Reviewerly/exHarmony huggingface dataset:
ACKE: Asymmetric Computing Key Exchange Protocol for IoT Environments

Most of the cryptographic protocols currently in use are not appropriate for Internet of Things (IoT) environments because of their huge computing overhead, especially for terminal-embedded devices with resource constrained. Moreover, the computing resources in IoT environments are frequently asymmetric, that is to say, the computing power of the terminal devices is always weak and the server side is relatively stronger. In order to guarantee the security in the scenario, we present the asymmetric computing cryptosystem. Take the key exchange protocol as an example, we show how to construct ACKE, an asymmetric computing key exchange protocol, by employing the Diffie–Hellman key exchange protocol and the Subset Product problem (NP-complete) in this article. The underlying idea this construction is to significantly decrease the computational complexity of one party, and allow for a suitable rise in the computational complexity of another party. Our proposed protocol is implemented on an IoT simulation platform composed of a notebook PC of Intel i5-5200U 2.2 GHz/8G and a smart watch of MTK6062 1.2 GHz/512M. The experimental results show that this work will assist in making the Diffie–Hellman type protocol suitable for practical applications in IoT environments.
Automatic Assessment Method of College Students Psychological Stress Based on Medical Big Data

Students’ psychological stress assessment has become an important part of college mental health management, but the current assessment methods are qualitative analysis, combined with the scale to get the assessment results. The lack of data support in the evaluation process leads to large evaluation errors. In view of the above practical problems, this paper studies the automatic evaluation method of college students’ psychological stress based on medical big data. After using crawlers to obtain medical big data, it was preprocessed. Taking physiological data as the evaluation index of psychological stress, the relationship between physiological indexes and psychological stress is obtained by data mining. The HMM model improved by SVM is used to quantify the evaluation results. After testing, the relative error of the evaluation method based on medical big data is less than 10%, and the evaluation accuracy is higher.
Robust Reversible Watermarking by Fractional Order Zernike Moments and Pseudo-Zernike Moments

Robust reversible watermarking (RRW) is one of the most popular areas in information hiding. Existing schemes have two drawbacks: 1) schemes that can resist conventional attacks often fail to resist geometric attacks, and 2) schemes that can resist geometric attacks often are not robust against conventional attacks and have poor stability. Inspired by the high robustness of fractional-order orthogonal moments (FoOM) and the good feature of resistance to geometric attacks of Zernike moments and pseudo-Zernike moments (ZM/PZM), in this research, FoOM is used to optimize ZM/PZM to obtain FoZM/FoPZM (namely, fractional-order Zernike moments and fractional-order pseudo-Zernike moments). Furthermore, a denoiser is proposed to preprocess the watermarks to improve the robustness against geometric and conventional attacks, the amount of extracted auxiliary information is decreases and the extraction process of auxiliary information is designed to be more stable. Specifically, first, the source of the difference between the watermarked image and the carrier image is identified, that difference is represented with less information, and then that information is embedded into the cover image as auxiliary information. Second, the watermark is embedded in the low-order FoZM/FoPZM component. Finally, the watermarked image is denoised using a denoiser before extracting the watermark. The experimental results show that the scheme has good stability and strong robustness. Compared with existing methods, the proposed scheme has a small and stable auxiliary information size, strong robustness to noise attacks such as Gaussian noise and salt-and-pepper noise attacks, and better resistance to geometric attacks such as rotation and scaling attacks.
Continuous–Discrete Cubature Kalman Filter With Log-Euclidean Metric-Based Covariance Integration

For continuous-discrete filtering with strong nonlinearity and large measurement intervals, a Log-Euclidean metric (LEM) based novel continuous-discrete cubature Kalman filter (LEMCDCKF) is proposed by shifting the cubature rule-based covariance propagation to Riemannian manifold. In conventional CDCKFs, the covariance differential equation based on cubature points is solved with Euclidean space integration schemes, which inevitably ignore the geometric property and restrict the performance of CDCKF. To remedy this shortage, we propose to define covariance on Riemannian symmetric positive definite (SPD) manifold and integrate the cubature rule-based covariance differential equation with the LEM-based novel scheme, which can successfully account for the manifold property of covariance matrices and provide accurate results. Moreover, by refining CDCKF with the LEM based scheme, the proposed LEMCDCKF shifts the covariance integration process to SPD manifold, which can break through the limitation of Euclidean numerical scheme. Numerical investigations verify the superior performance of the proposed LEMCDCKF in air traffic control scenarios with large measurement intervals.
A Geometric Knowledge Oriented Single-Frame 2D-to-3D Human Absolute Pose Estimation Method

