Reviewerly/exHarmony huggingface dataset:
The Nondeterministic Situation Calculus

The standard situation calculus assumes that atomic actions are deterministic. But many domains involve nondeterministic actions, with problems such as fully observable nondeterministic (FOND) planning and high-level program execution requiring solutions. Various approaches have been proposed to accommodate nondeterminism on top of the standard situation calculus language, for instance by introducing nondeterministic programs as in Golog and ConGolog. But a key problem in these approaches is that they don’t clearly distinguish between choices that can be made by the agent and choices that are made by the environment, i.e., angelic vs. devilish nondeterminism. In this paper, we propose a simple extension to the standard situation calculus that accommodates nondeterministic actions and preserves Reiter’s solution to the frame problem and answering projection queries through regression. We also provide a formalization of FOND planning and show how ConGolog high-level program execution in nondeterministic domains can be defined.
Development of Social and Intercultural Competence in Students through Peer Assessment

This article presents the results from research into the development of social and intercultural competence in student teachers through peer assessment. The purpose of the study is to identify and describe the degree to which peer assessment can influence the development of competencies. Also, to formulate recommendations for the integration of peer assessment in the education of students towards the development of social and intercultural competence. The theoretical analysis is based on a review of the scientific literature on social and intercultural competence and peer assessment, and it is followed by a quantitative and qualitative analysis. Twenty-five students, training to be teachers at Sofia University "St. Kliment Ohridski", Faculty of pedagogy, participated in the study. Each of them wrote an essay on intercultural competence and each of the students reviewed three of their colleagues' essays. The research confirms that providing feedback helps the development of social and intercultural competence in two directions: students form skills for providing constructive feedback and criticism; and they develop their abilities for accepting feedback, reflecting on their strengths and weaknesses, which leads to striving for self-improvement. Another conclusion, based on the analysis is, that by providing detailed instructions prior to peer assessment, by organizing a subsequent discussion and reflection, students would improve their skills in providing feedback, constructive criticism, considering the ideas of others with respect, understanding different perspectives. Hence, the study confirms that peer assessment provides educational opportunities for the development of social and intercultural competence.
Assessment of Nonlocal Means Stochastic Distances Speckle Reduction for SAR Time Series

Implementation of complex SAR speckle filtering algorithms is usually limited to experimental settings, that make use of heavy-duty computers or processing clusters. Operational applications of SAR imagery, such as those used to map flash-flooded areas, or to flag on-going deforestation, usually are not able to take advantage of these advanced filtering techniques. Here we introduce SDNLM3D, a fast and effective 3D filtering algorithm based on the non-local paradigm, suitable to be applied on cloud environments, such as the Google Earth Engine (GEE). A systematic, real-world based benchmark of more than 700 variations of the SDNLM3D revealed that the optimized version of the SDNLM3D filter outperforms the usual filters used operationally.
A deep attention-driven model to forecast solar irradiance

Accurately forecasting solar irradiance is indispensable in optimally managing and designing photovoltaic systems. It enables the efficient integration of photovoltaic systems in the smart grid. This paper introduces an innovative deep attention-driven model for solar irradiance forecasting. Notably, an extended version of the variational autoencoder (VAE) is introduced by amalgamating the desirable characteristics of the bidirectional LSTM (BiLSTM) and attention mechanism with the VAE model. Specifically, the introduced approach enables the conventional VAE’s ability to model temporal dependencies by incorporating BiLSTM at the VAE’s encoder side to better extract and learn temporal dependencies embed on the solar irradiance concentration measurements. In addition, the self-attention mechanism is embedded in the VAE’s encoder side following the BiLSTM to highlight pertinent features. The performance of the proposed model is evaluated through comparisons with the recurrent neural network (RNN), gated recurrent unit (GRU), LSTM, and BiLSTM. Measurements of solar irradiance in the US and Turkey are used to evaluate the investigated models. Results confirm the superior performance of the proposed model for solar irradiance forecasting over the other models (i.e., RNN, GRU, LSTM, and BiLSTM).
Significant Study of Data Encryption and Steganography

