Reviewerly/exHarmony huggingface dataset:
Modified Rijndael Algorithm for Resource-Constrained IoT-Based Wireless Sensor Networks

INTRODUCTION: IoT devices are small, have limited battery and computing capabilities, and cannot tolerate complex encryption. OBJECTIVES: Many encryption algorithms struggle to strike a balance between security and complexity. Increasing the level of security necessitates increasing complexity, which leads to higher encryption and decryption times. This research aims to provide a novel encryption method, Modified Rijndael Algorithm that balances security and complexity. METHODS: The classic Rijndael encryption technique has been modified to make it lighter and more secure, suitable for resource-constrained IoT devices. RESULTS: These modifications include adding the novel pre-processing step SISA-Crossover based on the two-point crossover operation of genetic algorithms, removing the Sub Byte step, changing the shift column phase, and decreasing the rounds. CONCLUSION: The proposed technique provides speed and higher security in encryption and decryption, increasing efficiency and randomness while reducing encryption and decryption times by 21.06% and CPU consumption by 13.2% compared to the traditional method.
Opyt ispol'zovaniia elektronnogo onlain-kursa "K-EGE po informatike i IKT" pri formatirovanii kriticheskogo myshleniia u uchashchikhsia inzhenernykh klassov

The article presents the experience of using an electronic online course &amp;quot;Exam in Computer Science and ICT&amp;quot; in preparing students for the Unified State Exam in computer science. This course was implemented during the implementation of the &amp;quot;Engineering Class&amp;quot; program at the Volgograd State Technical University. The influence of the level of development of students' critical thinking on the quality of preparation and the effectiveness of passing the Unified State Exam is substantiated. A model of task material for an electronic online course, including tasks for the development of critical thinking, is presented.
Understanding political polarization using language models: A dataset and method

Abstract Our paper aims to analyze political polarization in US political system using language models, and thereby help candidates make an informed decision. The availability of this information will help voters understand their candidates' views on the economy, healthcare, education, and other social issues. Our main contributions are a dataset extracted from Wikipedia that spans the past 120 years and a language model‐based method that helps analyze how polarized a candidate is. Our data are divided into two parts, background information and political information about a candidate, since our hypothesis is that the political views of a candidate should be based on reason and be independent of factors such as birthplace, alma mater, and so forth. We further split this data into four phases chronologically, to help understand if and how the polarization amongst candidates changes. This data has been cleaned to remove biases. To understand the polarization, we begin by showing results from some classical language models in Word2Vec and Doc2Vec. And then use more powerful techniques like the Longformer, a transformer‐based encoder, to assimilate more information and find the nearest neighbors of each candidate based on their political view and their background. The code and data for the project will be available here: “ https://github.com/samirangode/Understanding_Polarization ”
DINA-BAG: A Bagging Algorithm for DINA Model Parameter Estimation in Small Samples

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining accurate estimates of skill mastery at the population level. We introduce a novel algorithm (bagging algorithm for deterministic inputs noisy “and” gate) that is inspired by ensemble learning methods in the machine learning literature and produces more stable and accurate estimates of the population skill mastery profile distribution for small sample sizes. Using both simulated data and real data from the Examination for the Certificate of Proficiency in English, we demonstrate that the proposed method outperforms other methods on several metrics in a wide variety of scenarios.
Liars, Skeptics, Cheerleaders: Human Rights Implications of Post-Truth Disinformation from State Officials and Politicians

Abstract The purpose of this paper is to philosophically examine how disinformation from state officials and politicians affects the right to access to information and political participation. Next to the more straightforward implications for political self-determination, the paper examines how active dissemination of lies by figures of epistemic authority can be framed as a human rights issue and affects trust patterns between citizens, increases polarization, impedes dialogue, and obstructs access to politically relevant information by gatekeeping knowledge. Analyzing European Convention on Human Rights (ECHR) case law, the paper argues that human rights law provides some argumentative basis for extending individuals’ rights as epistemic and political agents towards a “right to truth spoken by politicians”. However, challenges in balancing a possible restriction of lies and assessing the real effective harm that comes from them remain, potentially leading to a vacuum of rights protection for less visible long-term harm to individuals and public discourse. In order to have a real chance at tackling the harmful consequences of publicly told lies from a human rights perspective, it is necessary to rethink the notion of harm to encompass more complex and abstract forms of politico-epistemic damage to individuals and the public.
Categorical versus gradient grammar in phonotactics

