Reviewerly/exHarmony huggingface dataset:
Work in Progress: An AI-Assisted Metaverse for Computer Science Education

This paper proposes the use of metaverse for computer science education by leveraging virtual reality (VR) and artificial intelligence (AI) technologies. VR glasses are used to record videos such as presentation slides and classroom lectures in the metaverse. AI technologies automatically generate customized class notes summarized from the video recordings. This education metaverse can accommodate students' digital avatars in different virtual workspaces, including collaboration spaces, laboratories, and classrooms. It consists of practical features that mimic a physical classroom setup, such as real-time voice chat, avatar movement, and shared screen presentations. In the metaverse journey, the AI notes generator module is used to create personalized class notes using technologies including optical character recognition, automatic speech recognition, natural language processing, and text-to-speech. We discuss current progress and future work to prototype an AI-assisted metaverse for proof-of-concept experiments in computer science education.
Rumyantsev Museum and Its Library: Controversy, Reorganization, Disbandment (1917—1921)

In 2022, the Russian State Library (RSL) celebrates the 160th anniversary of the Rumyantsev Museum, which has changed its status and official name several times throughout its history (after the February Revolution of 1917 — the State Rumyantsev Museum). The article considers the internal processes that took place in the Museum at the beginning of the 20th century, which resulted in its disbandment in 1918—1921 and the formation of the main library of the country based on its book holdings. This determines the scientific novelty of the study, which for the first time raises questions about the causes of these events. The source base of the research is the materials of the RSL Archive. For the first time, the author introduces into scientific circulation the official notes of employees, designed to make the necessary changes to get out of the critical situation in which the largest cultural centre of Moscow turned out to be before 1917 and in the post-revolutionary period. The presented notes were written by two groups of specialists of the museum, who proposed divergent ways of its further existence. Some of the staff considered it necessary to preserve the integrity of the museum and its further development. Another group insisted on the separation of the library from the museum and on its obtaining the status of an independent institution. For the first time, the article also introduces into scientific circulation the archival documents of the “Minutes Meetings of the Scientific Council” (later the Collegium) of the State Rumyantsev Museum, covering the stages of making decision on the disbandment and further transfer of the monuments of the museum’s branches to other institutions throughout the country. The documents from the RSL Archive reflect the controversy on this issue. In the wake of social changes, the internal museum disputes have gone beyond its borders. When discussing projects, representatives of the central libraries and the new Government spoke out in support of transformation. The process of finding the way out of the crisis situation led to the disbandment of the State Rumyantsev Museum and the formation of the All-Union Public Library named after V.I. Lenin (then, the V.I. Lenin State Library of the USSR, and nowadays the Russian State Library) based on its book collections.
Panakos: Chasing the Tails for Multidimensional Data Streams

System operators are often interested in extracting different feature streams from multi-dimensional data streams; and reporting their distributions at regular intervals, including the heavy hitters that contribute to the tail portion of the feature distribution. Satisfying these requirements to increase data rates with limited resources is challenging. This paper presents the design and implementation of Panakos that makes the best use of available resources to report a given feature's distribution accurately, its tail contributors, and other stream statistics (e.g., cardinality, entropy, etc.). Our key idea is to leverage the skewness inherent to most feature streams in the real world. We leverage this skewness by disentangling the feature stream into hot, warm, and cold items based on their feature values. We then use different data structures for tracking objects in each category. Panakos provides solid theoretical guarantees and achieves high performance for various tasks. We have implemented Panakos on both software and hardware and compared Panakos to other state-of-the-art sketches using synthetic and real-world datasets. The experimental results demonstrate that Panakos often achieves one order of magnitude better accuracy than the state-of-the-art solutions for a given memory budget.
Theoretical Foundation and Design Guideline for Reservoir Computing-Based MIMO-OFDM Symbol Detection

