Reviewerly/exHarmony huggingface dataset:
Using deep learning models for learning semantic text similarity of Arabic questions

&lt;span&gt;Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three &lt;span&gt;models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined&lt;/span&gt; features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.&lt;span&gt;99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest&lt;/span&gt; result of F1=86.086%.&lt;/span&gt;
Speedup vs. quality: Asynchronous and cluster-based distributed adaptive genetic algorithms for ordered problems

While the main motivation for Parallel Genetic Algorithms (PGAs) has been to improve the scalability of Genetic Algorithms (GAs), techniques and strategies for maintaining population diversity is an equally active research topic. Island Model Genetic Algorithms (IMGAs) represent one of the most mature strategies for developing PGAs in an effective and scalable manner. However, identifying how much migration and which individuals should migrate are open research problems. Meanwhile, recent developments in Adaptive Genetic Algorithms (AGAs) have led to techniques for monitoring and maintaining population diversity in an online manner. The aim of the present work is to introduce adaptive techniques and mechanisms into PGAs in order to determine when, how much and which individuals are most suitable for migration. We present a number of adaptive PGAs that aim to maintain diversity and maximise coverage of the solution space by minimising the overlap between islands. PGAs presented in this work are empirically assessed for their abilities in scalability, ability to find good quality solutions and maintain population diversity in ordered problems. These metrics are compared to existing adaptive and parallel GAs selected from the literature for their performance. We estimated the overhead costs of monitoring diversity and communication would result in a trade off between scalability and search capabilities. Our results suggest that an asynchronous adaptive PGA has the greatest speedup potential. However, while localising adaptive populations by k-means clustering is less scalable, results indicate that the method is more effective at directing the search in order to reduce the likelihood of islands searching in the same areas of the solution space. For this reason, an adaptive PGA with clustering-based migration demonstrates greater potential in solution quality while maintaining good speedup performance.
Generic aspects of the resource theory of quantum coherence

The class of incoherent operations induces a preorder on the set of quantum pure states, defined by the possibility of converting one state into the other by transformations within the class. We prove that if two $n$-dimensional pure states are chosen independently according to the natural uniform distribution, then the probability that they are comparable vanishes as $n\ensuremath{\rightarrow}\ensuremath{\infty}$. We also study the maximal success probability of incoherent conversions and find an explicit formula for its large-$n$ asymptotic distribution. Our analysis is based on the observation that the extreme values (largest and smallest components) of a random point uniformly sampled from the unit simplex are distributed asymptotically as certain explicit homogeneous Markov chains.
From Digital Literacy to Digital Intelligence

Digital literacy is an essential requirement for every individual to survive in the digital society.Initiatives have been done to develop a comprehensive digital literacy framework in many countries.These initiatives use different names for the framework including digital literacy, digital competence, digital readiness, and digital intelligence.The paper argues that the use of different names and the inclusion of new elements in the development of digital literacy framework show a dynamic and progressive efforts in the development of more comprehensive and adaptive frameworks to face the challenge of industrial revolution 4.0.Digital literacy movement in Indonesia has been initiated in 2017 by the ministry of communication and information in cooperation with many elements of civil society.This initiative is a good start and should be followed by more systematic efforts to develop a comprehensive and measurable digital literacy framework to improve the quality of digital literacy in Indonesia.This comparative study is part of a bigger study related to the development of a systematic and comprehensive digital literacy framework, which can be used as a guidance for the future development of digital literacy movement in Indonesia.
Stability and Generalization of Decentralized Stochastic Gradient Descent

The stability and generalization of stochastic gradient-based methods provide valuable insights into understanding the algorithmic performance of machine learning models. As the main workhorse for deep learning, the stochastic gradient descent has received a considerable amount of studies. Nevertheless, the community paid little attention to its decentralized variants. In this paper, we provide a novel formulation of the decentralized stochastic gradient descent. Leveraging this formulation together with (non)convex optimization theory, we establish the first stability and generalization guarantees for the decentralized stochastic gradient descent. Our theoretical results are built on top of a few common and mild assumptions and reveal that the decentralization deteriorates the stability of SGD for the first time. We verify our theoretical findings by using a variety of decentralized settings and benchmark machine learning models.
Decision Support System for Cloud Computing Service Selection Using the Weighted Product Method (Case Study: PT. Deptech Digital Indonesia)