Abstract Within the past 2 decades, probabilistic grammars have been put forward in the study of phonotactics as a necessary device to model gradient acceptability of lexical forms. This implicitly suggests that categorical grammars cannot even in principle account for such gradience. Most importantly, influential research has proposed that grammatical forms described by categorical grammars could be simply understood as by‐products of probabilistic thresholds. Therefore, either implicitly or explicitly, categorical grammars are painted as not only empirically insufficient but also theoretically and formally redundant by this interpretation of probabilistic grammars in the domain of phonotactics. This paper provides a general overview of this recent debate and argues against the standard threshold interpretation for probabilistic grammars based on mathematical results, explicit axiomatic principles, and recent experimental evidence. In its place, I propose an interpretation for gradient grammars that solves apparent inconsistencies, combines the two types of grammar, accommodates previous technical contributions and data, and establishes a clear theoretical role that opens up further research directions.
Period Estimation of Spread Spectrum Codes Based on ResNet

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.
Entropy regularization in probabilistic clustering

Abstract Bayesian nonparametric mixture models are widely used to cluster observations. However, one major drawback of the approach is that the estimated partition often presents unbalanced clusters’ frequencies with only a few dominating clusters and a large number of sparsely-populated ones. This feature translates into results that are often uninterpretable unless we accept to ignore a relevant number of observations and clusters. Interpreting the posterior distribution as penalized likelihood, we show how the unbalance can be explained as a direct consequence of the cost functions involved in estimating the partition. In light of our findings, we propose a novel Bayesian estimator of the clustering configuration. The proposed estimator is equivalent to a post-processing procedure that reduces the number of sparsely-populated clusters and enhances interpretability. The procedure takes the form of entropy-regularization of the Bayesian estimate. While being computationally convenient with respect to alternative strategies, it is also theoretically justified as a correction to the Bayesian loss function used for point estimation and, as such, can be applied to any posterior distribution of clusters, regardless of the specific model used.
A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic

Artificial Intelligence (AI) systems are increasingly being deployed in decentralized environments where they interact with other AI systems and humans. In these environments, each participant may have different ways of expressing the same semantics, leading to challenges in communication and collaboration. To address these challenges, this paper presents a novel hybrid model for shared conceptualization in decentralized AI systems. This model integrates ontology, epistemology, and epistemic logic, providing a formal framework for representing and reasoning about shared conceptualization. It captures both the intensional and extensional components of the conceptualization structure and incorporates epistemic logic to capture knowledge and belief relationships between agents. The model’s unique contribution lies in its ability to handle different perspectives and beliefs, making it particularly suitable for decentralized environments. To demonstrate the model’s practical application and effectiveness, it is applied to a scenario in the healthcare sector. The results show that the model has the potential to improve AI system performance in a decentralized context by enabling efficient communication and collaboration among agents. This study fills a gap in the literature concerning the representation of shared conceptualization in decentralized environments and provides a foundation for future research in this area.
Low-Voltage Network Modeling and Analysis with Rooftop PV Forecasts: A Realistic Perspective from Queensland, Australia

Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which ultimately can limit the amount of rooftop solar photovoltaic (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer-owned rooftop solar PV on Energy Queensland’s distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland’s distribution feeders were modeled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process, and quasi-dynamic simulation (QDSL) models were used to represent the dynamic volt–watt and volt–var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear relationships were revealed between the number of PV systems on a feeder and various network characteristics. Regression was used to form trend equations that represent the linear relationships for each scenario analyzed. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer-owned rooftop solar PV without the need for detailed modeling.
Adaptive IMM-UKF for Airborne Tracking