In this paper, we derive a theoretical upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The specific RC implementation considered in this paper is the echo state network (ESN), and an upper bound on its generalization error is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard ERC measure and a direct application of its definition, our work uses the standard ERC measure and tools allowing fair comparison with conventional RNNs. The derived result shows that the generalization error bound obtained for ESNs is tighter than the existing bound for vanilla RNNs, suggesting easier generalization for ESNs. With the ESN applied to symbol detection in MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) systems, we show how the derived generalization error bound can guide underlying system design. Specifically, the derived bound together with the empirically characterized training loss is utilized to identify the optimum reservoir size in neurons for the ESN-based symbol detector. Finally, we corroborate our theoretical findings with results from simulations that employ 3GPP standards-compliant wireless channels, signifying the practical relevance of our work.
BENIGREEN: Blockchain-Based Energy-Efficient Privacy-Preserving Scheme for Green IoT

The energy conservative extension of the Internet of Things (IoT), green IoT, is a revolutionary approach in connecting people, processes, and things in an energy-efficient way. The existing research works in the domain of green IoT forbid the use of decentralized management (e.g., blockchain) of data due to its intrinsic disadvantages of block mining, transaction incentives, and less throughput. However, the advantages of blockchains urge the development of new decentralized strategies utilizing the architecture of green IoT. The infancy stage of the conjunction between green IoT and blockchains, and the need of decentralization in energy management in green IoT motivate us for the present research. In this article, we address the problems of decentralization, energy conservation, and privacy simultaneously. We introduce the first blockchain-based privacy-preserving framework for green IoT. We name this framework “Blockchain-based energy-efficient and privacy-preserving data management scheme for GREEN-iot (BENIGREEN)” for smart cities. BENIGREEN uses weight metrics for energy-efficient cluster heads (CHs) selection. The use of weight metrics is a novel contribution in the field of green IoT. Furthermore, we integrate a decentralized blockchain framework with an authentication scheme for secure transmission among base station (BS) and sensor nodes by employing registration, certification, and revocation phases. Consequently, BS allocates the collected information from CHs to decentralized blockchain and cloud storage. The BS eliminates all malicious nodes from the network by employing a certificate revocation process. We execute thorough experiments in terms of the operation time, throughput, average energy consumption, and computational latency. The comparative analysis with the state-of-the-art schemes show that BENIGREEN is efficient for IoT paradigm.
Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations

Robotic detection of people in crowded and/or cluttered human-centered environments including hospitals, stores and airports is challenging as people can become occluded by other people or objects, and deform due to clothing or pose variations. There can also be loss of discriminative visual features due to poor lighting. In this letter, we present a novel multimodal person detection architecture to address the mobile robot problem of person detection under intraclass variations. We present a two-stage training approach using: 1) a unique pretraining method we define as Temporal Invariant Multimodal Contrastive Learning (TimCLR), and 2) a Multimodal YOLOv4 (MYOLOv4) detector for finetuning. TimCLR learns person representations that are invariant under intraclass variations through unsupervised learning. Our approach is unique in that it generates image pairs from natural variations within multimodal image sequences and contrasts crossmodal features to transfer invariances between different modalities. These pretrained features are used by the MYOLOv4 detector for finetuning and person detection from RGB-D images. Extensive experiments validate the performance of our DL architecture in both human-centered crowded and cluttered environments. Results show that our method outperforms existing unimodal and multimodal person detection approaches in detection accuracy when considering body occlusions and pose deformations in different lighting.
Recent Breakthroughs in Cognitive Informatics and Cognitive Computing towards Autonomous AI (Plenary Panel Report-I of IEEE ICCI*CC'22)

Basic research in Cognitive Informatics (CI) and Cognitive Computing (CC) provides fundamental theories of intelligence science for Autonomous AI (AAI) and cognitive systems. The field of CI and CC has led to general AI technologies triggered by the transdisciplinary advances in brain, intelligence, computer, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part I) on the “Recent Breakthroughs in Cognitive Informatics and Cognitive Computing towards AAI” in the 21st IEEE International ICCI*CC Conference (ICCI*CC'22). Strategic CI/CC applications are presented in cognitive systems, AAI, cognitive robots, intelligent vehicles, AI knowledge learning, autonomous intelligence generation, and safety-and-mission-critical systems.
Modeling Prototypical Preference Behavior and Diversity using Rank Score Characteristic Functions

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling.
Arabic hate speech detection system based on AraBERT

