Reviewerly/exHarmony huggingface dataset:
Quality management of higher education within the framework of the socio-investment model of economic growth: State audit and financial control

The article aims to study the contribution of quality management in higher education through state audit and financial control to the implementation of the socio-investment model of economic growth. The article is based on the scientific hypothesis that quality management in higher education should be based on university rankings. The article presents the authors’ view on the interpretation of recent results in the research field of quality management in higher education and reveals the strengths and weaknesses of the hypothesis. Systematization and critical analysis of the indicators of quality and effectiveness in higher education based on the leading university rankings for 2022—the Ministry of Education and Science of the Russian Federation—are performed. The case study method is used for the case analysis of the management of quality and effectiveness on the example of the leading Russian university, which has been the leader of these rankings for many years: Lomonosov Moscow State University (MSU). The scientific novelty and originality of the article are associated with a systematic view of the quality and effectiveness of higher education, which was first formed by the authors through a combination and grouping of indicators from the leading university rankings for 2022. The theoretical significance of the authors’ conclusions lies in the fact that the article clearly identifies a significant contribution of the quality control of education and the performance management of universities to the implementation of the socio-investment model of economic growth. The practical significance of the obtained results is that the scientific and methodological basis for monitoring and assessing quality and effectiveness in higher education based on the leading university ratings for 2022 makes it possible to improve the state audit and financial control of university activities in the implementation of the socio-investment model of economic growth.
Nonlinear Autoregressive Neural Networks for Air Temperature forecasting

In the field of climatic conditions forecasting, the linear classical time series models are inadequate for modelling and predicting accurately the air temperature variability. This work presents the novel application of nonlinear autoregressive neural networks (NAR) in air temperature forecasting. Hourly air temperature data were extracted from the Chania Airport station located at the island of Crete in Greece, for a 10-year period. The NAR is a multi-layer, dynamic, recurrent neural network that employs feedback connections for multi-horizon time series forecasting. Multiple NAR networks were trained with feedback connections for 6, 12 and 24 hours and for forecasting horizons up to 24-time steps. In the context of evaluating the performance of the trained NAR networks the Mean Absolute Error (MAE) was used and specifically the errors are examined in terms of their dependence with the atmospheric circulation. The results indicate that the use of a high degree of feedback decreases the forecasting error and increases the forecasting horizon of reliable air temperature estimation. The forecast error is dependent on the atmospheric circulation and higher MAE values are related with depressions that effect the study area.
As atitudes e comportamentos decorrentes da acção das campanhas de desnutrição em crianças realizadas pela ONG SD junto da população do distrito de Monapo – Nampula

In Mozambique 43% of children aged between 0 and 5 years old suffer from chronic malnutrition. Problems related to nutritional status are more prevalent in rural areas of the country, especially in the north, with greater emphasis on the provinces of Nampula and Cabo Delgado. In the various prevention and resolution measures, the communication tools, their strategies and actions can contribute to the information, awareness and awareness of the populations. The study presented here is the result of a doctoral investigation focusing on the effectiveness of social marketing campaigns to reduce malnutrition rates in children in the Monapo district, proposed by a non-governmental organization, which for ethical and protection reasons, the fictitious name SD was used. In this way, the main objective is based on understanding the attitudes and behaviors of pregnant mothers and mothers of children from 0 to 5 years of age in relation to the nutrition campaign. Thus, from a qualitative methodological approach perspective, in the context of the interpretive paradigm, the main results of the present investigation reveal a strong awareness of mothers about the risks associated with the problem, the benefits present in the ideal behavior and also in the face of a clear will to follow in accordance in the face of behavior. However, contextual and environmental issues proved to be a major obstacle to the practical applicability of these behaviors.
Integrating Blockchain technology for Nutritional Safety- A New Way Forward

The food industry is diversifying and adapting to shifting consumer tastes and environmental issues. Consumers value the quality and uniformity of the fundamental ingredients used to make packaged goods, as well as the longevity, originality, logistics, lifespan, and recyclability of packaging. For the global distribution of food, the safety of agricultural products is critical. In order to enhance efficiency and maximize product manufacturing and distribution, data and information technologies are becoming increasingly vital in the global food and agriculture industry. In the "food and agriculture" (FAI) industry, blockchain is a well-known technical innovation. It facilitates the timely sharing of information such as origin, batch number, and production date, as well as the openness and transparency of the manufacturing environment, food safety certification, and organic products, all of which contribute to food safety and customer trust.
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images Using Images, Normals and Vertices

