Reviewerly/exHarmony huggingface dataset:
Could You Understand Me? The Relationship among Method Complexity, Preprocessing Complexity, Interpretability, and Accuracy

The need to train experts who will be able to apply machine learning methods for knowledge discovery is increasing. Building an effective machine learning model requires understanding the principle of operation of the individual methods and their requirements in terms of data pre-preparation, and it is also important to be able to interpret the acquired knowledge. This article presents an experiment comparing the opinion of the 42 students of the course called Introduction to Machine Learning on the complexity of the method, preprocessing, and interpretability of symbolic, subsymbolic and statistical methods with the correctness of individual methods expressed on the classification task. The methodology of the implemented experiment consists of the application of various techniques in order to search for optimal models, the accuracy of which is subsequently compared with the results of a knowledge test on machine learning methods and students’ opinions on their complexity. Based on the performed non-parametric and parametric statistic tests, the null hypothesis, which claims that there is no statistically significant difference in the evaluation of individual methods in terms of their complexity/demandingness, the complexity of data preprocessing, the comprehensibility of the acquired knowledge and the correctness of the classification, is rejected.
Software Development and Tool Support for Curriculum Design: A Systematic Mapping Study

Curriculum design is the systematic process of establishing how a learning process is designed, developed, supported, and delivered. This process is supported by software tools which can help improve curriculum alignment and facilitate the design of courses or programs. This article aims to analyze software proposals for curriculum design support that consider using models, methods, and techniques in software development. To do this, a systematic mapping of studies was conducted, including six research questions. This study includes 45 articles published from 2011 to 2022. The results indicate that 60% use some model, method, or technique in software development. Most software uses some models, such as ontologies, UML diagrams, or IMS-1D models. Although most articles use some model, method, or technique, there is a lack of use of software engineering models such as UML diagrams, which are standard in the software industry and research.
Lifted Stackelberg Planning

In Stackelberg planning, a leader and a follower each choose a plan in the same planning task, the leader's objective being to maximize plan cost for the follower. This formulation naturally captures, among others, security-related scenarios where the leader defends an infrastructure against subsequent attacks by the follower. Indeed, Stackelberg planning has been applied to the analysis of email infrastructure security. At web scale, however, the planning tasks involved easily contain tens of thousands of objects, so that grounding becomes the bottleneck. Here we introduce a lifted form of Stackelberg planning to address this. We devise leader-follower search algorithms working at the level of the PDDL-style input model to the extent possible. Our experiments show that, in Stackelberg tasks with many objects, including in particular models of web infrastructure security, our lifted algorithms outperform grounded Stackelberg planning.
Improved Exploration of the Bench Transition System in Parallel Greedy Best First Search

While parallelization of A* is fairly well-understood, parallelization of GBFS has been much less understood. Recent work has proposed PUHF, a parallel GBFS which restricts search to exploration of the Bench Transition System (BTS), which is the set of states that can be expanded by GBFS under some tie-breaking policy. However, PUHF causes threads to spend much of the time waiting so that only states which are guaranteed to be in the BTS are expanded. We propose improvements to PUHF which significantly reduce idle time and allow more rapid exploration of the BTS, resulting in better search performance.
Towards an Effective Framework Combining Planning and Scheduling [Extended Abstract]

In a nutshell, Automated Planning deals with finding sequences of actions that achieve a required goal while scheduling deals with allocating activities on (limited) resources meeting specified constraints. Activities, however, might resemble actions in planning as we might capture what they can produce and under what conditions. That said, the "planning'' part represents selecting proper activities as well as their ordering which the "scheduling'' part represents allocating the activities to the resources. This extended abstract formalises the concept of "combined" planning and scheduling tasks and proposes the idea how these tasks can be compiled to classical planning tasks. Our idea is evaluated on tasks involving scheduling activities on reconfigurable machines.
Planning in Multi-Agent Domains with Untruthful Announcements

Earlier epistemic planning systems for multi-agent domains generate plans that contain various types of actions such as ontic, sensing, or announcement actions. However, none of these systems consider untruthful announcements, i.e., none can generate plans that contain a lying or a misleading announcement. In this paper, we present a novel epistemic planner, called EFP3.0, for multi-agent domains with untruthful announcements. The planner is similar to the systems EFP or EFP2.0 in that it is a forward-search planner and can deal with unlimited nested beliefs and common knowledge by employing a Kripke based state representation and implementing an update model based transition function. Different from EFP, EFP3.0 employs a specification language that uses edge-conditioned update models for reasoning about effects of actions in multi-agent domains. We describe the basics of EFP3.0 and conduct experimental evaluations of the system against state-of-the-art epistemic planners. We discuss potential improvements that could be useful for scalability and efficiency of the system.
Operator Pruning Using Lifted Mutex Groups via Compilation on Lifted Level