Tunisia has entered a phase freedom of speech with access to social media since the Jasmine Revolution in 2011. Toxic contents such as abusive and hateful speeches have become omnipresent on Tunisian social media. Considering the side effects of these toxic contents on the psychology of users, it is necessary to detect them automatically. The dialect of Tunisian is underrepresented. As a consequence, there is not enough data set. In this paper, we present the data collection process with the aim of having a Tunisian reference dataset, to evaluate different models of hate speech and abuse detection. We also present our neural network model based on AraBERT. Our experimental results on our dataset shows that the AraBERT model performs better with an F1 score of 0.99.
Semantic segmentation of remote sensing images using U-net and its variants : Conference New Technologies of Information and Communication (NTIC 2022)

The process of dividing aerial images into distinct segments based on their semantic content is a crucial aspect of computer vision research that has numerous real-world applications, including disaster monitoring, land mapping, weather forecasting, and agriculture. This work provides a comprehensive overview of the methods used for semantic segmentation of aerial images and how deep neural networks, especially convolutional neural networks and the U-net architecture, can be employed to achieve this. The methods discussed are trained on aerial image datasets, with the results demonstrating the effectiveness of using U-net and its variations for semantic segmentation of aerial imagery.
Enabling Cognitive Manufacturing in Heterogeneous Industrial Automation Systems

With the increased digitization in the manufacturing sector, cognitive computing entails great potential to improve services and production. This is also referred to as cognitive manufacturing. The general idea is to simulate human cognitive processes - with the aim to improve decision making - by using machine learning (ML) to leverage the increased amount of data. However, the seamless adoption of cognitive computing and ML techniques to industrial automation systems on all abstraction levels is currently impeded by different challenges. Prominent blocking points are the heterogeneity of the systems, which impedes uniform data access and ML integration, and the lack of support for managing various ML life cycle phases. In this work, we propose a framework to manage data and ML life cycles in industrial automation systems. The framework comprises an architecture for the flexible integration of ML components (from component to cloud level) and their adaptive management (including retraining and updates). We address three phases of ML explicitly: pre-deployment, deployment, and post-deployment. We present first results and experiences of applying the framework to an industrial use case and discuss its future potential towards enabling cognitive manufacturing.
Three-Dimensional Measurement Method for High Dynamic Range Surfaces Based on Adaptive Fringe Projection

As an optical measurement method, fringe projection three-dimensional (3D) measurement technology will have poor measurement effect due to overexposure or too dark of the fringes when measuring high dynamic range (HDR) surfaces. The adaptive fringe projection method is one of the effective methods to solve the HDR surfaces measurement, but pixel matching is the difficulty. The existing matching methods generally use phase for matching, which requires at least 24 additional patterns to be projected, seriously affecting the measurement efficiency. In order to solve these problems, an adaptive fringe projection measurement method using speckle pattern for pixel matching is proposed in this paper. First, two grayscale patterns are projected to calculate the adaptive projection intensity. Then a speckle pattern is projected to match the pixels of the captured pattern and the projected pattern. Finally, the adaptive fringe patterns are generated. Better than the methods before, only three additional patterns are needed to be projected to realize the measurement of the HDR surfaces. The experimental results show that the proposed method can reduce the number of projected patterns by more than 59.5% while achieving similar measurement accuracy, which greatly improves the measurement efficiency.
Velocity Obstacle for Polytopic Collision Avoidance for Distributed Multi-Robot Systems

Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimization problem with positive distance constraints between robots, which limits real-time performance and scalability. Additionally, generating collision-free behavior for polytopic-shaped robots is harder due to implicit and non-differentiable distance functions between polytopes. In this letter, we extend the concept of velocity obstacle (VO) principle for polytopic-shaped robots and propose a novel approach to construct the VO in the function of vertex coordinates and other robot's states. Compared with existing work about obstacle avoidance between polytopic-shaped robots, our approach is much more computationally efficient as the proposed approach for construction of VO between polytopes is optimization-free. Based on VO representation for polytopic shapes, we later propose a navigation approach for distributed multi-robot systems. We validate our proposed VO representation and navigation approach in multiple challenging scenarios including large-scale randomized tests, and our approach outperforms the state of art in many evaluation metrics, including completion rate, deadlock rate, and the average travel distance.
Toward Fuzzy Activation Function Activated Zeroing Neural Network for Currents Computing

