Miracle-dz/UniFAR-FTUs huggingface dataset:
A Modeling Approach for Blockchain-inspired Business Models: An Extension of the e3 -Value Method

Blockchain enables new ways of organizing economic activities. Blockchain promises to reduce costs and time associated with intermediaries and strengthen trust in an ecosystem of actors. The impact of this promising technology is reﬂected in an emerging stream of research and various companies exploring the potential uses of blockchain technology and building new blockchain-inspired business models. While there are promising use cases of this new technology, research and practice are still in their early stages in terms of changing existing and creating new business models. We propose an extension of the e 3 -value method to capture and represent the characteristics of blockchain-inspired business models. The approach is particularly useful for the value-based analysis of blockchain-inspired business models and the fundamental economic evaluation of these. It provides the foundation for capturing, communicating, innovating and experimenting with blockchain-inspired business models. We demonstrate the practicality of the extended e 3 -value method by evaluating and applying it to an existing blockchain business model.
Revisiting the Structured Variational Autoencoder

The Structured Variational Autoencoder (SVAE) was introduced ﬁve years ago [Johnson et al., 2016]. It presented a modeling idea—to use probabilsitic graphical models (PGMs) as priors on latent variables and deep neural networks (DNNs) to map them to observed data—as well as an inference idea—to have the recognition network output conjugate potentials to the PGM prior rather than a full posterior. While mathematically appealing, the SVAE proved impractical to use or extend, as learning required implicit differentiation of a PGM inference algorithm, and the original authors’ implementation was in pure Python with no GPU or TPU support. Now, armed with the power of JAX [Bradbury et al., 2018], a software library for automatic differentiation and compilation to CPU, GPU, or TPU targets, we revisit the SVAE. We develop a modular implementation that is orders of magnitude faster than the original code and show examples in a variety of different settings, including a scientiﬁc application to animal behavior modeling. Furthermore, we extend the original model by incorporating interior potentials, which allows for more expressive PGM priors, such as the Recurrent Switching Linear Dynamical System (rSLDS). Our JAX implementation of the SVAE and its extensions open up avenues for many practical applications, extensions, and theoretical investigations.
Evidence for Plasma Heating at Thin Current Sheets in the Solar Wind

Plasma heating at thin current sheets in the solar wind is examined using magnetic field and plasma data obtained by the WIND spacecraft in the past 17 years from 2004 to 2019. In this study, a thin current sheet is defined by an abrupt rotation (larger than 45°) of the magnetic field direction in 3 s. A total of 57,814 current sheets have been identified, among which 25,018 current sheets are located in the slow wind and 19,842 current sheets are located in the fast wind. Significant plasma heating is found at current sheets in both slow and fast wind. Proton temperature increases more significantly at current sheets in the fast wind than in the slow wind, while the enhancement in electron temperature is less remarkable at current sheets in the fast wind. The results reveal that plasma heating commonly exists at thin current sheets in the solar wind regardless of the wind speed, but the underlying heating mechanisms might be different.
Dropout and Ensemble Networks for Thermospheric Density Uncertainty Estimation

Accurately estimating spacecraft location is of crucial importance for a variety of safety-critical tasks in low-Earth orbit (LEO), including satellite collision avoidance and re-entry. The solar activity largely impacts the physical characteristics of the thermosphere, consequently affecting the trajectories of spacecraft in LEO. Stateof-the-art models for estimating thermospheric density are either computationally expensive or under-perform during extreme solar activity. Moreover, these models provide single-point solutions, neglecting critical information on the associated uncertainty. In this work we use and compare two methods, Monte Carlo dropout and deep ensembles, to estimate thermospheric total mass density and associated uncertainty. The networks are trained using ground-truth density data from five wellcalibrated satellites, using orbital data information, solar and geomagnetic indices as input. The trained models improve for a subset of satellites upon operational solutions, also providing a measure of uncertainty in the density estimation.
Methods for Speeding up Recommender System Computations Using a Graph Database