The selection of cloud computing services requires careful consideration and review. Some aspects of the criteria that must be considered such as direct selection between services that take a long time and need to be done repeatedly. Decision Support System with Weighted Product (WP) method is an effective method because the time needed for calculation is much shorter. The purpose of this research is to apply Weighted Product (WP) method in the decision support system to choose cloud computing services where as a case study is PT Deptech Digital Indonesia, so that it can make it easier for companies to make decisions according to their needs. The calculation results using the WP method give preference to the top 3 services: Google Cloud, Amazon Web Services and Microsoft Azure. This proves that research with the WP method can be applied to various services that will be used in the future according to predetermined criteria. Based on these results, the system can recommend cloud computing services according to the needs and a good level of accuracy.
Investigating of Mobile Learning Technology Acceptance in Companies

Technology plays a prominent role enhancing and innovating the quality of work nowadays.Currently, the world is witnessing a new wave of industrial revolution driven by the development and diffusion of digital technology; for instance, the Internet of Things (IoT) is being used, adopted, and incorporated into people's everyday lives.Mobile learning, as one of the modern innovations, affects employee learning in the corporate world.Tools like CourseMill® have critical significance due to its various benefits, such as enhance personal and convenience learning as information sharing, promote cooperation and collaboration in learning increasing management, bridge digital divide, and enhance job performance.However, numerous limitations of adopting mobile technologies were identified, such as high cost of implementation, unreliable broadband wireless connection, and shortcomings in mobile devices features like the small screen size.The study utilized both the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology model (UTAUT) to develop a research model to be used in the study.The multivariate exploratory factor analysis identified four main barriers that affect M-learning adoption, which is perceived usefulness (PU), perceived ease of use (PEU), social influences (SI), and facilitating conditions (FC).These factors have a significant positive influence on the acceptance and actual use of M-learning technology in companies.
Microscopic Tumour Classification by Digital Mammography

In this paper, we investigate the classification of microscopic tumours using full digital mammography images. Firstly, to address the shortcomings of traditional image segmentation methods, two different deep learning methods are designed to achieve the segmentation of uterine fibroids. The deep lab model is used to optimize the lesion edge detailed information by using the void convolution algorithm and fully connected CRF, and the two semantic segmentation networks are compared to obtain the best results. The Mask RCNN case segmentation model is used to effectively extract features through the ResNet structure, combined with the RPN network to achieve effective use and fusion of features, and continuously optimize the network training to achieve a fine segmentation of the lesion area, and demonstrate the accuracy and feasibility of the two models in medical image segmentation. Histopathology was used to obtain ER, PR, HER scores, and Ki-67 percentage values for all patients. The Kaplan-Meier method was used for survival estimation, the Log-rank test was used for single-factor analysis, and Cox proportional risk regression was used for multifactor analysis. The prognostic value of each factor was calculated, as well as the factors affecting progression-free survival. This study was done to compare the imaging characteristics and diagnostic value of mammography and colour Doppler ultrasonography in nonspecific mastitis, improve the understanding of the imaging characteristics of nonspecific mastitis in these two examinations, improve the accuracy of the diagnosis of this type of disease, improve the ability of distinguishing it from breast cancer, and reduce the rate of misdiagnosis.
Requirements Identification for Real-Time Anomaly Detection in Industrie 4.0 Machine Groups: A Structured Literature Review

Industrie4.0 environments generate an unprecedented amount of production data.This is due to the rising number of sensors and interconnected devices capable of emitting data in millisecond frequencies.Streaming analytics offers promising methodologies that can support handling and analysis of data volume and variety.Transparency and control over real-time data can increase production efficiency in tightly connected machine environments.Data transparency may avoid time-consuming assessment of machines to detect anomalous machine behavior causing production inefficiencies or failures.This paper aims to identify requirements to implement streaming analytics for the detection of anomalies in Industrie 4.0 production machine groups through a structured literature review.
Anleitung zur Erstellung von Annotationsrichtlinien

