Priorities for Improving Distance Education and Human Capital in Azerbaijan Public Administration

Distance education can also be used by a large part of the community. The form of distance education optimally develops learners' independence, activity, consciousness, and creativity in comparison with traditional forms of education. In addition, in this system, more scientific information can be obtained in a short time, and the student's conscious and logical ability to acquire systematic knowledge expands. On the other hand, distance education is one of the tools that can help our higher education to have an international rating. Let us take into account that one of the main tasks of the education system is to develop the human capital necessary for the modernization of the country and thus increase the international competitiveness of the Republic of Azerbaijan. It should be noted that digitalization can create real opportunities to turn our weak performance in education into strong performance. In this regard, the chapter examines the prospects for improving distance education in public administration for the development of human capital in Azerbaijan.

Research Progress on Precipitation Accuracy Verification and Statistical Post-Processing in Ensemble Numerical Forecast System

The temporal and spatial accuracy of precipitation of ensemble numerical forecast systems is an important factor that affects the level of meteorological and hydrological coupled forecasting. This article focuses on the current research of verification of precipitation accuracy and statistical post-processing. The verification of forecast precipitation accuracy mainly focuses on the probabilistic characteristics such deterministic accuracy, the resolution, the forecasting skills and the degree of dispersion. Some mainstream statistical post-processing methods have strong performance of spatial downscaling and error correction, but they commonly have the defect of destroying the temporal and spatial dependent structure of precipitation. A comprehensive statistical post-processing method integrated the three functions is the development direction in the future. At the same time, statistical post-processing methods to improve the certainty and probabilistic accuracy of forecast precipitation need to be systematically identified. Its impact on the spatio-temporal dependence structure also needs to be improved.

DIPLOMASI PUBLIK SEBAGAI NATION BRANDING DENGAN TERPILIHNYA INDONESIA SEBAGAI TUAN RUMAH FIBA WORLD CUP 2023

ABSTRAK Diplomasi publik dengan menggunakan nation branding untuk meningkatkan citra positif Indonesia dimata dunia dalam bidang olahraga bola basket, hingga terpilihnya Indonesia sebagai tuan rumah FIBA World Cup 2023. Pemerintah Indonesia menggunakan aktor-aktor non negara seperti atlit-atlit profesional dan beberapa organisasi untuk menjalankan programnyayaitu Perbasi dan IOC, dengan mengikuti berbagai persyaratan sebagai tuan rumah melalui partisipasi kualifikasi FIBA Asia 2021 dan perbaikan venue. Peningkatkan prestasi bola basket dalam ranah regional, nasional, dan internasional. Performa yang kuat oleh negara tuan rumah akan sangat berpengaruh dalam mengarahkan minat lokal dan masyarakat internasional yang dibutuhkan untuk kesusksesan turnamen ini, dilihat dari track record perkembangan Tim Nasional Indonesia yang sudah diakui oleh FIBA mengalami kemajuan yang cukup baik. Melakukan naturalisasi pemain, untuk menjadi hosting country memerlukan banyak hal untuk memenuhi beberapa kritteria sesuai dengan standarisasi event.Kata kunci : Diplomasi publik, Nation branding, Perbasi, FIBAABSTRACTPublic diplomacy by using nation branding to enhance Indonesia's positive image in the eyes of the world in the field of basketball, until the election of Indonesiaas the host of the FIBA World Cup 2023. The Indonesian government uses non-state actors such as professional athletes and several organizations to carry out its programs, namely Perbasi and IOC, by following various requirements as hosts through the participation of FIBA Asia 2021 qualifications and venue improvements. Enhancing basketball achievements in the regional, national and international realms. The strong performance by the host country will be very influential in directing the local and international community interest needed for the success of this tournament, seen from the track record of the development of the Indonesian National Team which has been recognized by FIBA as having progressed quite well. Naturalizing players, becoming a hosting country requires many things to meet several criteria in accordance with event standardization. Keywords: Public diplomacy, Nation branding, Perbasi, FIBA

The Finnish Academic Profession’s Divided Opportunities in Management and Governance

Finnish higher education consists of research-oriented universities and teaching-oriented universities of applied sciences, and both sectors have a role in research, development and innovation. This paper focuses on governance and management at the institutional and academic unit levels, based on responses to several questions in the APIKS survey regarding the influence of academics, performance targets of academic units and the influence of academics in decision making and workload. Institutions in both sectors of Finnish higher education emphasise strategies and are heavily reliant on public funding. Both sectors also have an orientation to strong performance management.

Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers

Problem: An application of Explainable Artificial Intelligence Methods for COVID CT-Scan classifiers is presented. Motivation: It is possible that classifiers are using spurious artifacts in dataset images to achieve high performances, and such explainable techniques can help identify this issue. Aim: For this purpose, several approaches were used in tandem, in order to create a complete overview of the classificatios. Methodology: The techniques used included GradCAM, LIME, RISE, Squaregrid, and direct Gradient approaches (Vanilla, Smooth, Integrated). Main results: Among the deep neural networks architectures evaluated for this image classification task, VGG16 was shown to be most affected by biases towards spurious artifacts, while DenseNet was notably more robust against them. Further impacts: Results further show that small differences in validation accuracies can cause drastic changes in explanation heatmaps for DenseNet architectures, indicating that small changes in validation accuracy may have large impacts on the biases learned by the networks. Notably, it is important to notice that the strong performance metrics achieved by all these networks (Accuracy, F1 score, AUC all in the 80 to 90% range) could give users the erroneous impression that there is no bias. However, the analysis of the explanation heatmaps highlights the bias.

Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings

While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, static word vectors continue to play an important role in tasks where word meaning needs to be modelled in the absence of linguistic context. In this paper, we explore how the contextualised embeddings predicted by BERT can be used to produce high-quality word vectors for such domains, in particular related to knowledge base completion, where our focus is on capturing the semantic properties of nouns. We find that a simple strategy of averaging the contextualised embeddings of masked word mentions leads to vectors that outperform the static word vectors learned by BERT, as well as those from standard word embedding models, in property induction tasks. We notice in particular that masking target words is critical to achieve this strong performance, as the resulting vectors focus less on idiosyncratic properties and more on general semantic properties. Inspired by this view, we propose a filtering strategy which is aimed at removing the most idiosyncratic mention vectors, allowing us to obtain further performance gains in property induction.

DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization

Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever. Here, we combine the advantages of the popular bandit-based HPO method Hyperband (HB) and the evolutionary search approach of Differential Evolution (DE) to yield a new HPO method which we call DEHB. Comprehensive results on a very broad range of HPO problems, as well as a wide range of tabular benchmarks from neural architecture search, demonstrate that DEHB achieves strong performance far more robustly than all previous HPO methods we are aware of, especially for high-dimensional problems with discrete input dimensions. For example, DEHB is up to 1000x faster than random search. It is also efficient in computational time, conceptually simple and easy to implement, positioning it well to become a new default HPO method.

PrimeNet: Adaptive Multi-Layer Deep Neural Structure for Enhanced Feature Selection in Early Convolution Stage

The colossal depths of the deep neural network sometimes suffer from ineffective backpropagation of the gradients through all its depths. Whereas, The strong performance of shallower multilayer neural structures prove their ability to increase the gradient signals in the early stages of training which easily gets backpropagated for global loss corrections. Shallow neural structures are always a good starting point for encouraging the sturdy feature characteristics of the input. In this research, a shallow, deep neural structure called PrimeNet is proposed. PrimeNet is aimed to dynamically identify and encourage the quality visual indicators from the input to be used by the subsequent deep network layers and increase the gradient signals in the lower stages of the training pipeline. In addition to this, the layerwise training is performed with the help of locally generated errors which means the gradient is not backpropagated to previous layers, and the hidden layer weights are updated during the forward pass, making this structure a backpropagation free variant. PrimeNet has obtained state-of-the-art results on various image datasets, attaining the dual objective of (1) compact dynamic deep neural structure, which (2) eliminates the problem of backwards-locking. The PrimeNet unit is proposed as an alternative to traditional convolution and dense blocks for faster and memory-efficient training, outperforming previously reported results aimed at adaptive methods for parallel and multilayer deep neural systems.

An examination of third- and fourth-graders’ equivalence knowledge after classroom instruction

The present correlational study examined third- and fourth-graders’ (N = 56) knowledge of mathematical equivalence after classroom instruction on the equal sign. Three distinct learning trajectories of student equivalence knowledge were compared: those who did not learn from instruction (Never Solvers), those whose performance improved after instruction (Learners), and those who had strong performance before instruction and maintained it throughout the study (Solvers). Learners and Solvers performed similarly on measures of equivalence knowledge after instruction. Both groups demonstrated high retention rates and defined the equal sign relationally, regardless of whether they had learned how to solve equivalence problems before or during instruction. Never Solvers had relatively weak arithmetical (nonsymbolic) equivalence knowledge and provided operational definitions of the equal sign after instruction.

Tiny Video Networks

Automatic video understanding is becoming more important for applications where real-time performance is crucial and compute is limited. Yet, accurate solutions so far have been computationally intensive. We propose efficient models for videos - Tiny Video Networks - which are video architectures, automatically designed to comply with fast runtimes and, at the same time are effective at video recognition tasks. The Tiny Video Networks run at faster-than-real-time speeds and demonstrate strong performance across several video benchmarks. These models not only provide new tools for real-time video applications, but also enable fast research and development in video understanding. Code and models are available.