—Recommender systems are commonly used for Internet-based activities to assist users in making decisions on what items to select. One very common use of recommender systems is in electronic commerce purchases. The need for recommender systems in electronic commerce is due to the vast amounts of items to choose from. Due to this vast amount of items, generation of recommendations for recommender systems is a computationally intensive activity. This paper reports on studies that were conducted to investigate methods for speeding up the computations for generating recommendations when the data that is used to generate recommendations is stored in a graph database. The proposed methods involve the pre-computation and storage of values that are used in the generation of recommendations. This leads to a speed-up of the computations for generating recommendations.
Mixture-of-experts VAEs can disregard variation in surjective multimodal data

Machine learning systems are often deployed in domains that entail data from multiple modalities, for example, phenotypic and genotypic characteristics describe patients in healthcare. Previous works have developed multimodal variational autoencoders (VAEs) that generate several modalities. We consider surjective data, where single datapoints from one modality (such as class labels) describe multiple datapoints from another modality (such as images). We theoretically and empirically demonstrate that multimodal VAEs with a mixture of experts posterior can struggle to capture variability in such surjective data.
An overview over Capsule Networks

Hinton et. al recently published the paper“Dynamic Routing Between Capsules” [20], proposing a novel neural network architecture. This Capsule Network (CapsNet) outperforms state-of-the-art Convolutional Neural Networks on simple challenges like MNIST [13], MultiMNIST [20] or smallNORB [6]. In this paper, we describe multiple aspects of the current research in Capsule Networks. This includes explaining the shortcomings of CNNs, the idea and architecture of Cap-sule Networks and the evaluation on multiple challenges. Furthermore, we give an overview of current research, improvements and real world applications, as well as advantages and disadvantages of the CapsNet.
Effect of Sowing Dates on Growth, Development and Pod Production of Snap Been (Phaseouls Vulgaris L.) Varieties during Summer Season

Two field experiments were carried out in the experimental farm of the faculty of Agriculture, Moshtohor, Benha University during two successive summer season; to study the effect of sowing dates March 1 referred as early sowing date, April 1 as medium sowing date and May 1 as late sowing date. Four cultivars were tested in the experiment namely Paulista, Samantha, Narina and Bronco. The data for vegetative growth and chemical composition was taken at 50 days from sowing. Flower behavior as well as quantity and quality of green pods were determined. 1All vegetative aspects i.e plant length, number of leaves and branches per plant as well as fresh weight of shoot were significantly increased with early sowing on the 1 of March. Cv. Bronco reflected the highest value of these vegetative criteria. On the other hand, the dry matter percentage of the shoots was increased with delaying seed sowing from March 1 to May 1. 2Early sowing (1 March) led to significant increases in all assayed photosynthetic pigments (chl. (a), (b) and carotenoids) as well as mineral constituent (N, P, K). Cv. Paulisa followed by cv. Bronco reflected the highest values. Early sowing (1 march) exhibited the maximum increase in reducing, non-reducing and total sugars as well as nucleic acids (RNA& DNA). Cv. Paulista reflected the highest level for all these parameters. 3Flowering behavior showed that early sowing date (1 March) exhibited the highest values of both number of flowers, pods per plant and fruit set percentage. Plants of cv. Bronco Possessed the superiorly in this respect followed by cv. Paulista. 4Sowing snap bean seed in early planting (1 March) significantly increased green pod yield per plant as well as feddan followed by cv. Bronco, cv. Paulista then cv. Narina. Meanwhile cv. Samantha produced the lowest yield. Worth to be mentioning that cv. Paulista improved pod quality in all measured physical and chemical parameters of pods. It can be concluded that cv. Bronco can be recommended on the early sowing date (1 March) to obtain high green pod yield. However, for better quality of pods cv. Paulista at early sowing (1 March) could be recommended.
End-to-End Learning on Multimodal Knowledge Graphs