This chapter gives a brief practical introduction into the development of annotation guidelines, for the scenario that new guidelines are created for a phenomenon or concept that has been described theoretically.In a single sentence, the goal of annotation guidelines can be formulated as: Given a theoretically described phenomenon or concept, describe it as generic as possible but as precise as necessary so that human annotators can annotate the concept or phenomenon in any text without running into problems or ambiguity issues.
Less is More - diveXplore 5.0 at VBS 2021

As a longstanding participating system in the annual Video Browser Showdown (VBS2017-VBS2020) as well as in two iterations of the more recently established Lifelog Search Challenge (LSC2018-LSC2019), diveXplore is developed as a feature-rich Deep Interactive Video Exploration system. After its initial successful employment as a competitive tool at the challenges, its performance, however, declined as new features were introduced increasing its overall complexity. We mainly attribute this to the fact that many additions to the system needed to revolve around the system’s core element – an interactive self-organizing browseable featuremap, which, as an integral component did not accommodate the addition of new features well. Therefore, counteracting said performance decline, the VBS 2021 version constitutes a completely rebuilt version 5.0, implemented from scratch with the aim of greatly reducing the system’s complexity as well as keeping proven useful features in a modular manner.
REAL-TIME COMPUTATIONAL FLUID DYNAMICS FLOW RESPONSE VISUALISATION AND INTERACTION APPLICATION BASED ON AUGMENTED REALITY

The behaviour of fluid flow is a complex paradigm for cognitive interpretation and visualisation. Engineers need to visualise the behaviour mechanics of flow field response in order to enhance the cognitive ability in problem solving. Therefore, mixed reality related technology is the solution for enhanced virtual interactive learning environment. However, there are limited augmented reality platforms on fluid flow interactive learning. Therefore, an interactive education application is proposed for students and engineers to interact and understand the complex flow behaviour pattern subjected to elementary geometry body relative to external flow. This paper presented the technical development of a real-time flow response visualisation augmented reality application for computational fluid dynamics application. It was developed with the assistance of several applications such as Unity, Vuforia, and Android. Particle system modules available in the Unity engine were used to create a two-dimensional flow stream domain. The flow visualisation and interaction were limited to two-dimensional and the numerical fluid continuum response was not analysed. The physical flow response pattern of three simple geometry bodies was validated against ANSYS simulated results based on visual empirical observation. The particle size and number of particles emitted were adjusted in order to emulate the physical representation of fluid flow. Colour contour was set to change according to fluid velocity. Visual validation indicated trivial dissimilarities between FLUENT generated results and flow response exhibited by the proposed augmented reality application.
Evaluating the Global Forecast System (GFS) for energy management over Minas Gerais State (Brazil) against in-situ observations

Several regions of Brazil have experienced periods of intense drought in the last decades. Hydropower plants produce most of the country’s energy and a reduction in reservoir flow can compromise the energy sector. Therefore, the Brazilian government has sought the diversification of energy production with other renewable sources. The introduction of new renewable sources, such as wind and solar, requires detailed studies of the local weather conditions usually through historical data analysis. However, several areas in Brazil lack weather stations. In this context, this study aims to assess the ability of the Global Forecast System (GFS) reanalysis product to represent wind, in the state of Minas Gerais (MG) which has 79.5% of energy production associated with water resources. Although the study considers a specific region, it presents a methodology that can be replicated in regions where data is not available. Over most areas, 10 m wind speed values of the GFS reanalysis were similar to those registered by weather stations. Results at 10 and 100 m of altitude show high wind speed values in the north of the state, a region where the highest power densities are also recorded (approximately 150 W m–2 during winter and spring). In conclusion, the GFS reanalysis product, albeit with the biases reported here, can be used in regions with scarce meteorological data to estimate the potential for wind energy production as a complementary source of hydroelectricity.
EEC: Learning to Encode and Regenerate Images for Continual Learning

