Reviewerly/exHarmony huggingface dataset:
A Mathematical Theory of Knowledge for Intelligent Agents

Knowledge is a property that measures the degree of awareness of an agent about a target in an environment. The goal in conventional intelligent and cognitive agent development is to build agents that can be trained to gain knowledge about a target. The definition and operations of this knowledge associated to the agent is not clear, whereas these are required for developing a reliable, scaleable and flexible agent. In this paper, we take into account such requirements, and present a concise theoretical framework for the design of cognitive and rational intelligent agents, their properties and operations on targets. We present many illustrative examples and an experiment to show how the incorporation of different cognitive properties to an agent enables the agent to act rationally with improve generalization performance in an interactive environment.
Fuzzy field theories in the string modes formalism

We review the formulation of the scalar field theory on the fuzzy sphere in terms of the string modes -functions optimally localized in position and momentum space.We show how this greatly simplifies the computation of loop contributions in position space and provides some new insights into the structure of the effective action.
Research on the Characteristics of English Texts on the Beijing Winter Olympics

This article focuses on the characteristics of the Beijing Winter Olympics English texts. It includes nominalization phenomenon, passive voice, compound sentences, attributive postpositions and fixed collocations and a summary of phrase expressions in Winter Olympics sports events. Combined with the background of the Beijing Winter Olympics, domestic and foreign sports event manuscripts are focused on. This paper analyzes contents, significance and future prospects of sports language development. It is beneficial to Chinese sports power construction, and could form a great social atmosphere for people.
Analysis of the Employability of Fresh Graduates in Double-carbon Industries Based on Web Crawler and Visualization Technology

Web crawlers are usually used to obtain a large amount of information, and combined with visualization technology, the obtained information can be expressed more intuitively. The dual-carbon industry has developed rapidly since the release of the dual-carbon policy, but the fresh graduates do not know enough about their employment information. Based on this, this article uses python to crawl the dual-carbon job descriptions on the job-hunting network for fresh graduates, and visualize them. Finally, make reasonable suggestions for the school and the government.
A Validated Ontology for Metareasoning in Intelligent Systems

Metareasoning suffers from the heterogeneity problem, in which different researchers build diverse metareasoning models for intelligent systems with comparable functionality but differing contexts, ambiguous terminology, and occasionally contradicting features and descriptions. This article presents an ontology-driven knowledge representation for metareasoning in intelligent systems. The proposed ontology, called IM-Onto, provides a visual means of sharing a common understanding of the structure and relationships between terms and concepts. A rigorous research method was followed to ensure that the two main requirements of the ontology (integrity based on relevant knowledge and acceptance by researchers and practitioners) were met. The high accuracy rate indicates that most of the knowledge elements in the ontology are useful information for the integration of multiple types of metareasoning problems in intelligent systems.
USING DIGITAL TECHNOLOGIES PROBLEMS IN STUDENT PROJECT TEAMS KNOWLEDGE MANAGEMENT

The article discusses the issues students' ownership of digital technologies and notes the problems associated with the insufficient development of students' digital competencies in the team project work implementation. In contrast to the popular opinion about the negative impact on the use of digital technologies in the educational process of the adaptation difficulties to software products of the university teaching staff, the article authors focus on the primitive possession of digital products and gadgets by students.&#x0D; One of the main problems the authors highlight is the underdevelopment of students' skills that allow them to immerse themselves in the study of software functionality, the search for possible digital products for consumption and the study of the modern digital services market. This is due to the peculiarities of the social category under consideration, which are presented in the article, as well as the prevailing consuming type of behavior among students and the presence of only superficial knowledge about the technologies functioning.&#x0D; The authors of the article consider the need for students to develop knowledge management competencies of project teams in a digital environment and using modern digital technologies, describe a few difficulties that arise when trying to establish digital interaction and knowledge management when working with student project teams and analyze the causes of their occurrence.
MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution

Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR; however, the existing CNN-based SR methods cannot fully search for background information in the measurement of feature extraction. In addition, in most cases, different scale factors of image SR are assumed to be different assignments and completed by training different models, which does not meet the actual application requirements. To solve these problems, we propose a multi-scale learning wavelet attention network (MLWAN) model for image SR. Specifically, the proposed model consists of three parts. In the first part, low-level features are extracted from the input image through two convolutional layers, and then a new channel-spatial attention mechanism (CSAM) block is concatenated. In the second part, CNN is used to predict the highest-level low-frequency wavelet coefficients, and the third part uses recursive neural networks (RNN) with different scales to predict the wavelet coefficients of the remaining subbands. In order to further achieve lightweight, an effective channel attention recurrent module (ECARM) is proposed to reduce network parameters. Finally, the inverse discrete wavelet transform (IDWT) is used to reconstruct HR image. Experimental results on public large-scale datasets demonstrate the superiority of the proposed model in terms of quantitative indicators and visual effects.
A Novel Metaphor Graph Drawing Method for Multidimensional Data Visualisation and Its Case Study on COVID-19 Vaccination Analysis

Visualisation techniques have been one of the best data processing and analysis methods in recent decades, and they have assisted in data understanding efforts in various fields. Visualisation techniques for low-dimensional data are well developed and applied in multiple sectors; however, multidimensional data visualisation techniques still present some limitations, such as inaccurate data comparison and perception, exaggerated visual differences, label occlusion, and overlap. This study addresses the pros and cons and proposes a novel graphical drawing method, the multidimensional rose chart. It adopts the design idea of the Nightingale rose chart, but overcomes relevant limitations. The main challenges of this area include the incomplete presentation of multidimensional data, the neglect of the linkage of multiple attributes, the inefficient use of space, and the lack of simplicity of the interface. Contributions include enriching the representations of multidimensional data through the use of colour shades, area, and height sizes to represent values; straightforward data attribute comparisons via graph nesting; and detailed attributes showing the use of specific value labels. To verify the preliminary validity of this method, we imported COVID-19 data into experiments and further compared the final layouts with traditional methods, such as the line chart, bar chart, tree, parallel coordinate chart, and Nightingale rose chart, as well as their structures, functionalities, clear advantages, and disadvantages. The experimental results show that multidimensional rose diagrams perform effectively in presenting multidimensional data when comparing other graph drawing methods in our case, and the outcomes match existing works' conclusions in related COVID-19 research sectors. This work has the potential to provide a suitable supplemental approach to the multidimensional data analysis.
Biomedical Applications using Hand Gesture with Electromyography Control Signal

Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG), force myography (FMG), and electrical impedance tomography (EIT) based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios. Index Terms : FMG; EMG; EIT; biological signal; human–system interactivities.
A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd

In populated cities, we often find crowded events like political meetings, religious festivals, music concerts, and events in shopping malls, which have more safety issues. Smart surveillance systems are used in big cities to keep crowds safe and make crowd security less complicated and more accurate. However, the surveillance systems proposed for a crowd are monitored by human agents, which are inefficient, error-prone, and overwhelming. Even with deep learning-based feature engineering in crowds, many variants of crowd analysis still lack attention and are technically unaddressed. Considering this scenario, the smart system requires the most advanced techniques to monitor the security of the crowd. Crowd analysis is commonly divided into crowd statics and behavior analysis. This paper explores more about crowd behaviour analysis, pedestrian and group detection which describes the movements that are noticed in the crowd image. Subsequently, the issues of the current methodology of pedestrian detection, datasets, and evaluation criteria are analyzed. Keyword : Crowd Analysis, Pedestrian and group detection, deep learning, Crowd IoT analysis, Human Activity Recognition.
The Expressivity of Quantified Group Announcements

Abstract Group announcement logic (GAL) and coalition announcement logic (CAL) allow us to reason about whether it is possible for groups and coalitions of agents to achieve their desired epistemic goals through truthful public communication. The difference between groups and coalitions in such a context is that the latter make their announcements in the presence of possible adversarial counter-announcements. As epistemic goals may involve some agents remaining ignorant, counter-announcements may preclude coalitions from reaching their goals. We study the relative expressivity of GAL and CAL and provide some results involving their more well-known sibling APAL. We also discuss how the presence of memory alters the relationship between groups and coalition.
Internet crimes to legal regulation

