Reviewerly/exHarmony huggingface dataset:
Multifractal complexity analysis-based dynamic media text categorization models by natural language processing with BERT

Fractals, being essentially mathematical constructs, are forms that embody the fundamental features of dynamism, self-organization, self-similarity and complexity. The lexical items and parts of sentences are comprehended as the constituents of schemata with a particular pattern made up of interacting elements. Among the most well-known means used to detect and analyze self-repeating patterns are multifractal methods which have numerous applications in many areas including computational linguistics. The predominance of properties like self-similarity, irregularity and vagueness in texts add more to the challenge of clear and accurate meaning conveyance. The ever-increasing amount of text data in different categories also contribute to the inherent complexity due to having properties like being unstructured, noisy and nonstandard. To address this challenge and complexity, this study has aimed at ensuring regularity and self-similarity within the digital-based complex media texts, which comprise the dataset, by multifractal methods (multifractal Bayesian, multifractal regularization and multifractal wavelet shrinkage) and attaining accurate classification and categorization of the words within texts in the dataset by Bidirectional Encoder Representations from Transformers (BERT), as the Natural Language Processing (NLP) method. The related steps of our integrative proposed method are as follows: firstly, regularity enhancement was attained by applying the multifractal methods (multifractal Bayesian, multifractal regularization and multifractal wavelet shrinkage) to the text dataset. Thus, the new datasets were generated, respectively, by obtaining the significant, self-similar and regular attributes. Subsequently, BERT, as the NLP method, was employed to the text dataset as well as to the three new datasets obtained for the classification purposes. In this way, accurate word detection within the text for the category classification was ensured for the analyses. The analysis results for the text dataset and the new datasets were compared by BERT and the most optimal result could be achieved by multifractal Bayesian method. Through this integrated scheme, we have enunciated the significance of the behavioral patterns of fractal while setting forth the distinctive quality of BERT owing to its capability of classification accuracy and adaptiveness into integrated methodologies.
A Cross-Platform HD Dataset and a Two-Step Framework for Robust Aerial Image Matching

Image matching has been an active research area in the computer vision community over the past decades. Significant advances in image matching algorithms have attracted attention from many emerging applications. However, aerial image matching remains demanding due to the variety of airborne platforms and onboard electro-optic sensors, long operational ranges, limited datasets and resources, and constrained operating environments. We present two contributions in this work to overcome these challenges: a) an upgraded cross-platform image dataset built over images taken from an aircraft and satellite and b) a two-step cross-platform image matching framework. Our dataset considers several practical scenarios in cross-platform matching and semantic segmentation. The first step in our two-step matching framework performs coarse-matching using a lightweight convolutional neural network (CNN) with help from aircraft instantaneous parameters. In the second step, we fine-tune standard off-the-shelf image matching algorithms by exploiting <i>spectral, temporal</i> and <i>flow</i> features followed by cluster analysis. We validate our proposed matching framework over our dataset, two publicly available aerial cross-platform datasets, and a derived dataset using various standard evaluation methodologies. Specifically, we show that both steps in our proposed two-step framework help to improve the matching performance in the cross-platform image matching scenario.
An 8.9–71.3 TOPS/W Deep Learning Accelerator for Arbitrarily Quantized Neural Networks

This brief presents the first general-purpose deep learning accelerator for arbitrarily quantized neural networks. A pre-processing and corresponding multiply-and-accumulate operator are proposed to perform the vector products between arbitrarily quantized two vectors. As a result, the accelerator can support precision scalability of quantization up to 4b for one of input activation and weight, and up to 8b for the other, respectively. The proposed approach shows a reduction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.57\times $ </tex-math></inline-formula> area and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.78\times $ </tex-math></inline-formula> power consumption, respectively, compared to those with the conventional 8b linear precision which is required to perform an equal level of task accuracy. The implemented accelerator in 28nm CMOS operates at an operating frequency range of 100-to-250MHz over a supply voltage range of 0.75-to-1.2V. Depending on configurations, it shows an energy efficiency of 8.9-to-71.3TOPS/W.
An Extended Framework for Semantic Interoperability in PaaS and IaaS Multi-cloud

