Reviewerly/exHarmony huggingface dataset:
Prayer Activity Recognition Using Smartphone

There are 1.8 billion Muslims around the world who pray five times a day. Using the Human Activity Recognition Method, we can get a variety of benefits for diagnosing prayer activities, such as automatically switching smartphones to silent mode during prayers. Using smartphones, we can get this help Smartphones currently have many accelerometers, gyroscopes, magnetometers, and other sensors built-in. These sensors can be used to detect the activity of prayers based on artificial intelligence because, during the prayers, people perform certain activities and perform the prayers. In this paper, we have tried to determine the activity of prayers using the sensors of smartphones. Although there are many sensors in the smartphone, we have used three sensors namely accelerometer, gyroscope, and magnetometer respectively. The use of an accelerometer gyroscope as well as a magnetometer sensor has helped in getting the activities of the prayer accurately. We used the SVM algorithm to accurately predict the six prayer activities in this research paper, with a maximum accuracy of 93.92%. We have seen that prayer activities are performed with different body movements Our model can diagnose this very well. We have proven that it is possible to increase this accuracy by using multiple sensors without using only one sensor.
Architecture Design of Blockchain-Based Applications

Integrating blockchain into software solutions is not straightforward as it requires sophisticated architectural design to connect and orchestrate centralized elements, such as backend logic, with decentralized elements, such as blockchain ledgers and smart contracts. We systematically explore this design space and possible architectural solution approaches. More specifically, we provide architectural blue prints for applications with different degrees of decentralization, describe conceptional components as well as possible relations between them. Our research shows that an event-driven architecture incorporating a messaging framework, tethered to dedicated components for handling blockchain state-changing and state-collecting operations, is a prevalent approach for choreographing blockchain-dependent business logic in blockchain-based applications.
Capabilities of neural network technologies for extracting new medical knowledge and enhancing precise decision making for patients

Objectives Currently, there is an active use of neural networks in various areas of human activity. Problems of recognition, diagnostics, optimization, forecasting, control, as well as obtaining new scientific knowledge are successfully solved. However, in medicine, due to the particular complexity of the human body, neural networks are mainly used only for solving the simplest problems of classification and diagnostics. The purpose of this article is to show that the possibilities of neural network modeling in medicine are much wider.Methods This goal is achieved by using special mathematical techniques developed by the authors that allow us to overcome these difficulties.Results The intellectual system created in this way made it possible to reveal a number of interesting scientific knowledge, which sometimes does not coincide with the generally accepted ideas of doctors. Virtual experiments conducted on computer models of patients using our intelligent system clearly demonstrate which lifestyle and medication variations can benefit a particular patient in the long term, and which can cause harm.Conclusion Our intellectual system allows us to identify new scientific knowledge, to carry out long-term forecasting of the development of the disease, to select the optimal courses of treatment and prevention of diseases.
Studying the Anonymity Trilemma with a Discrete-event Mix Network Simulator

In this work, we present a discrete event mix network simulator, that allows analysing how anonymity, latency, and bandwidth overhead are affected by various design choices (i.e., network topology, mixing technique, volume of traffic, packet size or use of cover traffic). To the best of our knowledge, this is the first such simulator as work on it began in 2017 to analyze the Loopix mix network, and the code of our simulator is available under an open-source license. To demonstrate the capabilities of our simulator, we perform an empirical analysis of the impact of core design choices on anonymity, scalability and latency in Elixxir, HOPR and Nym, currently deployed mix network infrastructures that make a variety of different choices in their design.
A Preliminary Approach to Plaque Detection in MRI Brain Images