A lifted mutex group is a schematic first-order description of sets of facts such that each set contains facts out of which at most one can hold in any reachable state. It was previously shown that lifted mutex groups can be used for pruning of operators during grounding of PDDL tasks, i.e., it is possible to prune unreachable and dead-end operators even before the grounded representation is known. Here, we show that applying such a pruning technique does not require a modification of the grounding procedure. Instead, it is possible to compile the conditions under which we can use lifted mutex groups to prune operators directly into the preconditions of lifted actions on the PDDL level. In fact, we show that such compilation captures the pruning power of lifted mutex groups perfectly.
Clandestine communications in cyber-denied environments

Both intelligence operatives and criminals have a constant need to be able to communicate clandestinely, circumventing surveillance efforts carried out by highly capable adversaries. The recent highly-publicized breaches of internet-based clandestine communications technology and targeted malware attacks, in combination with increasingly sophisticated methods for surveillance of internet traffic has arguably resulted in a cyber-denied environment. This paper employs a red-teaming approach to explore how clandestine communications can be structured using platforms that are physically separated from the internet and thus not vulnerable to internet-based surveillance or attacks. Recent developments in computer-based radio software can be combined with legacy radio technology to provide robust solutions for clandestine communications in a cyber-denied environment. Drawing on case studies from the Cold War, contemporary observations of clandestine radio networks in use today, and technical tests carried out by the authors, this paper stresses the importance for counterintelligence and law enforcement to be prepared for a potential shift in how clandestine communications are implemented by both hostile intelligence services and organized crime. Finally, the paper addresses the issue of proactively countering these techniques by presenting concrete methods for use by counterintelligence and law enforcement to detect radio-based clandestine communications and secure evidence.
CRT-Based Homomorphic Encryption over the Fraction

Homomorphic encryption technology is the holy grail of cryptography and has a wide range of applications in practice. This paper proposes a homomorphic encryption scheme over the fraction based on the Chinese remainder theorem (CRT) Dayan qiuyi rule. This homomorphic scheme performs encryption and decryption operations by forming congruence groups and has homomorphism. The solution in this paper first combines the traditional CRT algorithm with the Dayan qiuyi rule to obtain the CRTF algorithm that can be operated on the fraction field. Finally, in the decryption process, modulo arithmetic is used twice to obtain the correct plaintext components, restored to plaintext by CRTF. The scheme’s security is related to a decisional version of an approximate GCD problem. The proof of theoretical derivation shows that this paper’s homomorphic encryption scheme can realize the homomorphic addition operation in the fraction field. Compared with the CKKS scheme, efficiency is improved.
Comparative Analysis of Classical and Neural Networks based ChatBot’s Techniques

Conversational agents like Alexa from Amazon, Siri from Apple, Assistant from Google, and Cortana from Microsoft demonstrate extraordinary research and potential in conversational agents. A conversational agent, chatter-bot, or chatbot is a piece of computer software supposed to communicate at a level of intelligence comparable to a person's. Chatbots are designed for various purposes, such as task-oriented helpers and creators of open-ended discourse. Numerous approaches have been studied, from primitive types of hard-coded response generators to contemporary ways of constructing artificial intelligence. These are classified as rule-based or neural network-based systems. Unlike the rule-based technique, which is based on pre-defined templates and responses, the neural network approach is based on deep learning models. Rule-based communication is optimal for more straightforward, task-oriented conversations. Open-domain conversational modeling is a more complicated topic that depends heavily on neural network techniques. This article begins with an overview of chatbots before diving into the specifics of a variety of traditional, rule-based, and neural network-based methods. A table summarising previous field research closes the survey. It looks at the most recent and vital research on the subject, the evaluation instruments used areas for improvement, and the applicability of the proposed methods.
Protocol for the automatic extraction of epidemiological information via a pre-trained language model

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.2.
Accurate white light phase-shifting interferometry under PZT scanning error

