 2) while it is broadly recognized that ASC is a heterogeneous condition, we do not yet understand the differences in trajectories leading to their future outcome. We aim to fill both knowledge gaps.A multistage overlapping cohorts design assessing (cognitive) aging in ASC, is designed to obtain an accelerated longitudinal dataset. Data, including a multitude of questionnaires, diagnostics, and cognitive tests, are collected over four waves within a 10-year time frame. This will provide information regarding actual changes in quality of life, co-occurring health conditions, and cognition as well as the possibility to test external validity and temporal stability in newly formed behavioral subtypes. Participants consist of three groups of adults aged 20 to 90 years: 1) with a clinical diagnosis of ASC; 2) with a clinical diagnosis of ADHD, but no ASC; 3) no ASC/ADHD (i.e., comparison group; COM). The sample size differs between waves and instruments. Detailed analysis plans will be preregistered in AsPredicted or at the Open Science Framework. Ethical approval for this study was obtained from the ethical review board of the Department of Psychology of the University of Amsterdam (Wave 1 2011-PN-1952 and 2013-PN-2668, Wave 2 2015-BC-4270, Wave 3 and 4 2018-BC-9285). In line with the funding policies of the grant organization funding this study, future papers will be published open access.

International audience; Whenever a bug or vulnerability is detected in the Linux kernel, the kernel developers will endeavour to fix it by introducing a patch into the mainline version of the Linux kernel source tree. However, many users run older "stable" versions of Linux, meaning that the patch should also be "backported" to one or more of these older kernel versions. This process is error-prone and there is usually a long delay in publishing the backported patch. Based on an empirical study, we show that around 8% of all commits submitted to Linux mainline are backported to older versions, but often more than one month elapses before the backport is available. Hence, we propose a patch backporting technique that can automatically transfer patches from the mainline version of Linux into older stable versions. Our approach first synthesizes a partial transformation rule based on a Linux mainline patch. This rule can then be generalized by analysing the alignment between the mainline and target versions. The generalized rule is then applied to the target version to produce a backported patch. We have implemented our transformation technique in a tool called FixMorph and evaluated it on 350 Linux mainline patches. FixMorph correctly backports 75.1% of them. Compared to existing techniques, FixMorph improves both the precision and recall in backporting patches. Apart from automation of software maintenance tasks, patch backporting helps in reducing the exposure to known security vulnerabilities in stable versions of the Linux kernel.

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Dear Prospective Authors: I am delighted to inform you that this game-changing publishing initiative for bringing the best actionable insights on cybersecurity is entering its third year. The revie...

International audience; The task of tagging papers with semantic metadata in order to analyze their relatedness represents a good foundation for a paper recommender system. The analysis from this paper extends from previous research in order to create a graph of papers from a specific domain with the purpose of determining each article's importance within the considered corpus of papers. Moreover, as non-latent representations are powerful when used in conjunction with latent ones, our system retrieves semantically close words, not present in the paper, in order to improve the retrieval of papers. Our previous analyses used the semantic representation of papers in different semantic models with the purpose of creating visual graphs based on the semantic relatedness links between the abstracts. The current analysis takes a step forward by proposing a model that can suggest which papers are of the highest relevance, share similar concepts, and are semantically related with the initial query. Our study is performed using paper abstracts in the field of information technology extracted from the Web of Science citation index. The research includes a use case and its corresponding results by using interactive and exploratory network graph representations.

International audience; The paper presents a new segmentation method for Integrated ICS (Industrial Control Systems) with Corporate system. This new method aims at simplifying security zones identification by focusing only on the system's aspects that are really relevant for segmentation taking into account the system's constraints. Multiple research works have studied IICS (Inte-grated ICS) segmentation but their solutions are unfortunately not generic enough and do not sufficiently take into account all of the Integrated ICS specificity. Our new method tries to address the problem more efficiently by providing realistic and pragmatic answers to the issue while remaining sufficiently generic to be applied to different types of Integrated ICS.

The main objective of the current research was to evaluate aspects of the process of reading of printed text and of computerized text by considering students of Communication and Journalism of the University of Manizales, Colombia and students of Foreign Languages Department of Skovoroda Kharkiv National Pedagogical University, Ukraine. Statistically significant differences were found in the number of weekly hours of computer use by students according to the participating universities (p = 0.00). The analysis also determined significant differences between the grades received by those who used paper (p = 0.000) and digital (p = 0.000), with the results being higher in both cases for the students of Foreign Languages Department of Skovoroda Kharkiv National Pedagogical University- NPUK (Statistician U of Mann- Whitney).

Recently, high-speed IPv4 scanners, such as ZMap, have enabled rapid and timely collection of TLS certificates and other security-sensitive parameters. Such large datasets led to the development of the Censys search interface, facilitating comprehensive analysis of TLS deployments in the wild. Several recent studies analyzed TLS certificates as deployed in web servers. Beyond public web servers, TLS is deployed in many other Internet-connected devices, at home and enterprise environments, and at network backbones. In this paper, we report the results of a preliminary analysis using Censys on TLS deployments in such devices (e.g., routers, modems, NAS, printers, SCADA, and IoT devices in general). We compare certificates and TLS connection parameters from a security perspective, as found in common devices with Alexa 1M sites. Our results highlight significant weaknesses, and may serve as a catalyst to improve TLS security for these devices.

With the purpose of delivering more robust systems, this paper revisits the problem of Inverse Uncertainty Quantification that is related to the discrepancy between the measured data at runtime (while the system executes) and the formal specification (i.e., a mathematical model) of the system under consideration, and the value calibration of unknown parameters in the model. We foster an approach to quantify and mitigate system uncertainty during the development cycle by combining Bayesian reasoning and online Model-based testing.

This special section presents an updated version of the paper that won the first prize award in the ITU (International Telecommunication Union) Kaleidoscope conference (K-2020).

We recently renamed our “education” track’s papers from Education Pearl to Education Matters. This would be a good time to explain our vision for the track as a whole, as well as this specific renaming.

Proof assistants are getting more widespread use in research and industry to provide certified and independently checkable guarantees about theories, designs, systems and implementations. However, proof assistant implementations themselves are seldom verified, although they take a major share of the trusted code base in any such certification effort. In this area, proof assistants based on Higher-Order Logic enjoy stronger guarantees, as self-certified implementations have been available for some years. One cause of this difference is the inherent complexity of dependent type theories together with their extensions with inductive types, universe polymorphism and complex sort systems, and the gap between theory on paper and practical implementations in efficient programming languages. MetaCoq is a collaborative project that aims to tackle these difficulties to provide the first fully-certified realistic implementation of a type checker for the full calculus underlying the Coq proof assistant. To achieve this, we refined the sometimes blurry, if not incorrect, specification and implementation of the system. We show how theoretical tools from this community such as bidirectional type-checking, Tait-Martin-L\"of/Takahashi's confluence proof technique and monadic and dependently-typed programming can help construct the following artefacts: a specification of Coq's syntax and type theory, the Polymorphic Cumulative Calculus of (Co)-Inductive Constructions (PCUIC); a monad for the manipulation of raw syntax and interaction with the Coq system; a verification of PCUIC's metatheory, whose main results are the confluence of reduction, type preservation and principality of typing; a realistic, correct and complete type-checker for PCUIC; a sound type and proof erasure procedure from PCUIC to untyped lambda-calculus, i.e., the core of the extraction mechanism of Coq., Comment: In Proceedings LFMTP 2021, arXiv:2107.07376

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.