Reviewerly/exHarmony huggingface dataset:
Student’s innovative competence as a priority direction of educational management

Introduction. The research is topical due to the need for fundamental reforms in the field of educational management. The problem of acquiring innovative competencies by the students arises against the background of the continual transformation of social relations. Future specialists who are currently students of higher educational institutions (HEIs) must meet all socio-economic challenges in the future. Methods: an adapted questionnaire was used to assess the level of skills in students of pedagogical HEIs and the method of diagnosis of the individual measure of reflexivity, the Rokeach Value Survey; methods of data collection and coordination, statistical and mathematical processing of results through Pearson’s formulas. Results: In the experimental and control groups, 7.6% of respondents have a low level, 82.6% — a medium level, and 9.8% — a high level. The experimental group (45%) showed a higher level of high reflexivity results than the control group (30%). Conclusion: Based on the processing of the obtained results, it is concluded that the development of innovative competencies in students of pedagogical HEIs after the application of pedagogical conditions increases quantitatively. Prospects: further research should be aimed at identifying key relevant innovation competencies.
A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix

The rapid development of social economy not only increases people's living pressure but also reduces people's health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction. In this paper, the linear sequential extreme learning machine algorithm can be used to evaluate the health status of a large number of data, analyze the differences of each evaluation index, and construct the analysis model of health status. Therefore, this paper introduces rough set theory into linear sequential extreme learning machine algorithm. Rough set can analyze the double analysis of evaluation scheme, predict the health development of different individuals, and improve the evaluation accuracy of mass health evaluation. The simulation results show that the improved line sequential extreme learning machine algorithm can accurately analyze the mass health and meet the needs of different individuals' health evaluation.
Flying Chameleons: A New Concept for Minimum-Deployment, Multiple-Target Tracking Drones

In this paper, we aim to open up new perspectives in the field of autonomous aerial surveillance and target tracking systems, by exploring an alternative that, surprisingly, and to the best of the authors' knowledge, has not been addressed in that context by the research community thus far. It can be summarized by the following two questions. Under the scope of such applications, what are the implications and possibilities offered by mounting several steerable cameras onboard of each aerial agent? Second, how can optimization algorithms benefit from this new framework, in their attempt to provide more efficient and cost-effective solutions on these areas? The paper presents the idea as an additional degree of freedom to be exploited, which can enable more efficient alternatives in the deployment of such applications. As an initial approach, the problem of the optimal positioning with respect to a set of targets of one single agent, equipped with several onboard tracking cameras with different or variable focal lengths, is addressed. As a consequence of this allowed heterogeneity in focal lengths, the notion of distance needs to be adapted into a notion of optical range, as the agent can trade longer Euclidean distances for correspondingly longer focal lengths. Moreover, the proposed optimization indices try to balance, in an optimal way, the verticality of the viewpoints along with the optical range to the targets. Under these premises, several positioning strategies are proposed and comparatively evaluated.
Towards Intelligently Designed Evolvable Processors

Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material's properties to achieve a specific computational function. This article addresses the question of how successful and well performing Evolution-in-Materio processors can be designed through the selection of nanomaterials and an evolutionary algorithm for a target application. A physical model of a nanomaterial network is developed which allows for both randomness, and the possibility of Ohmic and non-Ohmic conduction, that are characteristic of such materials. These differing networks are then exploited by differential evolution, which optimises several configuration parameters (e.g., configuration voltages, weights, etc.), to solve different classification problems. We show that ideal nanomaterial choice depends upon problem complexity, with more complex problems being favoured by complex voltage dependence of conductivity and vice versa. Furthermore, we highlight how intrinsic nanomaterial electrical properties can be exploited by differing configuration parameters, clarifying the role and limitations of these techniques. These findings provide guidance for the rational design of nanomaterials and algorithms for future Evolution-in-Materio processors.
Ftmar: A Fusion Transformer Network for Multi-Resident Activity Recognition