Demyelinating plaques are areas of perineural demyelination, i.e., areas where myelin has disappeared due to regional ischemia or autoimmune diseases; they are visible as white matter hyperintensities in MR imaging. Regional blood flow reduction provokes a state in which the metabolic needs of the neuron are not met. The aforementioned metabolic imbalance of the neuron is the most frequent causative factor of demyelination. Local thrombosis, small vessel disease, or vessel compression are possible causative factors that provoke regional ischemia. The autoimmune background is also a possible cause as it usually results in numerous foci in brain white matter and the spinal cord. This article presents the experimental results of various algorithms applied for the segmentation of plaques. This issue is briefly discussed from a medical point of view, after which data obtained from magnetic resonance imaging, in particular from the Turbo Inversion Recovery Magnitude (TIRM) protocol, is shown. Then, experimental test results for convolutional filtering and a group of local binarization algorithms are presented. Algorithm sequences are also proposed that achieve an important increase in accuracy. The results are evaluated and the parameter spaces are defined for the best algorithms, including Sauvola, Local Normalization and SDA.
Somewhere Statistically Binding Commitment Schemes with Applications

We define a new primitive that we call a somewhere statistically binding (SSB) commitment scheme, which is a generalization of dual-mode commitments but has similarities with SSB hash functions (Hubacek and Wichs, ITCS 2015) without local opening. In (existing) SSB hash functions, one can compute a hash of a vector v that is statistically binding in one coordinate of v. Meanwhile, in SSB commitment schemes, a commitment of a vector v is statistically binding in some coordinates of v and is statistically hiding in the other coordinates. The set of indices where binding holds is predetermined but known only to the commitment key generator. We show that the primitive can be instantiated by generalizing the succinct Extended Multi-Pedersen commitment scheme (González et al., Asiacrypt 2015). We further introduce the notion of functional SSB commitment schemes and, importantly, use it to get an efficient quasi-adaptive NIZK for arithmetic circuits and efficient oblivious database queries.
The Ontogenesis Model may provide a useful guiding framework, but lacks explanatory power for the nature and development of L2 lexical representation

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Exploring German Multi-Level Text Simplification

We report on experiments in automatic text simplification (ATS) for German with multiple simplification levels along the Common European Framework of Reference for Languages (CEFR), simplifying standard German into levels A1, A2 and B1.For that purpose, we investigate the use of source labels and pretraining on standard German, allowing us to simplify standard language to a specific CEFR level.We show that these approaches are especially effective in low-resource scenarios, where we are able to outperform a standard transformer baseline.Moreover, we introduce copy labels, which we show can help the model make a distinction between sentences that require further modifications and sentences that can be copied as-is.
Review of Recommender System for OTT platform through Artificial Intelligence

OTT (AKA Over The Top) is a platform not delivered through traditional cable or receivers. OTT refers to the delivery of Audio and Video over the internet without the involvement of a multi-system operator. In the control and distribution of the content. Viewers can access it anywhere, anytime and able to save it for future viewing. Recommendations engines include data filtering systems that use different algorithms and data to advise a particular consumer/ viewer of the most relevant elements. The recommender systems for OTT platforms are vital to respond with quality and flawless content to the changing needs of an enormous viewer. Two key strategies proposed for the implementation of a recommendation system are collaborative filtering and content-based filtering. Both methods have their strengths, but in many cases, they cannot be successful. Various hybrid strategies are considered to improve performance. This study discusses various recommendation systems and their relationship with artificial intelligence used by leading OTT platforms and the developed algorithms to help people discover valuable and entertaining information.
The Relationship Between Personality, Recalled Cybersickness Severity, and Recalled Cybersickness Recovery Time

Characteristics of a virtual reality user are known to affect cybersickness, but the specific role of individual differences, such as personality, is largely unknown. This study addressed this gap through examination of subjective recall survey data relating to experiences in virtual reality, including severity of cybersickness symptoms, cybersickness recovery time, and personality. Mediational structural equation modeling on data from 203 participants who used virtual reality at least once per month indicated that extraversion, agreeableness, and conscientiousness were associated with cybersickness severity and that severity was associated with cybersickness recovery time. Further, cybersickness severity fully mediated the relationship between personality and recovery time. These findings highlight the potential relationship between individual differences in personality and suggest further investigation into cybersickness with experimental data and validated measures.
An Impact Assessment of Agile Ceremonies on Sprint Velocity Under Agile Software Development