This paper presents a novel architecture for simultaneous estimation of highly accurate optical flows and rigid scene transformations for difficult scenarios where the brightness assumption is violated by strong shading changes. In the case of rotating objects or moving light sources, such as those encountered for driving cars in the dark, the scene appearance often changes significantly from one view to the next. Unfortunately, standard methods for calculating optical flows or poses are based on the expectation that the appearance of features in the scene remains constant between views. These methods may fail frequently in the investigated cases. The presented method fuses texture and geometry information by combining image, vertex and normal data to compute an illumination-invariant optical flow. By using a coarse-to-fine strategy, globally anchored optical flows are learned, reducing the impact of erroneous shading-based pseudo-correspondences. Based on the learned optical flows, a second architecture is proposed that predicts robust rigid transformations from the warped vertex and normal maps. Particular attention is paid to situations with strong rotations, which often cause such shading changes. Therefore, a 3-step procedure is proposed that profitably exploits correlations between the normals and vertices. The method has been evaluated on a newly created dataset containing both synthetic and real data with strong rotations and shading effects. These data represent the typical use case in 3D reconstruction, where the object often rotates in large steps between the partial reconstructions. Additionally, we apply the method to the well-known Kitti Odometry dataset. Even if, due to fulfillment of the brightness assumption, this is not the typical use case of the method, the applicability to standard situations and the relation to other methods is therefore established.
Vital information matching in vision-and-language navigation

With the rapid development of artificial intelligence technology, many researchers have begun to focus on visual language navigation, which is one of the most important tasks in multi-modal machine learning. The focus of this multi-modal field is how to fuse multiple inputs, which is crucial for the integrated feedback of intrinsic information. However, the existing models are only implemented through simple data augmentation or expansion, and are obviously far from being able to tap the intrinsic relationship between modalities. In this paper, to overcome these challenges, a novel multi-modal matching feedback self-tuning model is proposed, which is a novel neural network called Vital Information Matching Feedback Self-tuning Network (VIM-Net). Our VIM-Net network is mainly composed of two matching feedback modules, a visual matching feedback module (V-mat) and a trajectory matching feedback module (T-mat). Specifically, V-mat matches the target information of visual recognition with the entity information extracted by the command; T-mat matches the serialized trajectory feature with the direction of movement of the command. Ablation experiments and comparative experiments are conducted on the proposed model using the Matterport3D simulator and the Room-to-Room (R2R) benchmark datasets, and the final navigation effect is shown in detail. The results prove that the model proposed in this paper is indeed effective on the task.
Social-Spiritual Face: Designing Social Reading Support for Spiritual Well-being

Techno-spiritual practices refer to the use of digital technologies to support various spiritual activities, such as scripture reading. While prior human-computer interaction studies largely focus on understanding techno-spiritual practices in both personal and ministry environments, there is a lack of design research that explores novel design opportunities, based on longitudinal field deployment. As an important techno-spiritual practice, this work focuses on scripture reading and investigates the design space of "social scripture reading," as it is often organized into small groups for successful behavior maintenance. We designed and evaluated BibleCell, a social scripture reading tool that supports personalized reading plans, scripture reading, and social sharing. After the third year of deployment, we performed a two-month user study in Korean Protestant churches to deepen our understanding of techno-spiritual practices in social contexts via in-depth interviews (n = 27). We report the major themes of social techno-spiritual practices, such as social motivators, social interaction patterns, and leadership roles. We discuss our findings using a novel design concept of social-spiritual awareness that considers both the social and spiritual aspects of interactions in social computing systems.
Diagnostics of the prescription of damage to the pancreas in explosive trauma and gunshot wounds during a military conflict

An independent set game is a cooperative game dealing with profit sharing in the maximum independent set problem. A population monotonic allocation scheme is a rule specifying how to share the profit of each coalition among its participants such that every participant is better off when the coalition expands. In this paper, we provide a necessary and sufficient characterization for independent set games admitting population monotonic allocation schemes. Moreover, our characterization can be verified efficiently.
An Efficient Motion Planning Method with a Lazy Demonstration Graph for Repetitive Pick-and-Place