The booming development of smart homes ignites huge interest in human activity recognition (HAR). Current work mainly puts emphasis on single resident activity recognition in a room with little consideration of the multiple residents scenario. Besides, traditional methods accomplish identification based on the temporal relationship of data, ignoring the relationship among different sensors, which are hard to achieve satisfactory classification performance. In this paper, we put forward an end-to-end framework named Fusion Transformer Network for Multi-resident Activity Recognition (FTMAR) to address the above issues. Firstly, FTMAR preprocesses binary ambient sensor sequences into embedding matrices as network input. Secondly, in order to fully take advantage of ambient sensor information, two Transformer-based encoders: Sequence Encoder and Embedding Encoder, with respect to time sequence dimension and embedding dimension are constructed separately. Then, we design an ensemble strategy by employing the convolution layer for intermediate feature fusion between encoders. Finally, to handle the multi-resident challenge, two classification heads enable the identification of residents and activities simultaneously. Experimental results on CASAS dataset show that our approach can improve recognition performance both in resident and activity.
Prediction of Weather Patterns Using Linear Angstrom Model

The paper investigates the correlation between solar irradiance and sunlit hours for the city of Sharjah, United Arab Emirates. This correlation is a strong function of the weather conditions, and is vital for the design and operation of solar energy systems. The Angstrom Model is a straightforward tool that establishes a linear relationship where the model parameters b and a (slope and y-intercept, respectively) are evaluated through plotting of experimental measurements. Wind speed and relative humidity are collected to interpret their effect on the quality of incident solar radiation. The model takes into consideration winter and summer months of the year 2021, hence it provides a complete weather pattern for the year. With continuous data logging, the model will be developed to predict the weather patterns of the city of Sharjah.
Logic proves that time does not get faster or slower (the universe is not produced by the singularity big bang)

I use the axiom that equal conditions must have the same result. Axiom proves that no matter how the velocity of an object changes, the time of all objects remains unchanged and unified. Time can be expressed as an eternal constant. Time belongs to the abstract concept of material attributes, and time is not a material concept. There is an abstract concept of uniform velocity in the universe (For example, the velocity of light wave in vacuum is constant “C”). According to the constant and uniform velocity of time, an important physical theory is proved: the universe is not produced by the singularity big bang. Mathematical classification code: 00A79;83F05;00A30;03A05;70A05;70F20;03A10;03F03.
Construction of an Optimal Scheduling Method for Campus Energy Systems Based on Deep Learning Models

Aiming at the problem of high cost and low efficiency of planning and scheduling caused by load uncertainty of campus energy system, a 3-layer planning and scheduling model based on multivariate load prediction is proposed, mainly including prediction layer, planning layer, and scheduling layer; a long-term and short-term prediction model of multivariate load is constructed based on random forest regression network and long and short-term memory network. With the objective of minimizing the comprehensive planning and scheduling cost and the scheduling operation cost, the optimal comprehensive system cost and configuration scheme are obtained by using improved particle swarm algorithm and CPLEX solver; the equipment status and system cost are analyzed by planning and scheduling under different scenarios. By comparing the planning and scheduling results of the constructed 3-layer model with the conventional two-layer model, the economy and reliability of the 3-layer planning and scheduling model are demonstrated.
The Effect of English Reading Based on Embedded Sensor System on the Cultivation of College Students’ Humanistic Spirit

In order to better improve the cultivation goal of college students’ humanistic spirit, this paper puts forward the research on the role of English reading based on embedded sensor system in the cultivation of college students’ humanistic spirit. Embedded sensor system is used to carry out college English reading teaching in the humanistic teaching content in the teaching process. In addition, the evaluation index of college students’ English reading humanistic spirit cultivation is constructed, in order to achieve the goal of improving English reading teaching and task spirit training.
Comprehensive Accelerator-Dataflow Co-design Optimization for Convolutional Neural Networks

The design space of possible schedules for mapping a Convolutional Neural Network layer onto a spatial accelerator array, referred as the dataflow, is enormous. The co-design of key architectural parameters (such as number of processing elements, sizes of register files and scratchpad memories) along with the dataflow to optimize the implementation of one or more CNN stages makes the design space explosively larger. Several recent efforts have addressed the design-space exploration problem for CNN accelerators via heuristics or limited search strategies. In this paper we develop the first optimization approach that uses analytical modeling and the solution of constrained nonlinear optimization problems for comprehensive algorithm-architecture co-design optimization. Using the Timeloop accelerator modeling framework, we demonstrate that the new optimization methodology can enable significant improvements over prior accelerator designs for both energy minimization and performance maximization.
Conversational Information Seeking: Theory and Evaluation

Research in conversational information seeking (CIS) is moving very rapidly in various directions such as user interaction, system design, and evaluation. The tutorial focuses on the theoretical foundations and information-seeking processes for CIS, as well as their evaluation. The tutorial aims to introduce and communicate CIS research to the community and discuss it from different perspectives, such as theoretical modelling, evaluation, and user simulation. Also, it aims at gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Generating fake human faces using One Class SVM