Knowledge graphs enable data scientists to learn end-to-end on heterogeneous knowledge. However, most end-to-end models solely learn from the relational information encoded in graphs' structure: raw values, encoded as literal nodes, are either omitted completely or treated as regular nodes without consideration for their values. In either case we lose potentially relevant information which could have otherwise been exploited by our learning methods. We propose a multimodal message passing network which not only learns end-to-end from the structure of graphs, but also from their possibly divers set of multimodal node features. Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities, including numbers, texts, dates, images and geometries, which are projected into a joint representation space together with their relational information. We implement and demonstrate our model on node classification and link prediction for artificial and real-worlds datasets, and evaluate the effect that each modality has on the overall performance in an inverse ablation study. Our results indicate that end-to-end multimodal learning from any arbitrary knowledge graph is indeed possible, and that including multimodal information can significantly affect performance, but that much depends on the characteristics of the data.
RNA N 6-methyladenosine modulates endothelial atherogenic 1 responses to disturbed flow in mice 2

responses to disturbed flow in mice 2 Bochuan Li, Ting Zhang, Mengxia Liu, Zhen Cui, Yanhong Zhang, Mingming Liu, 3 Yanan Liu, Yongqiao Sun, Mengqi Li, Yikui Tian, Ying Yang, Hongfeng Jiang, 4 Degang Liang 5 Tianjin Key Laboratory of Metabolic Diseases; Key Laboratory of Immune 6 Microenvironment and Disease (Ministry of Education); Collaborative Innovation Center of 7 Tianjin for Medical Epigenetics and Department of Physiology and Pathophysiology, Tianjin 8 Medical University, Tianjin, 300070, China. 9
The Optimization of the Dynamic K-Means Clustering Algorithm with the Cluster Initialization in Grouping Travelers Perception to the Beach Tourist Destinations in Bali, Indonesia

Segmentation is the first process in determining positioning. Segmentation of tourist destinations is done by taking into account the perception of tourists to the attribute of tourist destinations that include the conditions of nature and outdoor activities, music and nightlife, arts and culture, food and drinks, social conditions, cost of living, education, health, property, and environmental stewardship life. Segmentation uses clustering methods by applying dynamic clustering algorithm on the k-means. The use of the dynamic k-means clustering algorithms could work more optimally combined with the initialization of the cluster using the mean algorithm based on k-means on the initial stage. It can be seen from the variance, SSE, DBI obtain the smallest value and the closest PC to 1 as compared with the traditional k-means algorithm and dynamic k-means clustering algorithm. The resulting cluster of k-means clustering algorithm dynamically with the initialization of cluster consists of 4 pieces. Cluster 1 represents a cluster with the lowest perceived quality; otherwise the cluster 4 is a cluster with the highest perceived quality. Meanwhile the third cluster is a cluster with the highest perception.
Complex Path Queries for RDF Graphs ⁄

RDF entailment [5], and by extension RDF queries, can be computed using a kind of graph homomorphism known as conceptual graphs projection [3]. Another approach, that has been successfully used in graph databases [6], is to use regular expressions to find paths in a graph (i.e., given a directed labeled graph G and a regular expression E, find all pairs of nodes connected by a path such that the concatenation of the labels along the path belongs to the language generated by E, denoted by L(E)). However, some queries that can be expressed in one approach cannot be expressed in the other. A query whose homomorphic image in the database is not a path cannot be expressed by a regular expression, while RDF semantics is not meant to express paths of unknown length.
Algorithms and Techniques for Computation Offloading in Edge E nabled Cloud of Things (ECoT)–A Primer

Recent Cloud of Things (CoT) applications entail continual transmission of data from the things to the Cloud, which is unable to provide desirable QoS for delay sensitive IoT applications. The uncertain variations in communication delay from the Things to the Cloud become a high-risk factor for these applications. To provide satisfactory computation performance as well as to achieve energy efficient resource allocation/optimal workload allocation in mixed edge-cloud environment for IoT, offloading is an attractive technology that offloads some parts of applications to edge or cloud server to save time and energy for IoT with constrained resources. This paper presents the advanced techniques, strategies, schemes and algorithms used for computational offloading in Edge enabled Cloud of Things named as ECoT. The paper also introduces three typical edge computing technologies, termed as fog computing, cloudlets and Mobile Edge Computing (MEC). After comparing their architecture, principles, applications and standardization efforts, fog is selected as a promising middleware technology for CoT for further algorithm analysis. Furthermore, open research challenges, related issues and future directions of offloading in ECoT paradigm is discussed in detail.
Image Registration and Motion Correction