Cloud computing is currently a widely used technology, helping enterprises focus on their value-added, rather than spending time and money acquiring and managing software or hardware solutions. Unfortunately, it suffers from the lock-in problem, which leads cloud customers to use specific solutions from a single cloud provider. On the other hand, the multi-cloud strategy is becoming the trend these days, as it remains the best way to maximize the benefits of cloud computing technology. Consequently, communication between solutions of different cloud providers is necessary to ease access and use of services in multi-cloud environments. Hence, even with efforts to standardize semantic interoperability in multi-cloud computing, this problem remains difficult due to the different representation models used by cloud providers. In this paper, we propose an extended version of a framework presented in a previous work dealing with semantic interoperability in a multi-cloud environment. This extended framework is based on five new components, which improves our previous framework and covers, in addition to the IaaS service model, the PaaS one. The main objective is the retrieval of IaaS resources and PaaS services easily.
Exploiting Spatial Attention and Contextual Information for Document Image Segmentation

We propose a new framework of combining an attention mechanism with a conditional random field to deal with a document image segmentation task. The framework aims to recognize homogeneous regions, e.g. text, figures, or tables, in document images through a pixel-wise spatial attention module. The attention module obtains essential global information and gathers long-distance pixel dependencies. To get extra knowledge around images, we use a conditional random field to model contextual information in the document. The new framework enables an effective combination of pixel features with their contextual information in the document image segmentation task. We conduct extensive experiments over multiple challenging datasets and demonstrate the performance of our new framework in comparison to a series of state-of-the-art segmentation methods.
Touch-based Augmented Reality Marking Techniques on Production Parts

In the machining of production parts, the quality of the surface is subject to a variety of influences. Tool wear, thermal effects and vibrations can affect the surface quality of the machined object drastically. Some of those effects on the surface, like marks from vibrations, can be seen by machining experts directly on the object without measurement hardware or metrology systems. Those pieces of information held by the experts are valuable to the surface finishing staff, the metrology staff and data scientists, who could use them to improve both the product and the process. However, to enable this exchange of information, the relevant data needs to be indicated in a spatially resolved and machine-readable format. Additionally, the experts need to be able to mark the areas for all kinds of different requirements in a fast and suitable manner without possibly damaging the part itself. Our approach for this task is using a touch-based Augmented Reality system that allows the users to mark these areas of interest on the production part digitally without touching the production part itself. Therefore, we propose two different marking techniques - Shift&Freeze Marking and Relative Marking - based on literature from pointing tasks. We performed user trials to evaluate the usability, task load and overall performance of those techniques showing a general suitability for touch-based AR marking of relevant regions on productions parts.
QL-CBR Hybrid Approach for Adapting Context-Aware Services

A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically.Most existing context-aware systems provide context-aware services based on supervised algorithms.Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trialand-error interactions.They also have the ability to build excellent self-adaptive systems.In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user's dynamic context.To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in real situations, we propose a combination of the Q-learning and case-based reasoning (CBR) algorithms.We then analyze how the incorporation of CBR enables Q-learning to become more efficient and adapt to changing environments by continuously producing suitable services.Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.
Stochastic Learning Rate With Memory: Optimization in the Stochastic Approximation and Online Learning Settings

In this letter, multiplicative stochasticity is applied to the learning rate of stochastic optimization algorithms, giving rise to stochastic learning-rate schemes. In-expectation theoretical convergence results of Stochastic Gradient Descent equipped with this novel learning rate scheme under the stochastic setting, as well as convergence results under the online optimization settings are provided. Empirical results consider the case of an adaptively uniformly distributed multiplicative stochasticity and include not only Stochastic Gradient Descent, but also other popular algorithms equipped with a stochastic learning rate. They demonstrate noticeable optimization performance gains with respect to their deterministic-learning-rate versions.
Exploring the Changes in the Teaching and Research Nexus in Korean Academics Between 1992 and 2018

This study aims to examine how teaching and research have been related to each other in Korean university systems over the past 30 years and how they are similar to and different from those of other major countries, such as Canada, Germany, and Japan. The Carnegie International Survey on the Academic Profession in 1992, the Changing Academic Profession survey in 2008, and the Academic Profession in the Knowledge Society survey in 2018 were used in this study. The main findings reveal that Korean higher education is a hybrid of the Humboldtian and post-Humboldtian patterns. The characteristics of the Humboldtian model are described in dual mission statements and professors’ integrated roles. Most Korean academics viewed positively that teaching and research are compatible. The characteristics of a post-Humboldtian pattern are shown in the differentiation of resources and new types of academic staff, such as research- and teaching-focused professors. In the changing environment surrounding universities, the pattern of linking teaching and research has changed in South Korea.
Historical Alphabet Transliteration Software Using Computer Vision Classification Approach