In this paper, we propose a nonlinear tracking solution for maneuvering aerial targets based on an adaptive interacting multiple model (IMM) framework and unscented Kalman filters (UKFs), termed as AIMM-UKF. The purpose is to obtain more accurate estimates, better consistency of the tracker, and more robust prediction during sensor outages. The AIMM-UKF framework provides quick switching between two UKFs by adapting the transition probabilities between modes based on a distance function. Two modes are implemented: a uniform motion model and a maneuvering model. The experimental validation is performed with Monte Carlo simulations of three scenarios with ACAS Xa tracking logic as a benchmark, which is the next generation of airborne collision avoidance systems. The two algorithms are compared using hypothesis testing of the root mean square errors. In addition, we determine the normalized estimation error squared (NEES), a new proposed noise reduction factor to compare the estimation errors against the measurement errors, and an estimated maximum error of the tracker during sensor dropouts. The experimental results illustrate the superior performance of the proposed solution with respect to the tracking accuracy, consistency, and expected maximum error.
Making Open Scholarship More Equitable and Inclusive

Democratizing access to information is an enabler for our digital future. It can transform how knowledge is created, preserved, and shared, and strengthen the connection between academics and the communities they serve. Yet, open scholarship is influenced by history and politics. This article explores the foundations underlying open scholarship as a quest for more just, equitable, and inclusive societies. It analyzes the origins of the open scholarship movement and explores how systemic factors have impacted equality and equity of knowledge access and production according to location, nationality, race, age, gender, and socio-economic circumstances. It highlights how the privileges of the global North permeate academic and technical standards, norms, and infrastructures. It also reviews how the collective design of more open and collaborative networks can engage a richer diversity of communities, enabling greater social inclusion, and presents key examples. By fostering dialogue with multiple stakeholders, more effective avenues for knowledge production and representation can be built based on approaches that are accessible, participatory, interactive, ethical, and transparent, and that reach a far broader public. This expansive vision of open science will lead to a more unified knowledge economy.
Defining adaption measures for organisational multi-agent systems

AbstractDesigning adaptive multi-agent Systems (MAS) is a challenging development effort. A key point of adaptive systems is that they provide alternative options for acting and designers have to weight the number and elaboration of these alternatives. Here, we concentrate on organisation-oriented MAS and show that organisational models provide suitable means for identifying key measures of this adaptivity. We derive measures both from the static description of the organisation as well as from the induced adaptation dynamics grounded on a Markovian analysis of the behaviour. These measures allow for a goal-directed planning of adaption.Keywords: Complex adaptive systemsmulti-agent systemsorganisation-oriented designMAPE-loopmodels@run.timePetri nets Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 If we want to express this aspect, we have to replace the absolute option value Optp in (Equation1(1) Contingency(G):=E[Optp]=1|PG|⋅∑p∈P(G)Optp(1) ) by a value weighted with the distance of the task p from G's initial task p0, i.e.γdist(p0,p)⋅Optp where γ∈[0;1] is a decay factor.
A Novel Container Placement Mechanism Based on Whale Optimization Algorithm for CaaS Clouds

Advancements in container technology can improve the efficiency of cloud systems by reducing the initiation time of virtual machines (VMs) and improving portability. Therefore, many cloud service providers offer cloud services based on the container as a service (CaaS) model. Container placement (CP) is a mechanism that allocates containers to a pool of VMs by mapping new containers to VMs and simultaneously considering VM placements on physical machines. The CP mechanism can serve several purposes, such as reducing power consumption and optimizing resource availability. This study presents directed container placement (DCP), a novel policy for placing containers in CaaS cloud systems. DCP extends the whale optimization algorithm, an optimization technique aimed at reducing the power consumption in cloud systems with a minimum effect on the overall performance. The proposed mechanism is evaluated against established methods, namely, improved genetic algorithm and discrete whale optimization using two criteria: energy savings and search time. The experiments demonstrate that DCP consumes approximately 78% less power and reduces the search time by approximately 50% in homogeneous clouds. In addition, DCP saves power by approximately 85% and reduces the search time by approximately 30% in heterogeneous clouds.
TLS-MHSA: An Efficient Detection Model for Encrypted Malicious Traffic based on Multi-Head Self-Attention Mechanism