the agile software development process has a set of standard agile ceremonies, which are must for an agile team to conduct in order to preserve its agility. However, there is always a challenge for agile teams to decide how much agile sprint time should be spent on hosting agile ceremonies and agile product build tasks. This is due to the fact that if most of the agile sprint time is spent on hosting agile ceremonies then the time for agile product build tasks will be reduced significantly impacting the velocity of the agile team. Hence, there is a need to find a solution with the help of which agile team can strike a right balance among various agile ceremonies and agile product build tasks during a sprint. To overcome this gap, this research paper analyses the data for 14 agile sprints to understand the agile time distribution across agile sprint ceremonies and agile product build tasks under current approach. Because of which, this research paper proposes the values for two newly introduced agile co-efficient namely coefficient of agile ceremony time and co-efficient of agile product build time to suggest the ideal time to be spent on agile ceremonies during a sprint. From the research results, it was evident that the velocity of the agile team following the proposed approach reported an increase in velocity by 13.96% for sprints when compared to the velocity of the agile team following the existing approach. The research work also highlighted that the duration chosen for agile ceremony by agile teams is independent of sprint lengths.
Radar signal sorting based on GRU neural network

When the features of the emitter signals are similar, the emitter signals intercepted by the reconnaissance equipment overlap seriously in the space, time, and frequency domains, and the existing technology cannot effectively separate them. This paper considers that when the antenna of the emitter is scanning, the pulse amplitude (PA) sequence intercepted by the reconnaissance receiver has certain regularity. Therefore, a signal sorting method based on the feature of pulse amplitude is proposed. Firstly, the whole pulse sequence is preprocessed, and then a neural network prediction model based on Gated Recurrent Unit (GRU) is established. The GRU neural network is used to learn the changing law of the amplitude sequence. Finally, the learned law is used to predict and sort subsequent pulses. The simulation results show that compared with the listed methods, the proposed method has a better sorting effect.
The Knowledge-Based Economy in Asia and the Pacific: Links to the Sustainable Development Goals

The success of the Asia and the Pacific Rim countries in the world economy is based on their capacity to implement the Knowledge-Based Economy (KBE9). The UN Sustainable Development Goals (SDGs) gave us a platform to create a universal call to action to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. It provided us with more guidance and metrics to measure our progress. Some of the SDGs are directly related with the main components of the KBE. The objective of this chapter is to explore how the SDGs are related to the main components of the KBE. The study is structured as following: firstly, we present a characterization of the knowledge-based economy (KBE) and its evolution in Asia and the Pacific. Secondly, we analyze the links of the sustainable development goals (SDGs) with the most important components of the KBE. Therefore, we analyze SDG 17 and SDG 9 with relation to Science, Technology and SDG 4 regarding Education. Finally, we present some brief conclusions.
Review of Cybersecurity in LatinAmerica during the Covid-19 Pandemic. A brief Overview

The arrival of the covid-19 pandemic has generated a turning point in the way people live. A maj or role for telecommunications technology has been playing in enabling interaction between people and the productivity of people within an organization, despite restrictions put in place by several governments because of the pandemic. The massive use of ICT also exposes users and organizations to be victims of a possible cyber-attack. This paper aims to review the impact of the covid-19 pandemic on cybersecurity in Latin America, which have been the most recurrent attacks and some reported cases. For this purpose, we examined a literature review on the different types of cyberattacks, reports provided by multiple organizations in the field of cybersecurity, and news from Latin American countries about cybercrimes related to the covid-19 pandemic. It also discusses a trend in cyberattacks and some recommendations to improve cybersecurity for individuals and organizations. It concludes by defending the need to have a culture of cybersecurity to minimize the impact of cyber-attacks at both the organizational and individual levels.
Universal Digital Competencies of the Student as a New Concept Of Professional Pedagogy

