Reviewerly/exHarmony huggingface dataset:
The Inner Loop of Collective Human–Machine Intelligence

With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred to as Machine Theory of Mind (MToM). In this paper, we introduce the inner loop of human-machine teaming expressed as communication with MToM capability. We present three different approaches to MToM: (1) constructing models of human inference with well-validated psychological theories and empirical measurements; (2) modeling human as a copy of the AI; and (3) incorporating well-documented domain knowledge about human behavior into the above two approaches. We offer a formal language for machine communication and MToM, where each term has a clear mechanistic interpretation. We exemplify the overarching formalism and the specific approaches in two concrete example scenarios. Related work that demonstrates these approaches is highlighted along the way. The formalism, examples, and empirical support provide a holistic picture of the inner loop of human-machine teaming as a foundational building block of collective human-machine intelligence.
Extending the kinematic theory of rapid movements with new primitives

The Kinematic Theory of rapid movements, and its associated Sigma-Lognormal, model 2D spatiotemporal trajectories. It is constructed mainly as a temporal overlap of curves between virtual target points. Specifically, it uses an arc and a lognormal as primitives for the representation of the trajectory and velocity, respectively. This paper proposes developing this model, in what we call the Kinematic Theory Transform, which establishes a mathematical framework that allows further primitives to be used. Mainly, we evaluate Euler curves to link virtual target points and Gaussian, Beta, Gamma, Double-bounded lognormal, and Generalized Extreme Value functions to model the bell-shaped velocity profile. Using these primitives, we report reconstruction results with spatiotemporal trajectories executed by human beings, animals, and anthropomorphic robots.
Two-dimensional structural motion estimation via analytical signal enhancement of phase-based video processing

This paper concerns the use of phase information from image sequences for the extraction of structural two-directional motions. The phase-based motion estimation (PME) estimates local phase information via Gabor wavelet transform, and can only estimate the motion in single direction at a time, which will reduce the applicability and efficiency for many structures subject to more sophisticated bidirectional motions. This paper introduces a two-dimensional partial analytical signal to estimate the instantaneous phase and theoretically derives the relationship between the phase variations and the structure motion to achieve bidirectional motion estimations simultaneously. In addition to the analytical signal phase-based motion estimation (APME), this paper designs an ideal narrow-band filter (INF) addressing the limitation of the narrow frequency band in the analytical signals. In the proposed APME method, the acquisition of instantaneous phase improves the accuracy of structural bidirectional motion estimation. Moreover, the realization of the partial analytical signal is based on the fast Fourier transform (FFT) of the image and the dot product in the frequency domain, which bypasses the traditional PME convolution operation in the spatial domain. Therefore, the calculation efficiency of structural motion estimation is greatly improved. The proposed method is validated using a numerical simulation of a moving Gaussian surface with random motions. Results prove a good accuracy of the proposed APME method, and outperforming the traditional PME, the APME demonstrates an outstanding consistence to the true values (correlation coefficient 99.99%) four times better calculation efficiency. Further comparison with the experimental results of a cantilever beam also shows that the proposed method can better identify the structure bidirectional motion estimation.
MDHO: Mayfly Deer Hunting Optimization Algorithm for Optimal Obstacle Avoidance Based Path Planning Using Mobile Robots

Mobile robot becomes more significant in human life and industry, whereas navigation of robot in the dynamic environment results a challenging problem and it need to be solved in an efficient way. Path planning gained more attention in recent decades and puts its practical usage in different industries. Path planning for the mobile robot is to determine feasible path to reach target location in workspace. A more challenging problem with mobile robot is solving path planning issue by avoiding obstacles in an optimize way. Various methods are designed to perform path planning mechanism, but it faced complexity in finding the solution to reach the target. Hence, an efficient Mayfly Deer Hunting Optimization (MDHO) algorithm is designed in this research to move the mobile robots to reach target location in the environment using multi-objective function. However, multi-objective function is designed by considering the factors, like path length, path smoothness, and the obstacle avoidance. The path that satisfies the objective constraints is selected as optimal path to reach the target of mobile robot. The proposed model attains minimum path length, maximal path smoothness, and maximum fitness as 1159.0 m, 0.913, and 3.5418 by considering fixed obstacles and multiple targets.
Bioinformational philosophy and postdigital knowledge ecologies