Nowadays, the use of the internet is overgrowing throughout the world, which makes security a major concern for users.&nbsp; Data is any information that is stored on a digital platform. Security is about the protection of data. Data security means digital protective measures which are applied to prevent data from hackers. Data encryption is one of the security measures which is used on data. If somehow anyone steals the data, he could not access the data if data is already encrypted. Data Encryption algorithms are developed in computer science to secure the data of senders and receivers. The security of communication is a crucial issue on the internet. Various encryption algorithms depend on data such as text, voice, picture, video, etc. In daily life, security has become a wide necessity. Information protection is perhaps the most important of all. The data is open to high potential risks in our framework. We adopt diverse methods for various security reasons. Now we are all relying on the security and storage cloud platform but also vulnerable to multiple threats. The data in the cloud is not well-secured because anyone who can enter our credentials can access it, and cloud providers do have fair access to us. This paper discusses various data encryption techniques and much more.
Main difficulties in the development of the assessment competence of teachers

This article discusses the relevance of the problem of teachers readiness to develop assessment competence. A modern teacher should have professional competencies to adequately and objectively assess not only the quality of educational planned results of students, but also the quality of their own educational activities. To implement new and modern procedures for evaluating and controlling the quality of professional and educational activities, every teacher must have the desire and willingness to develop evaluation competence. In accordance with the normative documents, such as the Federal Law “On Education in the Russian Federation”, the State Program of the Russian Federation “Development of Education” for 2013–2020 and the Federal State Educational Standard of Primary General Education, the problem of developing the assessment competence of teachers in modern conditions has become very relevant and important recently. This study was conducted on the basis of Kuzbass Regional Institute of Advanced Training and Retraining of Educational Workers, in which we used theoretical and practical analysis, as well as observation, focus group discussion, survey and questionnaire survey of primary school teachers. The concept of “evaluative competence” is considered by us as an important and necessary component of educational activity, which is included in the structure of the teacher’s professional competence.
Finite State Machine in Game Development

Finite State Machine is one of the oldest techniques in gaming where it was used in old games like PACMAN and new games like TOM RAIDER also. In all these games one major goal was to make non player characters more intelligent. There are some advance types also available but FSM still is one of the most used technique for non-player characters. The main goal of this paper is to explain how FSM works, how to create FSM and implement it in games using scripting or visual scripting. Hierarchical Finite State Machine is also discussed in this paper as it overcomes the limitations of older simple FSM. Using FSM, we can create intelligent AI agents. We can implement FSM and HFSM in games to make NPS’s behave like AI.
3D reconstruction accuracy improvement of a stereo vision system by changing the convergence angle of the cameras

Stereo vision systems are commonly used for 3D depth reconstruction. The measurement accuracy of these scanners is affected by the quantization error caused by the camera sensors. In this paper, the effect of changing the convergence angle of the cameras on the quantization uncertainty is discussed. First, the mathematical model of a stereo vision scanner with the ability to adjust the convergence angle is introduced. Then, the effect of changing the convergence angle on the quantization error reduction is investigated. Finally, physical experiments are designed to validate the proposed approach. The experimental results show that the 3D reconstruction accuracy can be improved by changing the convergence angle of the cameras.
Detailed Account of Complexity for Implementation of Circuit-Based Quantum Algorithms

In this review article, we are interested in the detailed analysis of complexity aspects of both time and space that arises from the implementation of a quantum algorithm on a quantum based hardware. In particular, some steps of the implementation, as the preparation of an arbitrary superposition state and readout of the final state, in most of the cases can surpass the complexity aspects of the algorithm itself. We present the complexity involved in the full implementation of circuit-based quantum algorithms, from state preparation to the number of measurements needed to obtain good statistics from the final states of the quantum system, in order to assess the overall space and time costs of the processes.
Resampling and super-resolution of hexagonally sampled images using deep learning

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses nonuniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network architecture designed for SR known as residual channel attention network (RCAN). In particular, we use RCAN to further upsample and restore the imagery to produce the final SR image estimate. We demonstrate that this system is superior to applying RCAN directly to rectangularly sampled LR imagery with equivalent sample density. The theoretical advantages of hexagonal sampling are well known. However, to the best of our knowledge, the practical benefit of hexagonal sampling in light of modern processing techniques such as RCAN SR is heretofore untested. Our SR system demonstrates a notable advantage of hexagonally sampled imagery when employing a modified RCAN for hexagonal SR.
Model-Based Development of Design Basis Threat for Physical Protection Systems