With the spread of modern means of technology among individuals in societies and the widespread use of them and the expansion of dealing through them, every individual in society can communicate without any barriers or geography, with the availability of the ability to transmit and receive information and technologies, to obtain data and programs with all ease, and with the many advantages and benefits that accompanied the emergence of this new and advanced technology of science and knowledge, but this was accompanied by the emergence of many problems and negatives that appeared in the form of a crime committed by some users of technology, which is characterized by its danger and ease of committing and the dilemma of crossing the borders, which can be called cyber. It was necessary to confront these crimes through multiple means, including criminal legal protection, which is considered the most important means of society in maintaining its security and protecting its interests.
Hybrid Encryption Model using One-Time Pad and Route Cipher algorithm with Integrity Check (Using MD5 hashing)

The world is moving forward in terms of internet connectivity. While this enhances connectivity and strengthens communication, it brings with it its shortcomings. The advent of internet went hand in hand with emergence of hackers. This made businesses vulnerable and prone to malicious attacks by various groups and organizations. The business, to safeguard the data of the clients and consumers had to fool proof their servers and data-centres, hence the need to enhance and tighten security and close various vulnerable points. Cryptography is a technique involving encryption of data and messages so that only the intended audience can decrypt and comprehend it. For enhanced security, a hybrid encryption technique along with integrity check (MD5 hashing) is used. The encryption technique of One Time Pad (OTP - symmetric) and Route cipher (trans- positional) are combined and used.
MANDELBROT FRACTALS USING FIXED-POINT TECHNIQUE OF SINE FUNCTION

Here, we develop escape criteria for p c (z) = sin(z n ) -az + c, a, c ∈ C, n ≥ 2, exploiting four different iterations of fixed point theory to explore various Mandelbrot sets which are different than the classical Mandelbrot set .Our concern is to utilize the lesser number of iterations that are necessary to attain the fixed point of the transcendental complex-valued sine function.Further, we investigate the effect of variables on the shape, size, color, and dynamics of fractals.Noticeably, some of the obtained fractals symbolize the Swastika (a symbol of spirituality and divinity in Indian religions), Shivling (an abstract representation of the Hindu God Shiva), flowers, spiders, butterflies, Rangoli (made mainly in the festive season in India), art on glass, and so on.Interestingly, the higher-order Mandelbrot set in Picard-orbit has a resemblance to Corona-virus.
Towards Adversarial Robustness of Deep Vision Algorithms

Deep learning methods have achieved great success in solving computer vision tasks, and they have been widely utilized in artificially intelligent systems for image processing, analysis, and understanding. However, deep neural networks have been shown to be vulnerable to adversarial perturbations in input data. The security issues of deep neural networks have thus come to the fore. It is imperative to study the adversarial robustness of deep vision algorithms comprehensively. This talk focuses on the adversarial robustness of image classification models and image denoisers. We will discuss the robustness of deep vision algorithms from three perspectives: 1) robustness evaluation (we propose the ObsAtk to evaluate the robustness of denoisers), 2) robustness improvement (HAT, TisODE, and CIFS are developed to robustify vision models), and 3) the connection between adversarial robustness and generalization capability to new domains (we find that adversarially robust denoisers can deal with unseen types of real-world noise).
Novel transfer learning schemes based on Siamese networks and synthetic data

Transfer learning schemes based on deep networks which have been trained on huge image corpora offer state-of-the-art technologies in computer vision. Here, supervised and semi-supervised approaches constitute efficient technologies which work well with comparably small data sets. Yet, such applications are currently restricted to application domains where suitable deepnetwork models are readily available. In this contribution, we address an important application area in the domain of biotechnology, the automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation, where data characteristics are very dissimilar to existing domains and trained deep networks cannot easily be adapted by classical transfer learning. We propose a novel transfer learning scheme which expands a recently introduced Twin-VAE architecture, which is trained on realistic and synthetic data, and we modify its specialized training procedure to the transfer learning domain. In the specific domain, often only few to no labels exist and annotations are costly. We investigate a novel transfer learning strategy, which incorporates a simultaneous retraining on natural and synthetic data using an invariant shared representation as well as suitable target variables, while it learns to handle unseen data from a different microscopy tech nology. We show the superiority of the variation of our Twin-VAE architecture over the state-of-the-art transfer learning methodology in image processing as well as classical image processing technologies, which persists, even with strongly shortened training times and leads to satisfactory results in this domain. The source code is available at https://github.com/dstallmann/transfer_learning_twinvae, works cross-platform, is open-source and free (MIT licensed) software. We make the data sets available at https://pub.uni-bielefeld.de/record/2960030.
The “Every Child A Scientist” program: using digital technologies to enhance science learning among middle grade students from corporation schools in Chennai