Robotic systems frequently need to plan consecutive similar manipulation in some scenarios (e.g., pick-and-place tasks), leading to similar motion plans. Moreover, the workspace of a robot changes with the difference in operation actions, which affects subsequent tasks. Therefore, it is significant to reuse information from previous solutions for new motion planning instances to adapt to workplace changes. This paper proposes the Lazy Demonstration Graph (LDG) planner, a novel motion planner that exploits successful and high-quality planning cases as prior knowledge. In addition, a Gaussian Mixture Model (GMM) is established by learning the distribution of samples in the planning cases. Through the trained GMM, more samples are placed in a promising location related to the planning tasks for achieving the purpose of adaptive sampling. This adaptive sampling strategy is combined with the Lazy Probabilistic Roadmap (LazyPRM) algorithm; in the subsequent planning tasks, this paper uses the multi-query property of a road map to solve motion planning problems without planning from scratch. The lazy collision detection of the LazyPRM algorithm helps overcome changes in the workplace by searching candidate paths. Our method also improves the quality and success rate of the path planning of LazyPRM. Compared with other state-of-the-art motion planning algorithms, our method achieved better performance in the planning time and path quality. In the repetitive motion planning experiment of the manipulator for pick-and-place tasks, we designed two different experimental scenarios in the simulation environment. The physical experiments are also carried out in AUBO−i5 robot arm. Accordingly, the experimental results verified our method’s validity and robustness.
Quasilinear-time Computation of Generic Modal Witnesses for Behavioural Inequivalence

We provide a generic algorithm for constructing formulae that distinguish behaviourally inequivalent states in systems of various transition types such as nondeterministic, probabilistic or weighted; genericity over the transition type is achieved by working with coalgebras for a set functor in the paradigm of universal coalgebra. For every behavioural equivalence class in a given system, we construct a formula which holds precisely at the states in that class. The algorithm instantiates to deterministic finite automata, transition systems, labelled Markov chains, and systems of many other types. The ambient logic is a modal logic featuring modalities that are generically extracted from the functor; these modalities can be systematically translated into custom sets of modalities in a postprocessing step. The new algorithm builds on an existing coalgebraic partition refinement algorithm. It runs in time O((m+n) log n) on systems with n states and m transitions, and the same asymptotic bound applies to the dag size of the formulae it constructs. This improves the bounds on run time and formula size compared to previous algorithms even for previously known specific instances, viz. transition systems and Markov chains; in particular, the best previous bound for transition systems was O(mn).
RECURSOS INFORMACIONAIS EM ARTES: ACERVO BIBLIOGRÁFICO UNIVERSITÁRIO E SUSTENTABILIDADE CULTURAL

Constant movement is a reflection of our globalised society, where flows of individuals, images, data, etc. take place.One of the most characteristic activities to satisfy human needs in the world today is mobility.These flows lead to the establishment of links between different cultures and create synergies between different countries and communities.They cause an activation of cultural heritage and an interest in appraising one's own culture.Such growth in communication between Asian and European countries has changed the way we conceive, make, and produce, in the different areas of knowledge.This process, which arises as a result of reciprocal influences, has contributed to the work of European and American artists and designers, by providing new approaches to colour space, perspective, composition, drawing and subject matter.The different ways of creating have been intertwined and merged ever since these first cultural contacts between Japan and the West took place, thus provoking an enrichment of the arts at different times via different artistic movements such as Art Nouveau, Bauhaus, Constructivism, Futurism and Dada.This paper attempts to reveal how the exchange of works between Japan and the West fostered some profound changes in the way messages are materialised and communicated through images.The aim is to analyse these flows using examples from painting, graphic design and advertising, in order to understand how they affect different contexts and popular culture.
Research on Optimization of Neural Network Structure based on Variational Inequality: from the Perspective of Connection Structure

This paper analyzes the stability of generalized projective neural networks based on optimization theory, projective theory, stability theory of differential equations, and the principle of invariance, and gives solutions to generalized variational inequalities and a class of common sense monotonic linear variational inequalities. The new neural network model of the problem rigorously proves the various stability of the new chess pattern theoretically, especially the numerical examples of asymptotic stability show the feasibility and effectiveness of these networks at the same time. Due to the limitations of analysis methods, some existing neural networks for solving variational inequality problems (delay/non-delay) have relatively conservative stability conditions.
Grover's quantum search algorithm of causal multiloop Feynman integrals.