Physical protection system (PPS) is developed to protect the assets or facilities against threats. A systematic analysis of the capabilities and intentions of potential threat capabilities is needed resulting in a so-called Design Basis Threat (DBT) document. A proper development of DBT is important to identify the system requirements that are required for adequately protecting a system and to optimize the resources needed for the PPS. In this paper we propose a model-based systems engineering approach for developing a DBT based on feature models. Based on a domain analysis process, we provide a metamodel that defines the key concepts needed for developing DBT. Subsequently, a reusable family feature model for PPS is provided that includes the common and variant properties of the PPS concepts detection, deterrence and response. The configuration processes are modeled to select and analyze the required features for implementing the threat scenarios. Finally, we discuss the integration of the DBT with the PPS design process.
Identification of Corresponding Numerical Attributes in Heterogeneous Databases Based on Instances

Identification of attributes in heterogeneous databases is widely known as the schema matching problem, and many studies have been published. Although existing studies using schema information have been proposed, schema information cannot always be used. In such a case, a schema matching method using instances is used as an alternative method, which is called instance-based schema matching. In this paper, we propose an instance-based schema matching method for the attributes storing numerical data. It uses data distributions and correlations between two attributes. The proposed method is experimentally evaluated by using several databases. As a result, the proposed method could find appropriate attribute correspondences.
Flatness-Based Model Predictive Trajectory Planning for Cooperative Landing on Ground Vehicles

The autonomous landing of a fixed-wing unmanned aerial vehicle (UAV) on a moving unmanned ground vehicle (UGV) demands precise spatial synchronization of both vehicle systems. A promising strategy to achieve a successful landing in this heterogeneous rendezvous maneuver is the subdivision of the synchronization control task into a trajectory planner and underlying trajectory tracking controllers. For this purpose, a central trajectory planner, which is the subject of this paper, computes feasible trajectories for the UAV and UGV that converge towards each other. Current approaches mainly rely either on computationally expensive optimization problems or on potentially inaccurate linearization. In this paper, a new model predictive trajectory planning scheme based on the flatness property of kinematic models of the fixed-wing UAV and UGV is presented. By using vehicle models with this property, quadratic programs (QP) are formulated that can be solved efficiently in each time instance without linearization of the nonlinear vehicle models, which is a novel approach in this application. Crucial requirements for the trajectory planner are that it reduces the horizontal distance between the vehicles to below 0.5 m while maintaining safety constraints to allow safe landing, and a sampling rate of 40 Hz to allow rapid replanning in case of disturbances. To overcome the issue of input constraint transformation between the original and the flat models, different approximation methods are investigated. Simulation results with simple kinematic vehicle models and high-fidelity dynamic models using underlying trajectory tracking controllers and navigation filters are presented that demonstrate that the proposed trajectory planning method allows safe landing maneuvers with the considered vehicle setup.
Immutable and Privacy Protected E-Certificate Repository on Blockchain

Fake education certificates or fake degree is one of the major concerns in higher education. This fraud can be minimized if there is a tamper-proof and confidential registry of certificates wherein not one but multiple certified authorities verifies and stores the issued certificate in immutable repositories with proper privacy maintained. Secondly, there should be a mechanism for retrieving the authentic certificate without much cost and time. Blockchain is an immutable, shared, distributed ledger without the control of a single centralized authority that fits very well for the discussed use case. The proposed work, PrivateCertChain, has implemented the idea for university having multiple affiliated colleges, by deploying and verifying digitally signed e-certificate on Ethereum Blockchain. Multiple affiliated colleges can serve as the miners for verifying the signature of the issuer. For privacy concerns, the content of the certificate will be hashed and this hashed value will be stored in Blockchain along with the roll number of the certificate holder. Once the transaction hash is generated, it will be converted to QR code. The QR code is shared with the respective owner of the certificate and it will also serve as the credential of the certificate. Thus, anyone having the credential can view the authentic certificate which is kept on the blockchain, by scanning QR through the dedicated application designed for verification. The proposed solution can be a foolproof mechanism against all frauds as it guards for integrity, confidentiality, authenticity, and privacy of educational certificates.
Network-Flow-Problem-Based Approach to Multi-Agent Path Finding for Connected Autonomous Vehicles