The article presents the problem of developing mobile software for classification and automatic transliteration of historical alphabets to Latin alphabet using OCR Computer Vision algorithms and is presented on Palmyrene Alphabet. Our suggested solution of semi-automatic transliteration of historical alphabets speeds up and simplifies the process of ancient text analysis and makes reading historical alphabets available to the public. We created a mobile application template for field use and proved the functionality on our own photographic and digitized hand-written datasets of Palmyrene letters, using a MobileNet Artificial Neural Network for character recognition. Such an application helps archaeologists with a faster character transliteration of newly discovered, archived, but untranslated tablets, columns etc., and for checking hand-transliterated texts.
Deep Learning and Super-Hybrid Textual Feature Based Multi-category Thematic Classifier for Punjabi Poetry

For automatic classification tasks, computational linguistic examination of a literary text is challenging. Literary content such as poetry can be categorized based on emotion, theme, poet, hidden message, etc. In this work, we have proposed a theme-based deep Punjabi poetry classifier. The dataset comprises over 2000 poetries divided into 8 subcategories: nature, Festival, Linguistic, Patriotic, Romantic, Relation, Philosophy, and Spiritual. Tokenization, stop word removal, stemming, and particular symbol removal were among the pre-processing sub-phases applied to these poetries. We coined the phrase ‘Bag of Poetry Words’ (BOPW) for nearly 32000 of such extracted unigram tokens. Term frequency (TF) weighting scheme was used for weighing extracted tokens. Four different textual features (lexical, lexical with syntactic, lexical with semantic, and a super hybrid) were tested to develop a classifier based on poetry elements. Adaboost (AB), Bagging (BG), Bi-directional Long Short Term Memory (Bi-LSTM), C4.5, Gradient Boosting (GB), Hyperpipes (HP), K-nearest neighbor (KNN), Long Short Term Memory (LSTM), Naïve Bayes (NB), PART, Random Forest (RF), Support Vector Machine (SVM), Voting Intervals Interval (VFI), and ZeroR algorithms have experimented with different textual features. These 14 machine learning algorithms were divided into baseline learners, ensemble learners, and deep learners. The results revealed that the best performing algorithm was SVM from baseline learners, and the highest accuracy (76.14%) was achieved by incorporating a super hybrid textual feature. The best performing algorithm was GB from ensemble learners, with an accuracy of 64.10%. Bi-LSTM reported the highest accuracy of 80.32% using super hybrid features from deep learners.
Classification Logit Two-Sample Testing by Neural Networks for Differentiating Near Manifold Densities

The recent success of generative adversarial networks and variational learning suggests that training a classification network may work well in addressing the classical two-sample problem, which asks to differentiate two densities given finite samples from each one. Network-based methods have the computational advantage that the algorithm scales to large datasets. This paper considers using the classification logit function, which is provided by a trained classification neural network and evaluated on the testing set split of the two datasets, to compute a two-sample statistic. To analyze the approximation and estimation error of the logit function to differentiate near-manifold densities, we introduce a new result of near-manifold integral approximation by neural networks. We then show that the logit function provably differentiates two sub-exponential densities given that the network is sufficiently parametrized, and for on or near manifold densities, the needed network complexity is reduced to only scale with the intrinsic dimensionality. In experiments, the network logit test demonstrates better performance than previous network-based tests using classification accuracy, and also compares favorably to certain kernel maximum mean discrepancy tests on synthetic datasets and hand-written digit datasets.
When Differential Privacy Implies Syntactic Privacy

Two main privacy models for sanitising datasets are differential privacy (DP) and syntactic privacy. The former restricts individual values&#x2019; impact on the output based on the dataset while the latter restructures the dataset before publication to link any record to multiple sensitive data values. Besides both providing mechanisms to sanitise data, these models are often applied independently of each other and very little is known regarding how they relate. Knowing how privacy models are related can help us develop a deeper understanding of privacy and can inform how a single privacy mechanism can fulfil multiple privacy models. In this paper, we introduce a framework that determines if the privacy mechanisms of one privacy model can also guarantee privacy for another privacy model. We apply our framework to understand the relationship between DP and a form of syntactic privacy called t-closeness. We demonstrate, for the first time, how DP and t-closeness can be interpreted in terms of each other by introducing generalisations and extensions of both models to explain the transition from one model to the other. Finally, we show how applying one mechanism to guarantee multiple privacy models increases data utility compared to applying separate mechanisms for each privacy model.
Methods and Models of Eye-Tracking in Natural Environments

