Reviewerly/exHarmony huggingface dataset:
AGI-agent cognitive architecture agica - axiomatic approach

For the last half of the century there were proposed and modeled several dozen cognitive architectures as the models of mind. As one of the results of this Standard Model of the Mind was proposed and discussed in 2017 (now known also as “Common Model of Cognition”). It accumulated lessons learned in one structure. In the articles published in 2016-2018, the author formulated main definitions of the concepts of Artificial General Intelligence (AGI): AGI-Individual Type, AGI-Collective Type, AGI-Consciousness, AGI-Thought, AGI-Knowledge, AGI-Emotions. The author’s approach belongs to the direction Embodied Cognition in Cognitive Science and is following named “axiomatic approach” in Artificial Intelligence. The definitions proposed by the author are of constructive type from mathematical point of view and can be modeled by the existing software &amp; hardware tools and methods. In this article the author is proposing AGI-Agent cognitive architecture AGICA as detailed modification of Standard Model of the Mind. It can be used in the development of universal operating system for AGI-robots
Determining the orientation of circular areas in video images

One of the ways to describe objects in images is to identify some of their characteristic points, or points of attention. The neighborhoods of attention points are described by descriptors (sets of features) in such way that they can be identified and compared. Based on these features, the search for identical points on other images is carried out. The feature selection process should take into account not only the position of the focus point, but also the size and possible shape of the local neighborhood. Different methods of finding identical characteristic points are known, which use rectangular or circular neighborhood to describe them. The rectangular windows used for neighborhood analysis do not allow selecting the same areas on the reference image and image which is rotated and analyzed. Round windows allow this. The article proposes and studies algorithms for determining the orientation of circular areas in video images, which makes it possible to calculate, in addition to invariant characteristics, characteristics that take into account the location of individual sections of such areas. These are the algorithms: • determination of the orientation of the circular area by moments of the first and second order; • calculation of the mutual orientation of the investigated circular area and the standard according to the moments of the first and second order. As a result of research and experiments, it was established that depending on the algorithm and the content of the circular areas, there are possible deviations in determining their orientation directions. Factors affecting this: accuracy of finding characteristic points; application of certain orientation determination algorithms; differences in the angle of video recording of the reference image and the image under investigation; the location of characteristic points, inside the object or on its border, especially when the background changes; size of neighborhood (analysis window) of characteristic points. It seems appropriate to continue the research orientation determination accuracy of circular areas by various algorithms. To reduce the influence of various side factors, artificial rather than real images should be studied
Onírica: Device with Tangible Interface for the Development of Computational Thinking

Studies on Tangible User Interfaces (TUI) became popular from the works of Suzuki and Kata [15] and Ishii and Ullmer [7]. Among the many areas in that TUI can be explored, education stands out. Contact with interfaces beyond graphical interfaces can help and expand the way one learns. From this, this research project proposes a tangible interface focused on non-formal early childhood education to analyze whether the material used in the interface influences interaction and learning. To this end, it is intended to use the Design Science Research (DSR) methodology.
Dual consistency loss for contour-aware segmentation in medical images

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object's boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance- The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.
Discrimination of early HCC using single cell nucleus image and visualization of feature distribution in whole slide images

Among hepatocellular carcinoma (HCC), early HCC such as well-differentiated hepatocellular carcinoma is more difficult to distinguish from non-cancer than other cancers. In particular, very well-differentiated hepatocellular carcinoma is even more difficult to distinguish, and it is difficult for pathologists to distinguish between cancer and non-cancer from a single nucleus image. If a function to distinguish cancer with a single cell nucleus image is realized, it may be possible to find new features related to nuclei that are useful for differentiating early HCC. The function will also be very helpful in needle biopsy where the area that can be observed is limited.In this study, we investigated the potential to discriminate cancer/non-cancer from an image of a single hepatocyte nucleus using CNN. The results indicated that discrimination was achievable with a correct rate of around 70%.The probability of cancer/non-cancer was visualized on WSI. The visualization results indicated a difference between cancerous and non-cancerous areas in 71% of the cases, which will help pathologists distinguish region of interest. Grouping sections with similar features also proved useful in improving accuracy and visualization results.
Identification and mitigation of the vulnerability of web applications in institutions of higher education

