Reviewerly/exHarmony huggingface dataset:
Robust Video Stabilization based on Motion Decomposition

Video stabilization aims to eliminate camera jitter and improve the visual experience of shaky videos. Video stabilization methods often ignore the active movement of the foreground objects and the camera, and may result in distortion and over-smoothing problems. To resolve these issues, this paper proposes a novel video stabilization method based on motion decomposition. Since the inter-frame movement of foreground objects is different from that of the background, we separate foreground feature points from background feature points by modifying the classic density based spatial clustering method of applications with noise (DBSCAN). The movement of background feature points is consistent with the movement of the camera, which can be decomposed into the camera jitter and the active movement of the camera. And the movement of foreground feature points can be decomposed into the movement of the camera and the active movement of foreground objects. Based on motion decomposition, we design first-order and second-order trajectory smoothing constraints to eliminate the high-frequency and low-frequency components of the camera jitter. To reduce content distortion, shape-preserving constraints, and regularization constraints are taken to generate stabilized views of all feature points. Experimental results demonstrate the effectiveness and robustness of the proposed video stabilization method on a variety of challenging videos.
Heterogeneous Graph Transformer for Meta-structure Learning with Application in Text Classification

The prevalent heterogeneous Graph Neural Network (GNN) models learn node and graph representations using pre-defined meta-paths or only automatically discovering meta-paths. However, the existing methods suffer from information loss due to neglecting undiscovered meta-structures with richer semantics than meta-paths in heterogeneous graphs. To take advantage of the current rich meta-structures in heterogeneous graphs, we propose a novel approach called HeGTM to automatically extract essential meta-structures (i.e., meta-paths and meta-graphs) from heterogeneous graphs. The discovered meta-structures can capture more prosperous relations between different types of nodes that can help the model to learn representations. Furthermore, we apply the proposed approach for text classification. Specifically, we first design a heterogeneous graph for the text corpus, and then apply HeGTM on the constructed text graph to learn better text representations that contain various semantic relations. In addition, our approach can also be used as a strong meta-structure extractor for other GNN models. In other words, the auto-discovered meta-structures can replace the pre-defined meta-paths. The experimental results on text classification demonstrate the effectiveness of our approach to automatically extracting informative meta-structures from heterogeneous graphs and its usefulness in acting as a meta-structure extractor for boosting other GNN models.
An Efficient and Robust Semantic Hashing Framework for Similar Text Search

Similar text search aims to find texts relevant to a given query from a database, which is fundamental in many information retrieval applications, such as question search and exercise search. Since millions of texts always exist behind practical search engine systems, a well-developed text search system usually consists of recall and ranking stages. Specifically, the recall stage serves as the basis in the system, where the main purpose is to find a small set of relevant candidates accurately and efficiently. Towards this goal, deep semantic hashing, which projects original texts into compact hash codes, can support good search performance. However, learning desired textual hash codes is extremely difficult due to the following problems. First, compact hash codes (with short length) can improve retrieval efficiency, but the demand for learning compact hash codes cannot guarantee accuracy due to severe information loss. Second, existing methods always learn the unevenly distributed codes in the space from a local perspective, leading to unsatisfactory code-balance results. Third, a large fraction of textual data contains various types of noise in real-world applications, which causes the deviation of semantics in hash codes. To this end, in this paper, we first propose a general unsupervised encoder-decoder semantic hashing framework, namely MASH (short for Memory-bAsed Semantic Hashing), to learn the balanced and compact hash codes for similar text search. Specifically, with a target of retaining semantic information as much as possible, the encoder introduces a novel relevance constraint among informative high-dimensional representations to guide the compact hash code learning. Then, we design an external memory where the hashing learning can be optimized in the global space to ensure the code balance of the learning results, which can promote search efficiency. Besides, to alleviate the performance degradation problem of the model caused by text noise, we propose an improved SMASH (short for denoiSing Memory-bAsed Semantic Hashing) model by incorporating a noise-aware encoder-decoder framework. This framework considers the noise degree for each text from the semantic deviation aspect, ensuring the robustness of hash codes. Finally, we conduct extensive experiments in three real-world datasets. The experimental results clearly demonstrate the effectiveness and efficiency of MASH and SMASH in generating balanced and compact hash codes, as well as the superior denoising ability of SMASH.
Report on Smart Education in the Republic of Slovenia