White light phase-shifting interferometry (WLPSI) has obvious advantages such as large field of view, high precision and non-contact, so it is widely used in three-dimensional (3-D) topography measurement of micro-nano photoelectric devices. In this paper, the problems for large phase unwrapping error and low phase calculation accuracy under the scanning error of piezoelectric transducer (PZT) in current phase shifting measurement are studied. The novel and effective phase-shifting measurement method under scanning error is proposed. This method can be used to accurately recover the surface phase and complete the surface reconstruction under random scanning error with maximum for 10nm. Finally, the proposed method is verified by simulation and measurement experiments.
Sequential Machine Learning for Activity Sequence Prediction from Daily Work Report Data

It is critical for project owners to have a reasonable estimation of project duration before the letting date for contract time determination and project management purposes. To determine the project duration, highway agencies employ scheduling techniques and arrange activities in sequential order. Activity sequencing is a crucial task since a slight change in the sequence of critical activities can significantly influence project duration. Also, the task of activity arrangement is time-consuming for a broad portfolio of projects and requires skillful schedulers. To aid activity sequence determination, prior studies used project drawings, expert knowledge, and historical data to identify sequence rules, logic templates, and sequence prediction models. However, weaknesses and areas of improvement exist, including a lack of adequately leveraging available historical data, the necessity of human input, reliance on human experience rather than data, and poor detection of the overlapping time of activities. This study proposes a novel framework that predicts the sequences of work activities using historical daily work reports to train a long short-term memory recurrent neural network to predict the activity sequence and overlapping in future projects. The daily work reports of 720 highway projects obtained from a highway agency are used as the case study. A novel evaluation technique based on conditional probability is used to assess the model and compare its output sequence to sequences created randomly. The assessment results indicate that the model’s output is superior in 94.4% of situations, suggesting a high level of model reliability. The impact of key project characteristics such as project work type and size on activity prediction is examined, indicating a significant impact of project work type and no impact of project size on activity prediction. The results of this study can assist highway project owners in activity sequence and overlap determination by entering a series of activities and receiving the likely next successors.
Turning captchas against humanity: Captcha-based attacks in online social media

Nowadays, people generate and share massive amounts of content on online platforms (e.g., social networks, blogs). In 2021, the 1.9 billion daily active Facebook users posted around 150 thousand photos every minute. Content moderators constantly monitor these online platforms to prevent the spreading of inappropriate content (e.g., hate speech, nudity images). Based on deep learning (DL) advances, Automatic Content Moderators (ACM) help human moderators handle high data volume. Despite their advantages, attackers can exploit weaknesses of DL components (e.g., preprocessing, model) to affect their performance. Therefore, an attacker can leverage such techniques to spread inappropriate content by evading ACM. In this work, we analyzed 4600 potentially toxic Instagram posts, and we discovered that 44% of them adopt obfuscations that might undermine ACM. As these posts are reminiscent of captchas (i.e., not understandable by automated mechanisms), we coin this threat as Captcha Attack (CAPA). Our contributions start by proposing a CAPA taxonomy to better understand how ACM is vulnerable to obfuscation attacks. We then focus on the broad sub-category of CAPA using textual Captcha Challenges, namely CC-CAPA, and we empirically demonstrate that it evades real-world ACM (i.e., Amazon, Google, Microsoft) with 100% accuracy. Our investigation revealed that ACM failures are caused by the OCR text extraction phase. The training of OCRs to withstand such obfuscation is therefore crucial, but huge amounts of data are required. Thus, we investigate methods to identify CC-CAPA samples from large sets of data (originated by three OSN – Pinterest, Twitter, Yahoo-Flickr), and we empirically demonstrate that supervised techniques identify target styles of samples almost perfectly. Unsupervised solutions, on the other hand, represent a solid methodology for inspecting uncommon data to detect new obfuscation techniques.
Evaluation of web-based online agricultural information relevant to Tanzanian maize producers

This study examined the quality of web-based online agricultural information relevant to the maize industry in Tanzania. Selected online sources were evaluated to assess the agricultural information available in terms of four dimensions of quality, namely: authority, completeness, timeliness, and understandability. The study identified a wide variety of web-based online information on maize production, including information on seeds, fertilisers, pesticides, and grain-handling. It was found that the information was of variable quality. Among the 39 online sites studied, several lacked contact information, had outdated content, and contained information that was missing some important details, and none provided weather information. This study contributes to the body of knowledge on online agricultural information in an African context where the agricultural sector is central to national economic development. The online agriculture information evaluation tool used in the study can potentially be of use, in its current form or adapted, to researchers beyond Tanzania.
Improved strategy for Derek in the minimal variant of the Vector game