The security of information technology, specifically web applications, has become an area of concern today. Computer cybercrime is now a significant problem that affects more than just businesses and organizations. Higher education institutions also began to experience computer threats that revealed their information assets. Universities, polytechnics, colleges of education, research centers, and other postsecondary institutions are probably the most vulnerable because they house sensitive data on their faculty, staff, and students, as well as academic records of scientific and technological advancements and research. The first step in an information system security strategy is risk analysis management It helps in assessing the risk of information assets to know their security level or status, and assist in define a security control measures and implementation of technical plan to avoid threats that exploit some vulnerability that could cause severe damage to an asset or infrastructure of institutions higher education (IHEs). This article presents some recommendations to perform a risk analysis management in IHEs to accessed threats and vulnerability that helps to lower the risk of their information assets. This article presents existing educational threat and vulnerability on their web applications. Ensuring security is a goal of every organization regardless of its size or purpose and also proposed a risk management model. With the information technology, an organization may be considered secure when it ensures the confidentiality, integrity, and availability of information and IT assets. Confidentiality may be broken due to theft of sensitive information such as trade secrets, clients’ personal information.
Related-Key Differential Analysis of the AES

The Advanced Encryption Standard (AES) is considered to be the most important and widely deployed symmetric primitive. While the cipher was designed to be immune against differential and other classical attacks, this immunity does not hold in the related-key setting, and various related-key attacks have appeared over time. This work presents tools and algorithms to search for related-key distinguishers and attacks of differential nature against the AES. First, we propose two entirely different approaches to find optimal truncated differential characteristics and bounds on the minimum number of active S-boxes for all variants of the AES. In the first approach, we propose a simple MILP model that handles better linear inconsistencies with respect to the AES system of equations and that compares particularly well to previous tool-based approaches to solve this problem. The main advantage of this tool is that it can easily be used as the core algorithm to search for any attack on AES exploiting related-key differentials. Then, we design a fast and low-memory algorithm based on dynamic programming that has a very simple to understand complexity analysis and does not depend on any generic solver. This second algorithm provides us useful insight on the related-key differential search problem for AES and shows that the search space is not as big as one would expect. Finally, we build on the top of our MILP model a fully automated tool to search for the best differential MITM attacks against the AES. We apply our tool on AES-256 and find an attack on 13 rounds with only two related keys. This attack can be seen as the best known cryptanalysis against this variant if only 2 related keys are permitted.
Application of intelligent systems for the development of inclusive education in educational institutions

The current level of development of information technology makes it possible to create software products that develop and promote inclusive education in higher education institutions. With the use of intelligent information systems, education becomes more developed, humane and effective not only for students with special educational needs, but also for healthy students. The paper proposes the creation of a mobile application - an assistant that will facilitate orientation in the university space. The application will help to create conditions that meet the requirements and needs of each individual, regardless of the peculiarities of their development
MONOCULAR DEPTH ESTIMATION FOR NIGHT-TIME IMAGES

Abstract. Depth estimation plays a pivotal role in numerous computer vision applications. However, depth estimation networks trained exclusively on daytime images tend to yield poor performance when applied to nighttime scenarios due to domain differences and variations in scene characteristics. In order to address this limitation, we conducted experiments involving the creation of a synthetic nighttime dataset by employing image translation techniques through a generative network. Subsequently, we utilized the generated images to fine-tune the depth estimation network, aiming to investigate the potential for enhancing task performance using generated data. We evaluated our approach by testing with the generated data, and we observed a noticeable improvement in the depth estimation task both before and after fine-tuning. Consequently, our approach yields results that are comparable to those achieved by networks specifically designed for daytime prediction. These findings highlight the effectiveness of utilizing synthetic data to enhance the performance of depth estimation tasks, particularly in nighttime settings.
Causal Robust Trajectory Prediction Against Adversarial Attacks for Autonomous Vehicles