Mobile head-free eye-tracking is one of the most valuable methods we have in vision science for understanding the distribution and dynamics of attention in natural real-world tasks. However, mobile eye-tracking is still a somewhat nascent field, and experimental setups with such devices are not yet fully mature enough for consistently reliable investigation of real-world gaze behavior. Here, we review the development of eye-trackers from their inception to the current state of the art and discuss the experimental methodologies and technologies one can use to investigate natural goal-directed real-world gaze behavior in fully ecological experimental setups. We subsequently expand on the experimental approaches to discuss the modelling approaches used in the field with eye-tracking data, from conventional 2D saliency modelling to more fully embodied gaze approaches that incorporate gaze and motor behavior, which allow us to predict gaze dynamics in fully head-free experimental setups.
Guaranteed Output in $$O(\sqrt{n})$$ Rounds for Round-Robin Sampling Protocols

We introduce a notion of round-robin secure sampling that captures several protocols in the literature, such as the “powers-of-tau” setup protocol for pairing-based polynomial commitments and zk-SNARKs, and certain verifiable mixnets. Due to their round-robin structure, protocols of this class inherently require n sequential broadcast rounds, where n is the number of participants. We describe how to compile them generically into protocols that require only $$O(\sqrt{n})$$ broadcast rounds. Our compiled protocols guarantee output delivery against any dishonest majority. This stands in contrast to prior techniques, which require $$\varOmega (n)$$ sequential broadcasts in most cases (and sometimes many more). Our compiled protocols permit a certain amount of adversarial bias in the output, as all sampling protocols with guaranteed output must, due to Cleve’s impossibility result (STOC’86). We show that in the context of the aforementioned applications, this bias is harmless.
Research on Blockchain Privacy Protection Mechanism in Financial Transaction Services Based on Zero-Knowledge Proof and Federal Learning

In the financial transaction system centering on blockchain technology, institutions at different levels have different powers and roles, so that they have dissimilar private contents to protect. Taking supply chain financing as an example, a multi-level blockchain system is proposed in this paper. The main steps of building the system are as follows: Firstly, commercial banks and regulatory authorities cooperatively establish a risk control model by Federal Learning. Secondly, the private transaction information will be preserved by zero-knowledge proofs for downstream suppliers. Finally, an architecture of multi-level blockchain is designed to supervise the financial trading for guaranteeing credibility. The experimental results show that the system is more beneficial to privacy protection. By incorporating Federal Learning, it can provide stronger security and more reliable risk control. Further, that can also improve the efficiency and performance of the financial transaction system.
Teachers’ Readiness of Information and Communication Technology Integration in Higher Education Innovation

This study aims to identify the elements involved in Information and Communication Technology (ICT) integration in higher education innovation and examine its adoption effects and the support received from the institute and teachers. This study adopted the Activity Theory framework to collect related factors and examined them using stages involved in the diffusion process. The data were collected from teachers who has implemented ICT integrated innovative teaching and from the responses of 250 questionnaires from the universities in Taiwan and analyzed by AMOS. The study had three main findings: (1) Information-based Readiness has positive effect on User Readiness, (2) User Readiness has positive effect on Actual use, and (3) Actual use has positive effect on Satisfaction. The findings could enrich the research of ICT integrated education with different perspectives and would be helpful in extending the long-term development in academics and practical ICT application.
Blockchain for 5G-enabled networks in healthcare service based on several aspects

The pandemic crisis caused by COVID-19 is having a significant impact on our daily work and lives. 5G networks are ready to roll out across the world. This 5G technology helps by interconnecting devices with high quality-of-services (QoS) and efficient system capacity expansion. Aside from this, 5G offers other benefits regarding privacy, decentralization, visibility, and data interoperability. Blockchain technology is used in various industries, including electronic banking, health, and consensus mechanism. Blockchain is a new field based on the idea of a digitally distributed network and smart contract that eliminate all intermediary risks. Extending blockchain technology to the Internet of things (IoT) can provide further benefits. Because millions of IoT devices are connected, and more personal data are shared, one of the most critical challenges with 5G technology is authentication. While using blockchain technology, this data should be more secure. Electronic health records (EHRs) have become a common way to store and manage patient data in hospitals. Sharing these records improves the accuracy and efficiency of the current healthcare database management system. This study was conducted by gathering data and implementing applications regularly. In a blockchain, encryption algorithms know to obtain high-quality confidentiality of individual and organizational data. Healthcare knowledge and analysis are indeed essential to all parties, and privacy should still be retained and developed as technology advances. This proposed method provides a distributed ledger in 5G networks to encrypt information in intelligent healthcare systems.