The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. Reconstructed images from encoded episodes are replayed when training the classifier model on a new task to avoid catastrophic forgetting. The loss function for the reconstructed images is weighted to reduce its effect during classifier training to cope with image degradation. When the system runs out of memory the encoded episodes are converted into centroids and covariance matrices, which are used to generate pseudo-images during classifier training, keeping classifier performance stable with less memory. Our approach increases classification accuracy by 13-17% over state-of-the-art methods on benchmark datasets, while requiring 78% less storage space.
Digitalization and formation of digital culture: social and educational aspects

The digital society is a modern stage of civilization characterized by the dominant role of education and in-formation in all spheres of life, which has a decisive impact on the living conditions of people, their education and work, as well as on the relationship between the state, business and society through information and communication technologies. The process of forming a new type of culture should be based on the moderni-zation of the way of organizing the educational process in a modern university. It is proposed to define the digital culture of a future specialist as the values of digitalization that do not contradict generally accepted humanistic values, the presence of digital competence, possession of technologies for optimal orientation in digital reality and effective communication in the information space. The article analyses the current state of digitalization in all spheres of human life, considers characteristics and features of a shift to digital culture, represents the main levels of considering the concept of «digital culture», specifies the concept of «digital culture of the future specialist», and sets out the main vectors in the process of developing the investigated type of culture.
A new class of optimal wide-gap one-coincidence frequency-hopping sequence sets

In this paper, we propose a new class of optimal one-coincidence FHS (OC-FHS) sets with respect to the Peng-Fan bounds, including prime sequence sets and HMC sequence sets as special cases. Thereafter, through investigating their properties, we determine all of the FHS distances in the OC-FHS set. Finally, for a given positive integer, we also propose a new class of wide-gap one-coincidence FHS (WG-OC-FHS) sets where the FHS gap is larger than the given positive integer. Moreover, such a WG-OC-FHS set is optimal with respect to the WG-Lempel-Greenberger bound and the WG-Peng-Fan bounds simultaneously.
Identifying Expertise Gaps in Cyber Incident Response: Cyber Defender Needs vs. Technological Development

Incident response is an area within cyber defense that is responsible for detecting, mitigating, and preventing threats within a given network.Like other areas of cyber security, incident response is experiencing a shortage of qualified workers which has led to technological development aimed at alleviating labor-related pressures on organizations.A cognitive task analysis was conducted with incident response experts to capture expertise requirements and used an existing construct to help prioritize development of new technology.Findings indicated that current software development incorporates factors such as analyst efficiency and consistency.Gaps were identified regarding communication and team navigation that are inherent to dynamic team environments.This research identified which expertise areas are needed at lower-tier levels of incident response and which of those areas current automation platforms are addressing.These gaps help focus future studies by bridging expertise research to development efforts.
Partial Boolean Functions With Exact Quantum Query Complexity One

We provide two sufficient and necessary conditions to characterize any n-bit partial Boolean function with exact quantum query complexity 1. Using the first characterization, we present all n-bit partial Boolean functions that depend on n bits and can be computed exactly by a 1-query quantum algorithm. Due to the second characterization, we construct a function F that maps any n-bit partial Boolean function to some integer, and if an n-bit partial Boolean function f depends on k bits and can be computed exactly by a 1-query quantum algorithm, then F(f) is non-positive. In addition, we show that the number of all n-bit partial Boolean functions that depend on k bits and can be computed exactly by a 1-query quantum algorithm is not bigger than an upper bound depending on n and k. Most importantly, the upper bound is far less than the number of all n-bit partial Boolean functions for all efficiently big n.
Centroid Based Concept Learning for RGB-D Indoor Scene Classification.

This paper contributes a novel cognitively-inspired method for RGB-D indoor scene classification. High intra-class variance and low inter-class variance make indoor scene classification an extremely challenging task. To cope with this problem, we propose a clustering approach inspired by the concept learning model of the hippocampus and the neocortex, to generate clusters and centroids for different scene categories. Test images depicting different scenes are classified by using their distance to the closest centroids (concepts). Modeling of RGB-D scenes as centroids not only leads to state-of-the-art classification performance on benchmark datasets (SUN RGB-D and NYU Depth V2), but also offers a method for inspecting and interpreting the space of centroids. Inspection of the centroids generated by our approach on RGB-D datasets leads us to propose a method for merging conceptually similar categories, resulting in improved accuracy for all approaches.