Autonomous vehicles may mistakenly predict the future trajectories of neighboring vehicles when the trajectory prediction model is under attack. Recent works utilize adversarial training to mitigate the prediction errors of the trajectory prediction model under attacks. However, adversarial training exhibits high training costs and poor generality for different attack methods. Meanwhile, adversarial training improves the trajectory prediction performance under attacks by learning the adversarial examples, which leads to greater performance degradation in normal (without attacks) cases. In this paper, to ensure the driving safety of autonomous vehicles, we propose a causal robust trajectory prediction method named CausalRobTra, which employs Total Direct Effect (TDE) inference to defend trajectory predictors against adversarial attacks from the perspective of causal inference theory. First, we propose four directional metrics to evaluate the prediction errors of the trajectory prediction model under attacks. Then, we construct the causal graph of trajectory prediction under attacks and analyze the causalities among the nodes. Next, we conduct the counterfactual intervention on the history trajectory by replacing the history trajectory with the counterfactual trajectory to cut off the link between the history trajectory and the adversarial perturbation. Finally, we calculate TDE by subtracting the counterfactual prediction from the factual prediction to eliminate the impact of adversarial perturbation on the final prediction. Compared with no defense case, our method improves the performance by 13.4% under attacks and at the cost of 7.7% performance degradation on clean data. In addition, our method improves the performance by 20.6% on clean data compared with adversarial training and has a similar performance to adversarial training under attacks. Such an improvement can ensure the safety of autonomous vehicles under attacks and avoid many traffic accidents. Our CausalRobTra is a plug-and-play defense method that can be easily applied to any other trajectory prediction model. Extensive experiments demonstrate that our method effectively improves the adversarial robustness of the trajectory prediction model under attacks at the expense of lower performance degradation in normal (without attacks) cases.
Research and Practice of Precision Evaluation in Blended Learning Context

Precision evaluation plays a vital role in improving students’ blended learning performance. In accordance with the teaching philosophy of Outcome-Based Education (OBE) and education requirements in China, this paper put forward a Three-stage Blended Learning and Evaluation (TsBLE) method to improve students’ learning performance. The TsBLE method assumes that the blended learning process can be divided into three stages: pre-class online autonomous learning, in-class off-line in-depth research and after-class consolidation. Based on this assumption, the TsBLE method emphasizes that precision evaluation in blended learning should be multi-dimensional and fine-grained. Teaching practice was carried out to evaluate the effectiveness of the TsBLE method. The results showed that this method can effectively improve students’ learning performance and help teachers adjust teaching strategies in time.
INCEPTION TO SENSOR AND IoT TECHNOLOGY

The Internet of Things (IoT) is the recent research topic. The term “Internet of Things" (IoT) is a term that was conceived in 1999 by Kevin Ashton. The Internet of Things (IoT) is a network of physical objects or "things" that are embedded with devices, software, sensors, and networking in order to provide more value. By sharing data with the manufacturer, and by providing service operator or other devices that are linked, every single element is different. It is distinguishable due to its embedded computing system, but able to work together in the current internet. The IoT is being implemented in every hook and corner of industry and personal life of people. Governments are taking note and examining IoT from different perspectives. One aspect is how IoT system can help with performance, analytics and other tasks. Intelligence and decision making are the most important aspects. It combines various sensors and objects to interact directly with one another without human interference. This research paper focuses on the state of the art of IoT architecture. It has four significant layers, the Perception Layer, Network Layer, Middle Layer and Application Layer. The sensors can provide information on emerging health-care issues. Sensors have recently been considered as one of the most rapidly developing fields in physics, electronics, and biotechnology, and it is the one that has benefited the most from advances in individual microelectronics, optical, and computer sciences technologies.
Remote Sensing Image Registration Based on Grid Prediction of Pixel Deformation

Aiming at the problem of weak texture blurring in remote sensing image registration under complex conditions, a remote sensing image registration algorithm using grid generator to predict pixel deformation is proposed. We first improve the pixel-level deep fully convolutional encoder-decoder network using overlapping attention in the feature extraction stage to obtain the features of the two input images. The feature matching stage then utilizes a mesh generator to predict and parameterize pixel deformations. The network is then trained using the grid loss. The final parametric regression stage uses bidirectional parameters to optimize the network loss.
Remote sensing image registration based on global sensing convolution and feature fusion