The article is devoted to the definition of a new concept in professional pedagogy – “end-to-end digital competencies”, as well as the possibility of their formation in higher school in the process of mastering additional professional educational programs by students. The authors state the principle of their formation through e-learning, taking into account the principles of individualization of training. The purpose of the article is to give a theoretical justification and definition of the concept of “end-to-end digital technologies” as a new concept in professional pedagogy and an urgent task of higher education. Methodology. The article presents an analysis of the scientific literature on the issue of cross-cutting competencies, including digital ones, on the basis of which the authors define the concept of “cross-cutting digital competencies” as a new concept in professional pedagogy and one of the urgent tasks of higher education. In the conclusion of the article, the authors conclude that it is advisable to carry out the formation of digital competencies among higher school students according to a cross-cutting, penetrating principle, in the process of parallel development of the main educational programs of higher education and additional professional programs (advanced training, professional retraining).
VBLSH: Volume-balancing locality-sensitive hashing algorithm for K-nearest neighbors search

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets.
Grouping-Based Optimization Method for Multirobot System Pattern Formation

This article presents a novel optimization method for multirobot formation in an obstacle environment. For this challenge, we proposed an iterative optimization approach in previous work, in which all robots as a whole obtain the optimal goal pattern, and then, move to the goal without collision to form the pattern. However, this approach results in high consumption in terms of time and the path that robots travel as the number of robots increases. To improve efficiency, we propose a grouping-based optimization method. First, a specific grouping strategy (the number of groups and the number of data points in each group are fixed) is utilized to group the multiple robots. And then in an obstacle environment, each group of robots completes its optimal pattern formation in parallel without collision through coordination within and between groups. The simulation results of the multiletter pattern formation validate the effectiveness of the method proposed in this article compared to the method without grouping.
Integrating Transformers and Knowledge Graphs for Twitter Stance Detection

Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis.Recent works have explored knowledge infusion -supplementing the linguistic competence and latent knowledge of large pre-trained language models with structured knowledge graphs (KGs), yet few works have applied such methods to the SD task.In this work, we first perform stance-relevant knowledge probing on Transformers-based pre-trained models in a zero-shot setting, showing these models' latent real-world knowledge about SD targets and their sensitivity to context.We then propose novel knowledge-enriched stance detection models.We evaluate them on two Twitter stance datasets, achieving state-of-the-art performance on both.
Research on Thangka Image Retrieval Algorithm Based on Mean Hashing

Thangka images are rich in content and complex in texture information, so it is difficult to fully extract effective features and achieve good retrieval effect. This paper proposes that the extracted convolutional layer features are hashed with mean value encoding for Thangka image retrieval. First, VGG16 model is applied to extract the features of the last convolutional layer of the image for feature preservation, then the extracted features are mapped by mean hash algorithm, and finally, hamming distance is used for image similarity calculation. Experiments show that Thangka image retrieval based on mean hashing of image features is significantly better in terms of precision, recall and average retrieval accuracy than that based on image features of Thangka convolutional layer.
Deep Learning Based Channel Covariance Matrix Estimation with Scene Images

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from the environmental images and user speed, where the environmental images can reflect the user location information. Simulation results show that the proposed method is effective and will benefit the subsequent channel estimation.
Alias-Free Generative Adversarial Networks

We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. This manifests itself as, e.g., detail appearing to be glued to image coordinates instead of the surfaces of depicted objects. We trace the root cause to careless signal processing that causes aliasing in the generator network. Interpreting all signals in the network as continuous, we derive generally applicable, small architectural changes that guarantee that unwanted information cannot leak into the hierarchical synthesis process. The resulting networks match the FID of StyleGAN2 but differ dramatically in their internal representations, and they are fully equivariant to translation and rotation even at subpixel scales. Our results pave the way for generative models better suited for video and animation.
An Improved U-Net Architecture for Image Dehazing

In this paper, we present a simple yet powerful deep neural network for natural image dehazing. The proposed method is designed based on U-Net architecture and we made some design changes to make it better. We first use Group Normalization to replace Batch Normalization to solve the problem of insufficient batch size due to hardware limitations. Second, we introduce FReLU activation into the U-Net block, which can achieve capturing complicated visual layouts with regular convolutions. Experimental results on public benchmarks demonstrate the effectiveness of the modified components. On the SOTS Indoor and Outdoor datasets, it obtains PSNR of 32.23 and 31.64 respectively, which are comparable performances with state-of-the-art methods. The code is publicly available online soon.