A proof-of-concept application of a quantum algorithm to multiloop Feynman integrals in the Loop-Tree Duality (LTD) framework is applied to a representative four-loop topology. Causality obtained through the LTD formalism, is a suitable problem to address with quantum computers given the straightforward possibility to encode the two on shell states of a propagator on the two states of a qubit. A modification of Grover's quantum search algorithm is developed for querying multiple solutions over the unstructured set of solutions generated after the application of the LTD. The quantum algorithm is successfully implemented on IBM Quantum and QUTE simulators.
Sand and Dust Storms Monitoring Using FY-4A Satellite Data based on Convolutional LSTM Networks

Improving the sand and dust storms monitoring capability can provide early warning information for sandstorms, and effectively improve the PV power prediction accuracy under sand and dust storms weather. In this paper, a hybrid model dust monitoring method based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network is proposed. Using the normalized difference dust index (NDDI), CNN model, and 1DCNN-LSTM hybrid model, combined with the number four meteorological satellite (FY-4A). The channel scanning imaging radiometer AGRI (Advanced Geostationary Radiation Imager) data is used to monitor and study the sand and dust storms of the Taklimakan Desert in southern Xinjiang. The results show that the NDDI dust index established by images at different times needs to take different thresholds to identify the dust area. There are misidentifications in the coverage area as well as in the desert area. The sand and dust storms monitoring model established based on CNN network and 1DCNN-LSTM network, the accuracy (Accuracy) and loss function (Loss) of training samples and test samples are 99.9% and 1%, respectively, which has strong sand and dust storms monitoring capabilities. In practical applications, the 1DCNN-LSTM model is better than the CNN model in processing the boundary between sand and non-sand dust. In addition, the 1DCNN-LSTM model can also more accurately identify sand and dust storms in the case of a small amount of cloud occlusion area.
Automated Gesture Controlled Presentation Using Machine Learning

Abstract: Now-a-days presentations have a significant role in different fields of life. Whether you are a student, an entrepreneur, businessman, or a corporate worker, you must have had given presentations at some point in your life. PowerPoint presentations sometimes become less lively because either you have to use the keyboard to change and operate the slides or use a dedicated gadget to perform these tasks. We aimed to enable people to control the slideshow with the gestures of hands. The applications of gestures in human-computer interaction have massively risen in the past few years. The research has tried to control different operations of the PowerPoint slideshow through gestures. This research has used Machine Learning to detect gestures with subtle differences and tried to map them with some fundamental PowerPoint slideshow controlling functions using Python. This project make the work of the presenter quite easy.
Feasibility Analysis and Implementation of Adaptive Dynamic Reconfiguration of CNN Accelerators

In multi-tasking scenarios with dynamically changing loads, the parallel computing of convolutional neural networks (CNNs) causes high energy and resource consumption in the system. Another critical problem is that previous neural network hardware accelerators are often limited to fixed scenarios and lack the function of adaptive adjustment. To solve these problems, a reconfiguration adaptive system based on the prediction of algorithm workload is proposed in this paper. Deep Learning Processor Unit (DPU) from Xilinx has excellent performance in accelerating network computing. After summarizing the characteristics of hardware accelerators and gaining an in-depth understanding of the DPU structure, we propose a regression model for CNNs runtime prediction and a guidance scheme for adaptive reconfiguration combined with the characteristics of Deep Learning Processor Unit. For different DPU sizes, the accuracy of the proposed prediction model achieves 90.7%. With the dynamic reconfiguration technology, the proposed strategy can enable accurate and fast reconfiguration. In the load change scenario, the proposed system can significantly reduce power consumption.
Cryptographic Hash Functions For Digital Stamping

The current study's objectives are to analyze a broad overview of hash function applications in cryptography and investigate the connections between digital signature applications and cryptographic hash functions. Applications of the hash function are widespread and used for a variety of purposes, including password hashing, file integrity verification, key derivation, time stamping, rootkit detection, and digital signatures. Cryptographic hash functions are a crucial tool used in many sections of data security. A digital signature is a code that is electronically associated with a document and includes the sender's information. As a result, the usefulness of the digital signature in validating digital messages or documents is great. Without mathematics, there would be no cryptographic hash functions. Mathematics is credited with computer science's success, or, to put it another way, it is because of mathematical science that computer science is understood and can be explained to everyone. The study's main goals are to explain hash functions to the reader, as well as some of their uses, including digital signatures, and to provide detailed examples of some hash functions and their creation. Keywords: Hash Function, Cryptography, Digital Stamping