Click to increase image sizeClick to decrease image size Additional informationFundingThis work was supported by The Leverhulme Trust.
Smart Multimedia Information Retrieval

The area of multimedia information retrieval (MMIR) faces two major challenges: the enormously growing number of multimedia objects (i.e., images, videos, audio, and text files), and the fast increasing level of detail of these objects (e.g., the number of pixels in images). Both challenges lead to a high demand of scalability, semantic representations, and explainability of MMIR processes. Smart MMIR solves these challenges by employing graph codes as an indexing structure, attaching semantic annotations for explainability, and employing application profiling for scaling, which results in human-understandable, expressive, and interoperable MMIR. The mathematical foundation, the modeling, implementation detail, and experimental results are shown in this paper, which confirm that Smart MMIR improves MMIR in the area of efficiency, effectiveness, and human understandability.
Completeness in partial type theory

Abstract The present paper provides a completeness proof for a system of higher-order logic framed within partial type theory. The framework is a modification of Tichý’s extension of Church’s simple type theory, equipped with his innovative natural deduction system in sequent style. The system deals with both total and partial (multiargument) functions-as-mappings and also accommodates algorithmic computations arriving at various objects of the framework. The partiality of a function or a failure of a computation is not represented by a postulated null object such as the third truth value. The logical operators of the system are classical. Another welcome feature of this expressive system is that its consequence relation is monotonic.
Modeling Vehicle Insurance Adoption by Automobile Owners: A Hybrid Random Forest Classifier Approach

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The framework addresses the class imbalance problem in binary classification tasks by employing principal component analysis for feature selection, the synthetic minority oversampling technique for oversampling, and the random forest ML classifier for prediction. The results demonstrate that the proposed hybrid framework outperforms the conventional approach and achieves better accuracy. The purpose of this study is to provide insurance managers and practitioners with novel insights into how to improve prediction accuracy and decrease financial risks for the insurance industry.
Monoidal reverse differential categories

Abstract Cartesian reverse differential categories (CRDCs) are a recently defined structure which categorically model the reverse differentiation operations used in supervised learning. Here, we define a related structure called a monoidal reverse differential category , prove important results about its relationship to CRDCs, and provide examples of both structures, including examples coming from models of quantum computation.
Demonising Migrants in Contexts of Extremism: Analysis of Hate Speech in UK and Spain

Hate speech has been seen as a problem within democratic societies that has been exacerbated by social media. While platforms claim to moderate content, this proves impossible. Studying popular platforms in the UK and Spain and examining content within community pages dedicated to right-wing parties, we use framing analysis to identify the predominant frames in user comments that contained hate speech against migrants. Our research demonstrates a frequent use of arguments that encourage xenophobic and discriminatory attitudes to flourish. Specifically, we find that immigrants are commonly framed as potential criminals, people who steal resources and erode norms of the dominant culture and traditions. The fact that these frames are commonly used is worrying and indicates xenophobic attitudes exist within both societies under study. However, it is difficult to imagine regulatory systems that would prevent these attitudes from being expressed. Rather, we argue that it is more important to focus on correcting the conditions that cause such attitudes to take hold within a society.
Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining

Images captured on rainy days are prone to rain streaking on various scales. These images taken on a rainy day will be disturbed by rain streaks of varying degrees, resulting in degradation of image quality. This study sought to eliminate rain streaks from images using a two-stage network architecture involving progressive multi-scale recovery and aggregation. The proposed multi-scale aggregation residual channel attention fusion network (MARCAFNet) uses kernels of various scales to recover details at various levels of granularity to enhance the robustness of the model to streaks of various sizes, densities, and shapes. When applied to benchmark datasets, the proposed method outperformed other state-of-the-art schemes in the restoration of image details without distorting the image structure.
Steps in Introducing Reading Books to Children