This work presents a new model that generates automatically new fake human faces after learning from a training set containing real ones. The proposed model, based on One Class Support Vector Machine (OCSVM), was formulated mathematically and an effective iterative algorithm was proposed to implement it. Several tests, using a large dataset of human face pictures, have shown that our method can generate successfully face images of persons that don't really exist.
Design of High-Confidence Embedded Operating System based on Artificial Intelligence and Smart Chips

Design of the high-confidence embedded operating system based on artificial intelligence and smart chips is studied in this paper. The cooperative physical layer security system is regarded as a state machine. Relay nodes with untrusted behavior will affect the physical layer security of the system, and the system tries to prevent the untrusted behavior of relay nodes. While implementing public verification, it realizes the protection of data privacy. The third party can directly verify the data holding of the data stored in the cloud without verification by the user, and in the process of system expansion and growth, software can ensure vigorous vitality. For the verification, the smart chips are combined for the systematic implementations. The experimental results have shown the satisfactory results.
Memory-Throughput Trade-off for CNN-Based Applications at the Edge

Many modern applications require execution of Convolutional Neural Networks (CNNs) on edge devices, such as mobile phones or embedded platforms. This can be challenging, as the state-of-the art CNNs are memory costly, whereas the memory budget of edge devices is highly limited. To address this challenge, a variety of CNN memory reduction methodologies have been proposed. Typically, the memory of a CNN is reduced using methodologies such as pruning and quantization. These methodologies reduce the number or precision of CNN parameters, thereby reducing the CNN memory cost. When more aggressive CNN memory reduction is required, the pruning and quantization methodologies can be combined with CNN memory reuse methodologies. The latter methodologies reuse device memory allocated for storage of CNN intermediate computational results, thereby further reducing the CNN memory cost. However, the existing memory reuse methodologies are unfit for CNN-based applications that exploit pipeline parallelism available within the CNNs or use multiple CNNs to perform their functionality. In this article, we therefore propose a novel CNN memory reuse methodology. In our methodology, we significantly extend and combine two existing CNN memory reuse methodologies to offer efficient memory reuse for a wide range of CNN-based applications.
AI-Based Medical Voice Assistant During Covid-19

Presently, healthcare is the most important domain across the globe. Amidst this pandemic, all the doctors are much occupied and extremely busy. Getting proper treatment from doctor is getting difficult due to unavailability of doctors. Moreover, if a person feels uneasy and does not know what exactly the problem is, they really do not know which doctor they should take an appointment with. So, in this chapter intend to provide a web application as an all-in-one solution with an integrated voice assistant, which can help you with the process of disease diagnosis. For those doctors and patients who may need a second brain to make sure the diagnosis is correct, this is a platform with an AI-based medical voice assistant. It can work verbally with the doctor/patient and can assist him/her with the diagnostic process. This voice assistant should be able to select a patient’s illness on a confidence score to support diagnosis operations. Such a software can help both physicians and patients. Depending on the predicted disease/illness, the doctor may give the patient an e-prescription using the help of a voice assistant. The patient can also order medication using a voice assistant. The archives are stored in an orderly fashion, so users do not have to worry about losing them. This application is exclusively made to assist doctors and patients with intention of giving medical care in an interactive, lifesaving and resource saving manner. This application is a minor step in achieving a bigger target of completely digitalizing our medical care. .
Design and implementation of information application platform for primary medical institutions based on artificial intelligence

Primary-level medical institutions are the first line of defense for the health of primary-level residents, and their information construction is an important means to promote the development of primary-level public health services and realize the modernization of medical services. The informatization of primary medical institutions has practical requirements for optimizing the medical treatment process, improving the service capabilities of high-quality medical resources, and reducing costs. This article focuses on the needs of information-based intelligent services in primary medical institutions, using new-generation artificial intelligence technologies such as speech recognition, text structuring, and experience interaction to build an electronic medical record collection system for primary medical institutions; based on the electronic medical record data collected by the system, primary medical institutions integrates the original basic public health service platform to open up various public health service platforms by using service-oriented institutional design, focuses on the intelligent services of medical record content verification, retrieval and data, and builds an artificial intelligence-based information service platform for primary medical institutions. It improves the quality of medical care, work efficiency and reduce operating costs fundamentally.
RECOLO: A Python package for the reconstruction of surface pressure loads from kinematic fields using the virtual fields method