As a critical part of the 3D human pose estimation (HPE), establishing the 2D-to-3D lifting mapping is limited by depth ambiguity. Most current works generally lack the quantitative analysis of the relative depth expression and the depth ambiguity error expression in lifting mapping, resulting in low prediction efficiency and poor interpretability. To this end, this paper mines and leverages prior geometric knowledge of these expressions based on the pinhole imaging principle, decoupling the 2D-to-3D lifting mapping and simplifying the model training. Specifically, this paper proposes a prior geometric knowledge oriented pose estimation model with two-branch transformer architectures, explicitly introducing high-dimensional prior geometric features to improve model efficiency and interpretability. It converts the regression of spatial coordinates into the prediction of spatial direction vectors between joints to generate multiple feasible solutions further alleviate the depth ambiguity. Moreover, this paper raises a novel non-learning-based absolute depth estimation algorithm based on prior geometric relationship decoupling from relative depth expression for the first time. It establishes multiple independent depth mapping from non-root nodes to the root node to calculate the absolute depth candidate, which is parameter-free, plug-and-play, and interpretable. Experiments show that the proposed pose estimation model achieves state-of-the-art performance on Human 3.6M and MPI-INF-3DHP benchmarks with lower parameters and faster inference speed, and the proposed absolute depth estimation algorithm achieves similar performance to traditional methods without any network parameters. The source code are available at https://github.com/Humengxian/GKONet.
Comparative Reasoning for Intelligent Agents

We demonstrate new comparative reasoning abilities of NARS, a formal model of intelligence, which enable the asymmetric comparison of perceivable quantifiable attributes of objects using relations. These new abilities are implemented by extending NAL with additional inference rules. We demonstrate the new capabilities in a bottle-picking experiment on a mobile robot running ONA, an implementation of NARS.
Self-Comprehension for More Coherent Language Generation

Linguoplotter is a distributed and chaotic architecture where an entanglement of different processes interact to generate a text describing a raw data input. This paper describes recent additions to the architecture whereby a greater degree of language comprehension is used to improve the coherence of generated text. Some examples of the architecture operating are considered, including where it performs well and generates a good quality text; and instances where it gets trapped in loops that either prevent an output from being generated or cause a lower quality output to be produced before there is a chance to find a better alternative. Finally, ideas from the program Metacat are considered which could allow the program to observe its own processes and become a more human-like intelligence.
Memory System and Memory Types for Real-Time Reasoning Systems

In this paper we discuss different types of memory from several cognitive architectures in the context of Artificial General Intelligence. We then introduce the memory system for the Artificial General Intelligence system based on NARS with a description of its related features. Then we identify and characterize NARS memory into different types in terms of use, duration (short and long-term) and type (procedural, episodic, declarative, etc.). At the end we also provide demonstration of memory functionality showing how the same piece of knowledge can contain declarative, episodic and procedural components within it.
NUTS, NARS, and Speech

To investigate whether “Intelligence is the capacity of an information-processing system to adapt to its environment while operating with insufficient knowledge and resources" [29], we look at utilising the non axiomatic reasoning system (NARS) for speech recognition. This article presents NUTS: raNdom dimensionality redUction non axiomaTic reasoning few Shot learner for perception. NUTS consists of naive dimensionaility reduction, some pre-processing, and then non axiomatic reasoning (NARS). With only 2 training examples NUTS performs similarly to the Whisper Tiny model for discrete word identification.
Evaluation of Pretrained Large Language Models in Embodied Planning Tasks

Modern pretrained large language models (LLMs) are increasingly being used in zero-shot or few-shot learning modes. Recent years have seen increased interest in applying such models to embodied artificial intelligence and robotics tasks. When given in a natural language, the agent needs to build a plan based on this prompt. The best solutions use LLMs through APIs or models that are not publicly available, making it difficult to reproduce the results. In this paper, we use publicly available LLMs to build a plan for an embodied agent and evaluate them in three modes of operation: 1) the subtask evaluation mode, 2) the full autoregressive plan generation, and 3) the step-by-step autoregressive plan generation. We used two prompt settings: prompt-containing examples of one given task and a mixed prompt with examples of different tasks. Through extensive experiments, we have shown that the subtask evaluation mode, in most cases, outperforms others with a task-specific prompt, whereas the step-by-step autoregressive plan generation posts better performance in the mixed prompt setting.
Electronic Education Machine AGI-EEdu