The purpose of the article is to educate the children by reading books. It shows the step by step in introducing reading books to children. An introducing reading books to children on the early age is important. Books are a storehouse of knowledge that needs to be presenting to the children until they grow up . The role of parents towards children is very important because the way parents educate children will determine their abilities and creativity for their future. Parents should be responsible for the growth and development of children in building children's character by introducing small things around children such as books. The function is to ensure that the small family in their own children are provide facilities and infrastructure to develop skills as provisions in social life, as well as media in instilling social and cultural values as early as possible. The method used in this study is the descriptive method and qualitative approach.
Neural Architecture Search for GNN-Based Graph Classification

Graph classification is an important problem with applications across many domains, for which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. In the literature, to adopt GNNs for the graph classification task, there are two groups of methods: global pooling and hierarchical pooling. The global pooling methods obtain the graph representation vectors by globally pooling all of the node embeddings together at the end of several GNN layers, whereas the hierarchical pooling methods provide one extra pooling operation between the GNN layers to extract hierarchical information and improve the graph representations. Both global and hierarchical pooling methods are effective in different scenarios. Due to highly diverse applications, it is challenging to design data-specific pooling methods with human expertise. To address this problem, we propose PAS (Pooling Architecture Search) to design adaptive pooling architectures by using the neural architecture search (NAS). To enable the search space design, we propose a unified pooling framework consisting of four modules: Aggregation, Pooling, Readout, and Merge. Two variants, PAS-G and PAS-NE, are provided to design the pooling operations in different scales. A set of candidate operations is designed in the search space using this framework. Then, existing human-designed pooling methods, including global and hierarchical ones, can be incorporated. To enable efficient search, a coarsening strategy is developed to continuously relax the search space, and then a differentiable search method can be adopted. We conduct extensive experiments on six real-world datasets, including the large-scale datasets MR and ogbg-molhiv. Experimental results in this article demonstrate the effectiveness and efficiency of the proposed PAS in designing the pooling architectures for graph classification. The Top-1 performance on two Open Graph Benchmark (OGB) datasets 1 further indicates the utility of PAS when facing diverse realistic data. The implementation of PAS is available at: https://github.com/AutoML-Research/PAS.
Performance evaluation of high-dimensional quantum key distribution with single-photon detector based on avalanche photodiode

The current theoretical analysis model for high-dimensional quantum key distribution (QKD) was developed without carefully considering the dead time and after-pulse of the single-photon detectors. Here, we propose a new model that accounts for these two effects. With this model, we analyze the effects of various properties of the single-photon detector on the performance of the high-dimensional quantum key distribution and show numerically that the high-dimensional quantum key distribution can be achieved up to 180 km with InGaAs/InP-type single-photon detectors. This work can improve our understanding of the factors that determine the performance of high-dimensional quantum key distribution and will help to promote the application of single-photon detectors based on avalanche photodiodes in high-dimensional quantum key distribution.
Transmission of Digital Data in the 5G Era: Compression and Privacy

The vast majority of compressed digital data that flows nowadays on modern high-speed networks is directly related to human activity. It describes what we do, what we see and photograph, where we go, whom we meet, and specifically every moment of our lives. This brings up issues and concerns regarding the necessity to safeguard user privacy as well as to protect the digital multimedia contents that are delivered to offer new experiences. In this paper, we explore a unified approach to compression and privacy by considering different types of digital data (text, images, sound, and hyperspectral images).
Graph models for engineering design: Model encoding, and fidelity evaluation based on dataset and other sources of knowledge

Abstract Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-the-art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.
A Forecasting Approach to Cryptocurrency Price Index Using Reinforcement Learning