In recent years, the use of TLS (Transport Layer Security) protocol to protect communication information has become increasingly popular as users are more aware of network security. However, hackers have also exploited the salient features of the TLS protocol to carry out covert malicious attacks, which threaten the security of network space. Currently, the commonly used traffic detection methods are not always reliable when applied to the problem of encrypted malicious traffic detection due to their limitations. The most significant problem is that these methods do not focus on the key features of encrypted traffic. To address this problem, this study proposes an efficient detection model for encrypted malicious traffic based on transport layer security protocol and a multi-head self-attention mechanism called TLS-MHSA. Firstly, we extract the features of TLS traffic during pre-processing and perform traffic statistics to filter redundant features. Then, we use a multi-head self-attention mechanism to focus on learning key features as well as generate the most important combined features to construct the detection model, thereby detecting the encrypted malicious traffic. Finally, we use a public dataset to verify the effectiveness and efficiency of the TLS-MHSA model, and the experimental results show that the proposed TLS-MHSA model has high precision, recall, F1-measure, AUC-ROC as well as higher stability than seven state-of-the-art detection models.
Development of student worksheets through study chitosan as corrosion inhibitor on metal

This research aims to develop student worksheets (SW) through the study of the effectiveness of chitosan as a coating of corrosion on the metal Zn, Fe, and Al in acidic media, which are valid based on the feasibility aspect of content, language, presentation and graphic. This type of research is the development of research with reference to the development process of the 4-D model of development. Instrument data collection sheet validation is given to three validators, and from analysis of the data obtained on the validity of the feasibility aspects of content, language, presentation and graphic row is equal to 93.88%, 96.67%, 95.83%, and 93.75% with a valid category. Based on the results of limited trials conducted on teachers and students, each obtained an average score of 92.50% and 87.90% with good criteria. It can be concluded that SW developed based on the study of the effectiveness of chitosan as a corrosion coating on metals in acidic media is stated to be valid and can be used in high school chemistry subjects in the subject of reduction–oxidation (redox) reactions and corrosion.
Steane enlargement of entanglement-assisted quantum error-correcting codes

Abstract We introduce a Steane-like enlargement procedure for entanglement-assisted quantum error-correcting codes (EAQECCs) obtained by considering Euclidean inner product. We give formulae for the parameters of these enlarged codes and apply our results to explicitly compute the parameters of enlarged EAQECCs coming from some BCH codes.
Full communication memory networks for team-level cooperation learning

Communication in multi-agent systems is a key driver of team-level cooperation, for instance allowing individual agents to augment their knowledge about the world in partially-observable environments. In this paper, we propose two reinforcement learning-based multi-agent models, namely FCMNet and FCMTran. The two models both allow agents to simultaneously learn a differentiable communication mechanism that connects all agents as well as a common, cooperative policy conditioned upon received information. FCMNet utilizes multiple directional Long Short-Term Memory chains to sequentially transmit and encode the current observation-based messages sent by every other agent at each timestep. FCMTran further relies on the encoder of a modified transformer to simultaneously aggregate multiple self-generated messages sent by all agents at the previous timestep into a single message that is used in the current timestep. Results from evaluating our models on a challenging set of StarCraft II micromanagement tasks with shared rewards show that FCMNet and FCMTran both outperform recent communication-based methods and value decomposition methods in almost all tested StarCraft II micromanagement tasks. We further improve the performance of our models by combining them with value decomposition techniques; there, in particular, we show that FCMTran with value decomposition significantly pushes the state-of-the-art on one of the hardest benchmark tasks without any task-specific tuning. We also investigate the robustness of FCMNet under communication disturbances (i.e., binarized messages, random message loss, and random communication order) in an asymmetric collaborative pathfinding task with individual rewards, demonstrating FMCNet’s potential applicability in real-world robotic tasks.