In experimental mechanics, conducting non-intrusive measurements of surface pressure distributions acting on blast-loaded structures remains a challenge even in controlled, laboratory environments (see e.g., (Pannel et al., 2021)).Still, for the design of tomorrow's sustainable and material-efficient structures, detailed knowledge of how pressure loads from extreme loading events interact with deformable structures is essential.When pressure loads are imposed on a deformable structure, fluid-structure interaction (FSI) effects are known to cause non-trivial loading scenarios which are difficult to quantify (see e.g., (Aune et al., 2021)).This project aims at reconstructing the full-field surface pressure loads acting on a deforming structure employing the virtual fields method (VFM) on full-field kinematic measurements (Kaufmann et al., 2019).Even though the current framework is limited to reconstructions of full-field pressure information from deformation data of thin plates in pure bending, it also allows for future extensions to other loading and deformation scenarios.Provided that the properties of the structure are known, the transient pressure distribution on the plate can be reconstructed.To understand the capabilities and accuracy associated with the pressure reconstruction methodology, the package provides tools for performing virtual experiments based on analytical data or data from finite element simulations.The current implementation is based on the deflectometry technique, using the grid method to obtain the deformation measurements and corresponding kinematics of the structure.
Multi-stage Optimal Control Problem Formulation for Drone Racing Through Gates and Tunnels

Finding a time-optimal trajectory through gates and tunnels is a difficult challenge in autonomous drone racing, especially in a fully autonomous context where the computations are all performed onboard. This paper presents an optimal control problem formulation to represent and solve the motion planning problem for a multi rotor drone racing through a series of gates or tunnels, without a priori knowledge about the drone pose right in front of or behind the gates nor about the exact time instance when to pass through the gates. The formulation is shown to produce feasible trajectories for a set of handcrafted configurations, and has the potential to be fast enough for online replanning purposes.
Role stereotypes in software designs and their evolution

Role stereotypes are abstract characterisations of the responsibilities of the building blocks of software applications. The role a class plays within a software system reflects its design intention. Wirfs-Brock introduced the following six role stereotypes: Information Holder, which knows information, Structurer, which maintains object relationships, Service Provider, which offers computing services, Coordinator, which delegates tasks to others, Controller, which directs other’s actions, and Interfacer, which transforms information. Knowledge about class role stereotypes can help various software development and maintenance tasks, such as program understanding, program summarisation, and quality assurance. This paper presents an automated machine learning-based approach for classifying the role-stereotype of classes in Java projects. We analyse this approach’s performance against a manually labelled ground truth for three open source projects that contain 1,500+ Java classes altogether. The contributions of this paper include: (i) a machine learning (ML) approach to address the problem of automatically inferring role-stereotypes of classes in Object-Oriented Programming Languages, (ii) the manually labelled ground truth, (iii) an evaluation of the performance of the classifier, (iv) an evaluation of the generalisability of the approach, and (v) an illustration of new uses of role-stereotypes. The evaluation shows that the Random Forest algorithm yields the best classification performance. We find, however, that the performance of the ML-classifier varies a lot for different role stereotypes. In particular, its performance degrades when classifying rarer stereotypes. Among the 23 features that we study, features related to the classes’ collaboration characteristics and complexity stand out as the best discriminants of role stereotypes.
Evaluating FFT-based Algorithms for Strided Convolutions on ARMv8 Architectures?

Convolutional Neural Networks (CNNs) have been widely adopted in all kinds of artificial intelligence applications. Most of the computational overhead of CNNs is mainly spent on convolutions. An effective approach to reducing the overhead is FFT-based fast algorithms for convolutions. However, current FFT-based fast implementations cannot be directly applied to strided convolutions with a stride size of greater than 1. In this paper, we first introduce rearrangement- and sampling-based methods for applying FFT-based fast algorithms on strided convolutions. Then, the highly optimized parallel implementations of the two methods on ARMv8-based many-core CPU are presented. Lastly, we benchmark the implementations against the two GEMM-based implementations on this ARMv8 CPU. Our experimental results with convolutions of different kernel, and feature maps, and batch sizes show that the rearrangementbased method generally exceed the sampling-based one under the same optimizations in most cases, and these two methods can get much better performance than GEMMbased ones when the kernel, feature maps and batch sizes are large. The experimental results with the convolutional layers in popular CNNs further demonstrate the conclusion above.