Vehicle coordination is one of the essential technologies for connected autonomous vehicles (CAVs). It is necessary for the route-level coordination as well as trajectory-level coordination to solve conflicts among vehicles in congested situations. The multi-agent path finding problem (MAPF) has been studied to efficiently find collision-free paths (i.e., routes) on a graph for a large number of agents. However, the paths cannot be applied for CAVs because the vehicle's kinematic constraints are not considered in the standard MAPF. This paper proposes a new variant of MAPF that considers the orientation and dimensions for CAVs by extending the graph structure and collision definition to obtain paths that can generate feasible trajectories in the real world. The proposed MAPF is formulated by a network flow problem approach using 0-1 integer linear programming. A trajectory generation based on the paths by MAPF is also implemented and the feasibility of the paths are confirmed by simulations for CAVs.
Patch-Level Nuclear Pleomorphism Scoring Using Convolutional Neural Networks

In an effort to ease the job of pathologists while examining Hematoxylin and Eosin stained breast tissue, this study presents a deep learning-based classifier of nuclear pleomorphism according to the Nottingham grading scale. We show that high classification accuracy is attainable without pre-segmenting the cell nuclei. The data used in the experiments was acquired from our partner teaching hospital. It consists of image patches that were extracted from whole slide images. Using the labeled data, we compared the performance of three state-of-the-art convolutional neural networks and tested the trained model on the unseen testing data. Our experiments revealed that the densely connected architecture (DenseNet) outperforms the residual network (ResNet) and the dual path networks (DPN) in terms of accuracy and F1 score. Specifically, we reached an overall validation accuracy and F1 score of over 0.96 and 0.94 respectively.
A Low-Cost Solution for 3D Reconstruction of Large-Scale Specular Objects

In this paper, we present a low-cost 3D reconstruction method for large-scale specular objects based on deflectometry. Experiments show that our system reaches high accuracy and meets requirements of the target applications in the cultural heritage preservation.
Open Source-Based UAVs for STEAM Education: Some Case Studies

The importance of Science Technology, Engineering, Arts, and Mathematics (STEAM) education has been strongly recognized over the last few years. STEAM education requires an approach to integrate these disciples, and robots and unmanned aerial vehicles (UAVs) are often utilized as ideal tools for hands-on STEAM education. Over the past few years, open source-based fully autonomous UAVs have been priced below $100. This fact also has allowed UAV Research and Development (R&amp;D) to bring reverse innovation in education. This paper introduces our projects contributing to local communities, including R&amp;D and STEAM educational programs that exploit open-source UAV platforms. We describe three examples of STEAM educational programs using UAV technologies: a three-day scratch-build UAV workshop at the Kirirom Institute of Technologies in Cambodia, a sailing robot development project at a private STEAM education and fabrication lab for children, VIVISTOP Kashiwanoha in Chiba, Japan, and an ongoing project of UAV R&amp;D at Japan Aviation Academy whose framework is going to be extended to a workshop for local children in Ishikawa, Japan, with a local board of education.
Learning Analytics to Detect Evidence of Fraudulent Behaviour in Online Examinations

Lecturers are often reluctant to set examinations online because of the potential problems of fraudulent behaviour from their students.This concern has increased during the coronavirus pandemic because courses that were previously designed to be taken face-to-face have to be conducted online.The courses have had to be redesigned, including seminars, laboratory sessions and evaluation activities.This has brought lecturers and students into conflict because, according to the students, the activities and examinations that have been redesigned to avoid cheating are also harder.The lecturers' concern is that students can collaborate in taking examinations that must be taken individually without the lecturers being able to do anything to prevent it, i.e. fraudulent collaboration.This research proposes a process model to obtain evidence of students who attempt to fraudulently collaborate, based on the information in the learning environment logs.It is automated in a software tool that checks how the students took the examinations and the grades that they obtained.It is applied in a case study with more than 100 undergraduate students.The results are positive and its use allowed lecturers to detect evidence of fraudulent collaboration by several clusters of students from their submission timestamps and the grades obtained.
Generating individual intrinsic reward for cooperative multiagent reinforcement learning

Multiagent reinforcement learning holds considerable promise to deal with cooperative multiagent tasks. Unfortunately, the only global reward shared by all agents in the cooperative tasks may lead to the lazy agent problem. To cope with such a problem, we propose a generating individual intrinsic reward algorithm, which introduces an intrinsic reward encoder to generate an individual intrinsic reward for each agent and utilizes the hypernetworks as the decoder to help to estimate the individual action values of the decomposition methods based on the generated individual intrinsic reward. Experimental results in the StarCraft II micromanagement benchmark prove that the proposed algorithm can increase learning efficiency and improve policy performance.