The “Every Child A Scientist” (ECAS) program, since its inception in 2002, has emphasized the importance of experiential and activity based learning to supplement standard “chalk and talk” learning. In a modified version of this program (2021–22), 153 middle school students from two corporation schools of Chennai participated in a two-week science learning sessions using digital e-learning modules. The present study summarises the outcomes of this program. The customised course content developed based on the state school curriculum, delivered using digital smart boards, with audio-visual learning, hands on practical sessions, provided easy grasp of scientific concepts. Integration of practical laboratory based sessions bridged the gap between theory and practice. Simplifying science learning using digital tools has the potential to enhance school academic performance. Students could consciously relate scientific concepts to events in their day-to-day life. Bilingual teaching contributed to easy assimilation and understanding of science concepts. An enabling academic environment with approachable educators is crucial to science learning. Access to internet and desktop systems, awareness of internet based learning resources as secondary sources of learning facilitated and fostered self-learning. Providing nutri-dense meals in schools for overall cognitive development, better learning and physical growth will also ease parental pressures. Thus, technology and science based methods will provide solutions to big challenges in the future. In this context, providing quality science education using digital tools can help integrate socio-economically marginalised students into the main stream.
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints.
Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning

Deep Metric Learning (DML) is a group of techniques that aim to measure the similarity between objects through the neural network. Although the number of DML methods has rapidly increased in recent years, most previous studies cannot effectively handle noisy data, which commonly exists in practical applications and often leads to serious performance deterioration. To overcome this limitation, in this paper, we build a connection between noisy samples and hard samples in the framework of self-paced learning, and propose a \underline{B}alanced \underline{S}elf-\underline{P}aced \underline{M}etric \underline{L}earning (BSPML) algorithm with a denoising multi-similarity formulation, where noisy samples are treated as extremely hard samples and adaptively excluded from the model training by sample weighting. Especially, due to the pairwise relationship and a new balance regularization term, the sub-problem \emph{w.r.t.} sample weights is a nonconvex quadratic function. To efficiently solve this nonconvex quadratic problem, we propose a doubly stochastic projection coordinate gradient algorithm. Importantly, we theoretically prove the convergence not only for the doubly stochastic projection coordinate gradient algorithm, but also for our BSPML algorithm. Experimental results on several standard data sets demonstrate that our BSPML algorithm has better generalization ability and robustness than the state-of-the-art robust DML approaches.
MES-Attacks: Software-Controlled Covert Channels based on Mutual Exclusion and Synchronization

Multi-process concurrency is effective in improving program efficiency and maximizing CPU utilization. The correct execution of concurrency is ensured by the mutual exclusion and synchronization mechanism (MESM) that manages the shared hardware and software resources. We propose MES-Attacks, a new set of software-controlled covert channel attacks based on MESM to transmit confidential information. MES-Attacks offer several advantages: 1) the covert channels are constructed at software level and can be deployed on any hardware; 2) closed share of resource ensures the quality of the channels with low interference and makes them hard to be detected; and 3) it utilizes the system's software resources which are abound and hence difficult to isolate. We built covert channels using different MESMs on Windows and Linux, including Event, Timer, FileLockEX, Mutex, Semaphore and flock. Experimental results demonstrate that these covert channels can achieve transmission rate of 13.105 kb/s, 12.383 kb/s, and 6.552 kb/s, respectively in the scenarios of local, cross-sandbox and cross-VM, where the bit error rates are all under 1\%.