This project is part of the digitization of engineering education at the Royal Institute of Technology (KTH). The digitization process of the observed undergraduate course in electrical engineering so far allows students to use the Learning Management System (LMS) to find course literature and notes, recorded lectures, theoretical exercises in the form of quizzes, and online interactive calculation exercises with built-in feedback for each question. What remains to be solved is the human-type contact that can answer a student’s question, for example: “How am I doing in the course?”. Usually, an experienced teacher would be able to answer that question with his own predictions. The purpose of this project is to use AGI technology to construct a software solution that, by adapting to the environment, i.e. the existing LMS, with insufficient knowledge and resources about the future, can provide a qualified prediction about the student’s future performance. That solution, here with the working name “AGI-EEdu”, would get to know each student in the course and (with the help of data from students’ activities in the LMS, as well as the other data based on aggregated statistical data from previous exam) could provide unique answers and advice to each student in real time throughout the course.
Rethinking the Physical Symbol Systems Hypothesis

It is now more than a half-century since the Physical Symbol Systems Hypothesis (PSSH) was first articulated as an empirical hypothesis. More recent evidence from work with neural networks and cognitive architectures has weakened it, but it has not yet been replaced in any satisfactory manner. Based on a rethinking of the nature of computational symbols – as atoms or placeholders – and thus also of the systems in which they participate, a hybrid approach is introduced that responds to these challenges while also helping to bridge the gap between symbolic and neural approaches, resulting in two new hypotheses, one that is to replace the PSSH and the other focused more directly on cognitive architectures.
An Adaptive Vision Architecture for AGI Systems

This paper presents an unsupervised object detection system which can offline-learn generic visual features via Siamese neural network, yet is able to learn new object classes at run-time with a prototype learning approach applied on the latent representations. The operating requirements of this system feature bounded processing time per frame, while dealing with a fixed amount of available memory. This system works under the Assumption of Insufficient Knowledge and Resource and is hence operating in real-time and open to new information which can arrive at any time, as systems such as NARS and AERA ideally also demand for perception.
Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms

In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS), which is a general-purpose cognitive reasoning framework. In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks. To investigate this, we conduct a comparative analysis of the performance of ONA as an implementation of NARS and Q-Learning in various environments that were created using the Open AI gym. The environments have different difficulty levels, ranging from simple to complex. Our results demonstrate that NARS is a promising alternative to RL, with competitive performance in diverse environments, particularly in non-deterministic ones.
Machine learning basic concepts for the movement disorders specialist

In this contribution, we provide a basic introduction to key concepts of Machine Learning (ML). ML can be considered the subfield of Artificial intelligence (AI) which concerns the extraction of information from example data in order to enable computer systems to learn specific tasks and improve their performance. We first introduce most important ML paradigms: unsupervised and supervised learning, which can be associated with typical tasks such as clustering and dimensionality reduction or classification and regression, respectively. Key concepts are illustrated in terms of two imaginary “toy” data sets: the first one represents the properties of a set of various vegetables and fruit. The second one relates to data that could be acquired from a cohort of subjects in the context of movement disorders. For the sake of clarity, emphasis is put on concepts of supervised learning, in particular on classification problems. Examples of important ML methods are illustrated in terms of the above-mentioned imaginary data sets. Without going into technical details, we introduce popular approaches such as linear classifiers, neural networks and prototype-based systems. Moreover, the typical practical workflow of ML, including training, validation, and working phase is outlined. We conclude by briefly discussing issues of practical relevance such as the interpretability of ML models and Explainable AI (XAI). Being far from providing a complete overview of the very broad area of Machine Learning, this chapter should provide a starting point for the further exploration of this very active field of research and its increasing importance in medical data analysis.
Computational-Level Analysis of Constraint Compliance for General Intelligence

Human behavior is conditioned by codes and norms that constrain action. Rules, “manners,” laws, and moral imperatives are examples of classes of constraints that govern human behavior. These systems of constraints are “messy:” individual constraints are often poorly defined, what constraints are relevant in a particular situation may be unknown or ambiguous, constraints interact and conflict with one another, and determining how to act within the bounds of the relevant constraints may be a significant challenge, especially when rapid decisions are needed. General, artificially-intelligent agents must be able to navigate the messiness of systems of real-world constraints in order to behave predictability and reliably. In this paper, we characterize sources of complexity in constraint processing for general agents and describe a computational-level analysis for such constraint compliance. We identify key algorithmic requirements based on the computational-level analysis and outline a limited, exploratory implementation of a general approach to constraint compliance.
Context-Rich Evaluation of Machine Common Sense