In the chapter, some elements of smart education in Slovenia are presented. The comprehensive introduction of Slovenian digital education started in 1994. The most significant development and implementation of teacher digital pedagogical competencies, digital school strategies, education e-materials, and digital pedagogy happened between 2005 and 2015. We present the strategic guidelines and development projects which have addressed some features of smart education in Slovenia after 2015: (1) smart multiple stakeholders communities of key and external stakeholders in education to discuss, rethink, and decide what society needs from the education systems; (2) renovated curriculum to be more future (less history) oriented with challenges to address more subjects; (3) theory and practice of digital pedagogy, personalized, and collaborative learning supported by further development of team lesson planning and evaluation, ongoing digital assessment, multiple on-time feedback as well as learning evidence and analytics; (4) smart teachers and their CPD on pedagogical digital competences, basic knowledge of computer sciences, computational thinking as well as problem solving and gamification; (5) comprehensive supportive ecosystem (virtual single sign-in entry point for each individual with access to the repositories (e-materials, e-services, evets)), digital platforms for learning and cooperation, e-communities, certificates and micro-credentials, administration platforms, digital leadership, equal opportunities, digital infrastructure, and modern school architecture and interior. Also, some contributions of Slovenia to the policy development at EU level regarding blended learning, frameworks on digital competency, AI, and Digital Education Hub are described.
Analysis of Smart Education in China and CEECs

This final chapter concludes the book with a summary of best practices in developing smart education in China and CEECs. As argued in the previous chapters, smart education could be considered as the high end of digital transformation in education. Thus, in this chapter, we summarize the country experts’ perspective of smart education together with suggestions on how to advance smart education globally.
Report on Smart Education in Greece

This chapter provides an overview of smart education in Greece. The provision of connectivity and ICT equipment in schools remains a key priority in many countries and Greece has made considerable efforts to upgrade the technological infrastructure and ensure access to a stable and high-speed connection to the Internet. The related policies of ICT in Greek education and some key projects are presented to describe the vision, philosophy, and goals of the national strategy for Digital Greece. Furthermore, the Ministry of Education in Greece is responsible for the implementation of considerable actions to promote the collection, organization, efficient search, and distribution of digital educational content for primary, secondary, and tertiary education following a set of specific guiding principles. To this extent, this chapter aims to uncover the best practices and current trends of smart education in Greece regarding the curriculum and practices that are conducted on promoting both teachers’ and students’ digital literacy, the innovative teaching methods and strategies that will support the deep integration of ICT in education, as well as the solutions and policies on open educational resources to promote equal and inclusive education.
Report on Smart Education in Montenegro

The aim of this paper is to investigate the level of smart education in Montenegro, i.e., to determine how different forms of this type of education can affect the acquisition of knowledge. Smart education is a very broad concept, which includes different types of stakeholders (school, students, parents, local community, government, etc.) and which characterizes the efficiency of teaching and adapting content, with the aim of achieving greater levels of functional knowledge. Therefore, such form of education implies the use of modern scientific and technological methods and tools, which include students, teachers, as well as parents, in order to accomplish the best possible effects. ICT and smart technology help teachers to monitor and evaluate every step that a student takes to solve a certain task (problem) or achieve a goal, but also help parents to better understand the way their children acquire knowledge. This chapter, among other things, will provide a review of the National Education Profile, i.e., it will include some of the following issues of the education system in Montenegro: review of ICT in education, information infrastructure and internet access, ICT policies in education, teaching reform and training of teachers. Additionally, special focus will be placed on the role of technological innovations and digitalization in the acquisition of knowledge during the Covid-19 pandemic. The results of the research can serve as recommendations to decision makers in the area of education of today's generations. Additionally, the results of this study will create a basis for the possibility of comparison with other countries, especially those in the region, i.e., those countries that have similar level of development, in order to work together with aim to improve the situation and strengthen competitiveness through education.
Mobile Assisted Language Learning Across Educational Contexts