In the minimal variant of the Vector game, the game is played at a circular table with n seats also called positions consecutively labelled from 0 to n−1. At start, a single token is placed at position 0. The two players, called Magnus and Derek, jointly control the token. If the current position is i, a round consists of Magnus choosing a positive distance l ≤n/2, then Derek deciding if the token moves clockwise or anticlockwise. Magnus’s aim in the game is to minimize the cardinality of the set of all positions occupied in the course of the game (while Derek’s is to maximize it). Let α(n) denotes the eventual size of the occupied set if both players play optimally. The focus of this paper is on a strategy for Derek to achieve at least α(n) visited positions in a small number of rounds (α(n)2 rounds) no matter how Magnus plays.
Extensional Distortion Removal of Wide Angle Fovea Image by Iteratively Applying Square Lattice Test Pattern

This paper proposes a method to remove high distortion from input images using a Wide Angle Fovea (WAF) sensor. The WAF sensor is specifically designed to acquire an input image with (1) wide-angle field of view (FOV) and (2) high resolution in the central FOV. The WAF sensor achieves this by distorting the input image. Furthermore, it is difficult to accurately to remove a distortion so high by applying conventional camera calibration methods such as the Zhang method. Therefore, we propose a novel distortion removal method that does not consider distortion coefficients for distortion correction. In the proposed method, the distortion is removed according to an extensive distortion correction lookup table. This method removes distortion from input images by wide-angle sensors using a local small test pattern and not a huge one. Although this implies that, inevitably, this method requires a much larger number of input images with a narrow FOV, it achieves processing complexity reduction for accurately mapping an undistorted image with a sufficiently wide angle. Accuracy evaluation experiments were conducted, and their results quantitatively proved that our proposed method performed effectively for distortion correction.
The ethnocultural aspect in the educational process of a pedagogical university

The paper considers ethnocultural aspects in the educational activities of the university in the process of training bachelors of education. The authors show the place and role of ethnocultural courses and practices in the educational process of a teachers training university on the example of Samara State University of Social Sciences and Education. The paper substantiates theoretical and methodological approaches to the study of the problem of ethnocultural and multicultural education in modern Russian society, characterizes current requirements of educational standards of the school and higher pedagogical education in terms of achieving personal results by students, regarding the main areas of extracurricular activities in an educational institution; describes the experience of introducing ethnocultural courses and practices in the educational process of a teachers training university, substantiates their relevance in the process of training teaching staff, identifies problems in the content and learning process within the framework of these courses and practices. The main purpose of the research is to identify the place and role of ethnoculturological courses and practices in the educational process of a teachers training university. The study has shown that the ethnocultural aspect is relevant in professional training of bachelors of education, regardless of the major, since it prepares a prospective teacher to conduct educational and extracurricular activities in the Russian secondary school, especially in regions with a complex ethnic confessional composition of the population. Ethnological knowledge and skills in the ethnocultural sphere are in demand, first of all, in the organization of extracurricular activities, but are also necessary in teaching certain courses and for an interpersonal dialogue with students.
The problem of choosing methodological bases in developing modern learning materials for students of higher education institutions

The present research deals with the problem of developing modern learning materials for students of higher education institutions. The authors emphasize that in the traditions of the Russian educational system, the pedagogical staff itself is charged with implementing this educational and methodological direction, that is why textbooks are often created intuitively, based on personal professional experience, sometimes with no scientific basis. This fact raises the question of the need for a deep analysis of scientific researches in this sphere to determine the principles in designing a particular educational publication and reasonably choose methodological bases. The authors have attempted to structure the revealed methodological bases (pedagogical approaches) depending on the idea and the functional strategic goal of education itself. Two groups of basic approaches and their derivatives are singled out and described: competence approach, aimed at the formation of a standard specialist in the professional sphere and implemented mainly through activity-based educational technologies contextual approach, problem-based learning, task-based approach; and humanitarian and anthropological approach, aimed at personal growth of a student in his entirety (personality-oriented approach, andragogical approach), disclosure of his potential, his work with personal meanings (hermeneutic approach, euristic approach), formation of his own world picture, realization of his individual educational trajectory. In the end, the authors make a conclusion that it is necessary to combine the approaches of different nature in developing the concept of learning materials, as the exclusion of one of the components will significantly limit the potential opportunities in the personal and professional growth of students.