Building machines capable of common sense reasoning is an important milestone in achieving Artificial General Intelligence (AGI). While recent advances, such as large language models, are promising, systematic and sufficiently robust evaluations of these models on common sense have been inadequate, and designed for an earlier generation of models. One criticism of prior evaluation protocols is that they have been too narrow in scope e.g., by restricting the format of questions posed to the model, not being theoretically grounded, and not taking the context of a model’s responses in constructing follow-up questions or asking for explanations. In this paper, we aim to address this gap by proposing a context-rich evaluation protocol designed specifically for evaluating machine common sense. Our protocol can subsume popular evaluation paradigms in machine common sense as special cases, and is suited for evaluating both discriminative and generative large language models. We demonstrate the utility of the protocol by using it to conduct a pilot evaluation of the ChatGPT system on common sense reasoning.
DIGITAL TRANSFORMATION OF EDUCATIONAL PROCESSES IN PEDAGOGICAL HIGHER EDUCATION INSTITUTIONS

The article analyzes the need to transform digital processes in pedagogical higher education institutions.The relevance of this topic is explained by the importance of modernizing education, carrying out reforms and implementing strategic changes in teaching in pedagogical HEIs.The modernization of the content of education involves the acquisition of practical skills, motivation and professional realization by students in the process of blended and distance learning.Information and communication technologies have ensured the continuity of the education system, which helps future professionals prepare for modern life in a digital society, and also meets the requirements for employees of any professional field and skill level.The analysis of the current state of the problem under study allowed the authors to identify the main directions of digital transformation of the content of education, which are considered in the works of scholars.The article defines the concepts of "educational content" and "transformation" and establishes that the content of education is reflected in educational programs.The results of the research analysis are highlighted by the prospects and risks faced by both students and teachers of the Faculty of Humanities and Pedagogy of the National University of Life and Environmental Sciences of Ukraine and the Faculty of Pedagogical Education, Management and Art of the Hryhorii Skovoroda Pereiaslav-Khmelnytskyi State Pedagogical University in connection with the transition to distance and blended learning.For this purpose, a survey of students and teachers was conducted using Google Form.According to the survey, the current state of teacher education requires the introduction of digital technologies in the educational process and an imperative change in educational methods.The study made it possible to identify the main directions of digital transformation of educational content in pedagogical higher education institutions.
Research and Application of Energy Efficiency Optimization Algorithm for Spacecraft Simulation Platform

High performance computing plays an increasingly important role in the simulation and verification of the aerospace. The high-performance simulation and computing platform for spacecraft provides effective support for various professional fields such as spacecraft orbit design, mechanical structure analysis, electromagnetic simulation, etc. With the increasing scale and quantity of spacecraft, all kinds of simulation tasks tend to be complex and require high solving time, which puts forward higher requirements for the computing capability of the platform. While improving the computing power of the platform, its performance and scale are growing year by year, and its energy consumption is also growing synchronously. This paper combines the characteristics of various professional computing tasks in spacecraft simulation, experimental verifies with the actual operation data, analyzes the platform energy consumption model, and compares the optimized scheduling algorithm with the conventional scheduling algorithm. On the premise of not affecting the platform throughput and operation time, the overall energy consumption of the platform is reduced by an average of 25%, effectively improving the energy efficiency of the platform and saving the operation cost.
A Note on the Confusion Coefficient of Boolean Functions

Low confusion coefficient values can make side-channel attacks harder for vector Boolean functions in Block cipher. In this paper, we give new results of confusion coefficient for f ⊞ g, f ⊡ g, f ⊕ g and fg for different Boolean functions f and g, respectively. And we deduce a relationship on the sum-of-squares of the confusion coefficient between one n-variable function and two (n - 1)-variable decomposition functions. Finally, we find that the confusion coefficient of vector Boolean functions is affine invariant.
Design of Aerospace Cloud Computing Server Based on Docker Cluster

To solve the problems of different space-based application platforms, lack of unified specifications and poor real-time performance, this paper proposes a design scheme of space-based cloud server based on Docker cluster, drawing on the container technology and cloud native architecture used by ground cloud computing. The computing module of the space-based cloud server realizes network communication through the route switching module, and integrates a high-capacity storage module to complete data access. The real-time operating system running integrated container services on the computing module and the lightweight container cluster management framework realize the dynamic deployment and orchestration of space-based container applications. The design framework has been verified in the space-based cloud server prototype, and the test results show that the framework achieves universal high-performance computing, virtual resource integration scheduling, and autonomous task migration. In the future, it can be deployed to space-based satellite clusters to provide users with space-based cloud information services, including universal high-performance computing, information intelligent processing, etc.