Almost two decades have gone by since the printing of Kukulska-Hulme and Traxler’s (2007) seminal book Mobile learning: A handbook for educators and trainers. Evidently, there has been less debate concerning whether mobile technologies should be welcomed in language education. Instead, attention has been shifted towards ensuring the accessibility, equality, quality, safety, and sustainability of mobile-assisted language learning (MALL) (Kukulska-Hulme 2021b). With a broad collection from secondary to tertiary education and from formal to informal learning contexts, Mobile assisted language learning across educational contexts aims to offer up-to-date and comprehensive knowledge of the current advancement of MALL. With this book, Valentina Morgana and Agnes Kukulska-Hulme provide the readers with a new understanding of the effective approaches to integrating mobile technologies in language education. The book consists of seven chapters, which can be categorized into four parts. The opening chapter in Part 1, by Valentina Morgana, starts with the rationale for editing the volume by stressing its unique selling point—the inclusion of diverse educational contexts and methodologies in relation to MALL. The chapter also contains a short introduction to MALL research, including the distinctive features and the commonly adopted research methods, followed by a summary of the remaining chapters.
AN OVERVIEW OF CLOUD COMPUTING SECURITY CONCERNS

A flexible, affordable, and tested platform for delivering commercial or consumer IT services over the Internet is cloud computing.Pools of easily accessible virtualized resources make up cloud computing.People today are used to storing their personal data in the cloud.Using servers, cloud computing stores, manages, and processes data that is hosted online.However, because crucial services are frequently contracted out to a third party, it is more difficult to uphold data security and privacy, support data and service availability, and prove compliance.As a result, cloud computing poses an additional level of risk.Cloud computing makes use of a variety of technologies and it also inherits their security concerns, which are covered in this article.We identify the main vulnerabilities in these kinds of systems as well as the most significant threats found in the literature pertaining to cloud computing and its environment.We also relate these security holes and threats to potential fixes.
Application of Discrete Pruned Enumeration in Solving BDD

The bounded distance decoding (BDD) is a fundamental problem in lattice-based cryptography which is derived from the closest vector problem (CVP). In this paper, we adapt the lattice enumeration with discrete pruning, a burgeoning method for the shortest lattice vector problem (SVP), to solve BDD in various cryptanalysis scenarios using direct method. We first transfer the basic definition involved in discrete pruning technique from SVP to CVP, prove corresponding properties and give the specific procedures of the algorithm. Additionally, we use the discrete pruning technique to interpret the classical CVP algorithms, including Babai’s nearest plane and Lindner–Peikert nearest planes, which can be regarded as discrete pruned enumeration on some special pruning sets. We propose three probability models in the runtime analysis to accurately estimate the cost of our algorithm in different application scenarios. We study the application of discrete pruned enumeration for BDD mainly on LWE-based cryptosystem and DSA with partially known nonces. The experimental results show that our new algorithm has higher efficiency than the previous algorithms which directly solve BDD, including the nearest plane(s) algorithms and the lattice enumeration with classical pruning strategies, and we are able to recover the DSA secret with less leaked information than the previous works.
Text to Image Synthesis Using Multistage Stack GAN

Many recent studies on text-to-image synthesis decipher approximately 50% of the problem only. They failed to compute all the imperative details in it. This chapter presents a solution using stacked generative adversarial networks (GAN) to generate lifelike images based on the given text. The stage-I GAN creates a distorted images by depicting the rudimentary/basic colours and shape of a scene predicted on text illustration. Stage-II GAN ends up on generating high-resolution images with naturalistic features using Stage-I findings and the text description as inputs. The output generated by this technique is more credible than many other techniques which are already in use. More importantly, stack GAN produces 256 x 256 images based on the text descriptions, while the existing algorithms produces 128 x 128.
Artificial Intelligence Accountability in Emergent Applications

The rise of deep learning techniques has produced significantly better predictions in several fields which lead to a widespread applicability in healthcare, finance, and autonomous systems. The success of such models comes at the expense of a trackable and transparent decision-making process in areas with legal and ethical implications. Given the criticality of the decisions in such areas, governments and industries are making sizeable investments in the accountability aspect in AI. Accordingly, the nascent field of explainable and fair AI should be a focal point in the discussion of emergent applications especially in high-stake fields. This chapter covers the terminology of accountable AI while focusing on two main aspects: explainability and fairness. The chapter motivates the use cases of each aspect and covers state-of-the-art methods in interpretable AI and methods that are used to evaluate the fairness of machine learning models, and to detect any underlying bias and mitigate it.
Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications

Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network.
Skeleton Clustering: Dimension-Free Density-Aided Clustering

We introduce a density-aided clustering method called Skeleton Clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that are less dependent on the dimension but have intuitive geometric interpretations. The clustering framework constructs a concise representation of the given data as an intermediate step and can be thought of as a combination of prototype methods, density-based clustering, and hierarchical clustering. We show by theoretical analysis and empirical studies that the skeleton clustering leads to reliable clusters in multivariate and high-dimensional scenarios. Supplementary materials for this article are available online.
Segmentation of Nucleus and Cytoplasm from H&amp;E-Stained Follicular Lymphoma

This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&amp;E-stained image is projected onto L*a*b* color space to quantify the visual differences. Secondly, this transformed image is segmented with the help of k-means clustering into its three cytological components (i.e., nuclei, cytoplasm, and extracellular), followed by pre-processing techniques in the third step, where adaptive thresholding and the area filling function are applied to give them proper shape for further analysis. Finally, the demarcation process is applied to pre-processed nuclei based on the local fitting criterion function for image intensity in the neighborhood of each point. Integration of these local neighborhood centers leads us to define the global criterion of image segmentation. Unlike active contour models, this technique is independent of initialization. This paper achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation.
No-go theorem and a universal decomposition strategy for quantum channel compilation

We propose a general and systematic strategy to compile arbitrary quantum channels without using ancillary qubits, based on proximal policy optimization -- a powerful deep reinforcement learning algorithm. We rigorously prove that, in sharp contrast to the case of compiling unitary gates, it is impossible to compile an arbitrary channel to arbitrary precision with any given finite elementary channel set, regardless of the length of the decomposition sequence. However, for a fixed accuracy $\epsilon$ one can construct a universal set with constant number of $\epsilon$-dependent elementary channels, such that an arbitrary quantum channel can be decomposed into a sequence of these elementary channels followed by a unitary gate, with the sequence length bounded by $O(\frac{1}{\epsilon}\log\frac{1}{\epsilon})$. Through a concrete example concerning topological compiling of Majorana fermions, we show that our proposed algorithm can conveniently and effectively reduce the use of expensive elementary gates through adding the weighted cost into the reward function of the proximal policy optimization.
Soft-masks guided faster region-based convolutional neural network for domain adaptation in wind turbine detection

Wind turbine generator system plays a fundamental role in electricity generation in industry 4.0, and wind turbines are usually distributed separately and in poor locations. Unmanned Aerial Vehicles (UAV) which could overcome the above challenges are deployed to collect photographs of wind turbines, could be used for predictive maintenance of wind turbines and energy management. However, identifying meaningful information from huge amounts of photographs taken by drones is a challenging task due to various scales, different viewpoints, and tedious manual annotation. Besides, deep neural networks (DNN) are dominant in object detection, and training DNN requires large numbers of accurately labeled training data, and manual data annotation is tedious, inefficient, and error-prone. Considering these issues, we generate a synthetic UAV-taken dataset of wind turbines, which provides RGB images, target bounding boxes, and precise pixel annotations as well. But directly transferring the model trained on the synthetic dataset to the real dataset may lead to poor performance due to domain shifts (or domain gaps). The predominant approaches to alleviate the domain discrepancy are adversarial feature learning strategies, which focus on feature alignment for style (e.g., color, texture, illumination, etc. ) gaps without considering the content (e.g., densities, backgrounds, and layout scenes) gaps. In this study, we scrutinize the real UAV-taken imagery of wind turbines and develop a synthetic generation method trying to simulate the real ones from the aspects of style and content. Besides, we propose a novel soft-masks guided faster region-based convolutional neural network (SMG Faster R-CNN) for domain adaptation in wind turbine detection, where the soft masks help to extract highly object-related features and suppress domain-specific features. We evaluate the accuracy of SMG Faster R-CNN on the wind turbine dataset and demonstrate the effectiveness of our approach compared with some prevalent object detection models and some adversarial DA models.