To address the problem of poor registration performance in current remote sensing image data volume and complex scenes, we propose a DenseNet model based on global-aware convolution blocks and adaptive dual-feature fusion blocks for improving the accuracy of remote sensing image registration. The method captures global features by replacing part of the dense blocks with position aware circular convolution, using the global kernel and circular convolution. Meanwhile, an adaptive dual-feature fusion attention mechanism is introduced to integrate multi-level features to obtain higher registration accuracy. First, a modified DenseNet is used to extract feature information from the image, followed by a bidirectional matching relationship obtained by a bidirectional correlation matching network, and the final parameters are synthesized by weighting the bidirectional parameters obtained by regression, and finally, the image registration is completed by the affine transformation. The experimental results show that this method can effectively improve the accuracy of remote sensing image registration and outperforms other existing methods in terms of performance.
Theoretical and Methodological Foundations of Research on the Development of Intellectual Mobility in the Conditions of Internationalization of Higher Education in Pedagogical Science

The article is devoted to the study of the theoretical and methodological foundations of the development of intellectual mobility in the conditions of internationalization of higher education in pedagogical science. The article presents various positions of scientists that reveal intellectual mobility in the conditions of internationalization as a rather complex, multidimensional personal education reflecting the willingness of a person to quickly find, analyze, productively apply growing flows of information, produce new ideas and accept innovations with tolerance.
Graph Contrastive Learning for Clustering of Multi-layer Networks

Multi-layer networks precisely model complex systems in society and nature with various types of interactions, and identifying conserved modules that are well-connected in all layers is of great significance for revealing their structure-function relationships. Current algorithms are criticized for either ignoring the intrinsic relations among various layers, or failing to learn discriminative features. To attack these limitations, a novel graph contrastive learning framework for clustering of multi-layer networks is proposed by joining nonnegative matrix factorization and graph contrastive learning (called jNMF-GCL), where the intrinsic structure and discriminative of features are simultaneously addressed. Specifically, features of vertices are firstly learned by preserving the conserved structure in multi-layer networks with matrix factorization, and then jNMF-GCL learns an affinity structure of vertices by manipulating features of various layers. To enhance quality of features, contrastive learning is executed by selecting the positive and negative samples from the constructed affinity graph, which significantly improves discriminative of features. Finally, jNMF-GCL incorporates feature learning, construction of affinity graph, contrastive learning and clustering into an overall objective, where global and local structural information are seamlessly fused, providing a more effective way to describe structure of multi-layer networks. Extensive experiments conducted on both artificial and real-world networks have shown the superior performance of jNMF-GCL over state-of-the-art models across various metrics.
Methods for Solving Differential Equations in Python Language

This article provides an overview of methods for solving differential equations using the Python programming language. The article discusses in detail numerical methods such as the Euler method, the Runge-Kutta method and the finite difference method and provides examples of their application using the SciPy library. In addition to this, the article discusses the basic steps in solving differential equations, including determining the equation, boundary conditions, and initial conditions. Code examples are discussed in detail to facilitate understanding and implementation of methods in your own projects. This material is intended for developers and researchers who want to effectively use the Python language to solve differential equations in various fields of science and technology.
Syntax Monads for the Working Formal Metatheorist

Formally verifying the properties of formal systems using a proof assistant requires justifying numerous minor lemmas about capture-avoiding substitution. Despite work on category-theoretic accounts of syntax and variable binding, raw, first-order representations of syntax, the kind considered by many practitioners and compiler frontends, have received relatively little attention. Therefore applications miss out on the benefits of category theory, most notably the promise of reusing formalized infrastructural lemmas between implementations of different systems. Our Coq framework Tealeaves provides libraries of reusable infrastructure for a raw, locally nameless representation and can be extended to other representations in a modular fashion. In this paper we give a string-diagrammatic account of decorated traversable monads (DTMs), the key abstraction implemented by Tealeaves. We define DTMs as monoids of structured endofunctors before proving a representation theorem a la Kleisli, yielding a recursion combinator for finitary tree-like datatypes.