In order to improve the convergence and noise resistance ability of the ZNN models, a fuzzy activation function (FAF) is designed. Based on the FAF, a fuzzy activation function activated zeroing neural network (FAFZNN) for online fast computing circuit currents is proposed. By introducing the fuzzy logic technique, the convergence and noise resistance ability of the proposed FAFZNN model are further promoted, and it realizes prescribed-time stable, which is irrelevant to its system initial states even in noisy environment. Moreover, the prescribed-time convergence and strong robustness to noises of the proposed FAFZNN model are verified by strict mathematical analysis. The comparable simulation results for static direct currents (DC) and dynamic alternating currents (AC) computing in noiseless and noisy environment further validates its superior effectiveness and robustness for practical applications.
Design and Application of Teaching System for Chinese Language and Literature Major Drove by Interpreted AI Technology

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Application of intelligent analysis based on project management in development decision-making of regional economic development

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A model for measuring multi‐concern assurance of critical infrastructure control systems

Abstract Digital transformation of engineering practice, paradigms, processes, and workforce engender agreement uncertainty among professionals, particularly in the critical industry control system field. Control systems are susceptible to cyber‐mediated changes that can uniquely affect the control of the physical world from data‐centric information systems. Change to the system can be introduced by any proposed or forced alteration that affects the acceptability, suitability, feasibility, or resiliency to perform its intended mission, either positively or negatively. The potential impact of the cybersecurity threat on control systems is difficult to quantify. Agreement among professionals about decision authority and Command and Control (C2) over this threat is even more challenging to quantify. Understanding what cybersecurity entails still needs to be widely understood by the critical infrastructure control system workforce, and the control system assets are not widely understood by the Information Technology (IT) workforce. This research introduces a model and methodology for measuring multi‐concern assurance through the statistical uncertainty analysis of Likert semantic differential scales. The model addresses agreement in priority, the lack of which means there might be competing aims, competing spending, and competing focus on different aspects of the cybersecurity governance or policy as examples. The outcome identifies where different types of professionals do not agree about cybersecurity readiness and best practices for critical infrastructure control systems.
Is physical co-presence a prerequisite for Durkheimian collective effervescence? Reflections on remote working during the COVID-19 pandemic

This paper explores why it is that so many of us regard virtual communication technologies as imperfect substitutes for co-present organisational interaction. In so doing, it invokes Durkheim's concept of collective effervescence; that is, the bonding phenomenon experienced between people in physical proximity. Initially, ethnographic data are presented from a Scottish commune known as the Findhorn Foundation, where the word 'energy' is widely used by participants to describe the feelings associated with co-present interaction. Macrosocial data are then drawn from the 'Return, Reimagine, Reinvent' series of reports published by McKinsey & Co. which documents remote working experiences during the pandemic. Both data sets suggest that even in an era of advanced virtual connectivity, physical co-presence remains a prerequisite for collective effervescence. Furthermore, the data reveal that while virtual connections are useful for routine communication, our sense of collective effervescence must be periodically 'recharged' by means of intermittent physical assembly.
Perturbative boundaries of quantum advantage: Real-time evolution for digitized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>λ</mml:mi><mml:msup><mml:mi>ϕ</mml:mi><mml:mn>4</mml:mn></mml:msup></mml:math> lattice models

The real time evolution of quantum field theory models can be calculated order by order in perturbation theory. For $\ensuremath{\lambda}{\ensuremath{\phi}}^{4}$ models, the perturbative series have a zero radius of convergence which in part motivated the design of digitized versions suitable for quantum computing. In agreement with general arguments suggesting that a large field cutoff modifies Dyson's reasoning and improves convergence properties, we show that the harmonic digitizations of $\ensuremath{\lambda}{\ensuremath{\phi}}^{4}$ lattice field theories lead to weak coupling expansions with a finite radius of convergence. Similar convergence properties are found for strong coupling expansions. We compare the resources needed to calculate the real-time evolution of the digitized models with perturbative expansions to those needed to do so with universal quantum computers. Unless new approximate methods can be designed to calculate long perturbative series for large systems efficiently, it appears that the use of universal quantum computers with digitizations involving a few qubits per site has the potential for more efficient calculations of the real-time evolution for large systems at intermediate coupling.