Cryptocurrency has emerged as a well-known significant component with both economic and financial potential in recent years. Unfortunately, Bitcoin acquisition is not simple, due to uneven business and significant rate fluctuations. Traditional approaches to price forecasting have proven incapable of proving adequate data and solutions because prices can now be forecast in real time. We recommended a machine learning-based alternative for a mortgage lender based on highlighted problems in forecasting the price of Bitcoin. The proposed system included a reinforcement learning algorithm for price estimation and forecasting, as well as a blockchain framework for an efficient and secure environment. The proposed prediction, compared to other state-of-the-art strategies in this sector, demonstrated better performance. In this system, the proposed prediction reached improved consistency, in comparison to other systems, with respect to Monero (XMR), Litecoin (LTC), Oryen (ORY), and Bitcoin (BTC).
Secure and Efficient Multicast-Enabled Handover Scheme Pertaining to Vehicular Ad Hoc Networks in PMIPv6

In VANET, mobility management and handover management are two of the most intriguing and challenging research topics. The existing mobility management infrastructures are unable to provide seamless secure mobility and handover management. It is very common in a vehicular network that when a vehicle roams between two domains, its reachability status may be compromised. The main reason for this is the higher handover latency and packet loss during the handover process. In the last decade, IP-based mobility protocols have been proposed for interoperable handover management systems. There has been a great deal of interest in providing IP multicast to mobile nodes such as vehicles, and numerous strategies have been put forth thus far. This research article proposes an IP multicast-enabled handover architecture for VANET in PMIPv6. Adding the IP multicast facility to the authentication server allows handover management that is both intra-domain and inter-domain, which originally was not supported by PMIPv6. This makes it possible for the IP service of a vehicle to maintain a connection from any location, without changing the earlier application. Additionally, a secure architecture with authentication capabilities built on top of PMIPv6 is suggested for VANET to address the authentication problem. Finally, the article compares the performance of the proposed architecture with that of the ones currently in use by varying several factors, including the vehicle’s density, the setup costs required, and the unit transmission costs on wired and wireless links, and it shows that our proposed solution ensures the handover process with a minimal cost change.
Web-Conferencing Systems (WCS): Individual, Group or Full-Class Teacher Education Format?

Web-conferencing systems (WCS) are gaining momentum in the implementation of distance learning for young people, especially during and after the spread of COVID-19. In this regard, it seems urgent to identify technologies for distance learning education that correspond to modern teacher education to the fullest and are effective, user-friendly and convenient for all subjects of the educational process. This experiment revealed the most popular formats of communication for long-distance students in the implementation of future teachers’ professional training by means of video conferencing on the Zoom platform. The analysis of the implementation of various formats of interaction based on the questionnaire of research participants demonstrated a positive trend in the level of user expectations from educational interaction, technical preferences and students’ attitude to distance learning by means of web-conferencing. The implementation of distance learning using web-conferencing in group and full-class formats of interaction made it possible to increase social presence through the interactive cooperation of the students with each other and with the teacher in the educational and social context. These formats enhance social and cognitive interaction, thus developing key competencies in future teachers related to the ability to implement effective communication when working in a teaching team. It causes an increase in educational efficiency level and is critical, since it allows information to be obtained through interaction with each other and with the content presented on the screen. Thereby, educational distance communication with the help of web-conferencing should be considered as a new form of social and educational communication in the implementation of professional training rather than as a technical non-traditional tool for teacher education.
A Study of Privacy-Preserving Neural Network Prediction Based on Replicated Secret Sharing

Neural networks have a wide range of promise for image prediction, but in the current setting of neural networks as a service, the data privacy of the parties involved in prediction raises concerns. In this paper, we design and implement a privacy-preserving neural network prediction model in the three-party secure computation framework over secret sharing of private data. Secret sharing allows the original data to be split, with each share held by a different party. The parties cannot know the shares owned by the remaining collaborators, and thus the original data can be kept secure. The three parties refer to the client, the service provider and the third server that assist in the computation, which is different from the previous work. Thus, under the definition of semi-honest and malicious security, we design new computation protocols for the building blocks of the neural network based on replicated secret sharing. Experimenting with MNIST dataset on different neural network architectures, our scheme improves 1.3×/1.5× and 7.4×/47.6× in terms of computation time as well as communication cost compared to the Falcon framework under the semi-honest/malicious security, respectively.