Registration is a widely used and essential tool in imaging o f all forms, and especially in medical imaging. A great deal of effort and research has gone into developing registr ation methods to align images of various types. This talk aims to introduce the basic concepts behind registrati on, particularly the fully automatic registration methods , as applied in medical imaging. Because of the great variety o f approaches used, it is necessary to only concentrate on a few here, but many surveys, reviews and textbooks coveri ng both broad [14, 15, 20, 23, 28, 37, 41] and more specialised [2, 9, 10, 16, 24, 25, 26, 31] topics in medical im ge registration exist for the interested reader.
MACHINE LEARNING APPLICATION BENCHMARK FOR IN-ORBIT ON-BOARD DATA PROCESSING

This paper introduces the MLAB project, a research and development activity funded by ESA General Support Technology Programme under the lead of Airbus Defence and Space GmbH with the goal of developing a machine learning application benchmark for space applications. First, the project is introduced, and examples of applications are described including their design challenges. The benchmark speciﬁcations is described, and different processing platforms to be submitted are presented. Finally, initial performance results for ﬁrst submissions are shown as examples.
Competitive Session Government support measures and innovation capabilities: The case of developing economy SMEs

This paper investigates the impact of government support on small and medium enterprises in developing economies. Conceptually, we show how government initiatives are more likely to be successful if enhanced by the combination of the dynamic and operational capabilities that are useful for innovation. Data from a panel of 4,008 Vietnamese businesses demonstrate that technological and R support measures by government were negatively related to product innovation, but were positive for process innovation. The data also highlight the role of the cost of access, employee turnover and technology orientation, which reduce the impact of HR support and technological assistance for product-related innovation. Hypotheses based on the development of dynamic and operational capabilities examine the creation and interaction of innovation capabilities.
Optimizing Temporal Resolution Of Convolutional Recurrent Neural Networks For Sound Event Detection

In this technical report, the systems we submitted for subtask 4 of the DCASE 2021 challenge, regarding sound event detection, are described in detail. These models are closely related to the baseline provided for this problem, as they are essentially convolutional recurrent neural networks trained in a mean teacher setting to deal with the heterogeneous annotation of the supplied data. However, the time resolution of the predictions was adapted to deal with the fact that these systems are evaluated using two intersection-based metrics involving different needs in terms of temporal localization. This was done by optimizing the pooling operations. For the first of the defined evaluation scenarios, imposing relatively strict requirements on the temporal localization accuracy, our best model achieved a PSDS score of 0.3609 on the validation data. This is only marginally better than the performance obtained by the baseline system (0.342): The amount of pooling in the baseline network already turned out to be optimal, and thus, no substantial changes were made, explaining this result. For the second evaluation scenario, imposing relatively lax restrictions on the localization accuracy, our best-performing system achieved a PSDS score of 0.7312 on the validation data. This is significantly better than the performance obtained by the baseline model (0.527), which can effectively be attributed to the changes that were applied to the pooling operations of the network.
POS Tagging of Konkani, A Resource Scarce Indian Language

Supervised Machine Learning Models have achieved state-of-the-art results in POS Tagging for resource rich languages. Resource rich languages have the luxury of availability of huge annotated corpora. However, this is not the case with many Indian languages. Konkani is one such resource poor language with 7.4 million 1 native speakers, where there is a scarcity of available annotated data. Lack of tagged data has led to the exploration of unconventional methods like Active Learning, Self Training with Hidden Markov Models, Clustering using word embeddings and Graph Clustering. Out of all these, Graph Clustering, Active Learning, and Self
Reinforcing Moral Disengagement in the Relationship of Ethical Leadership on Employee Task Performance

This study analyzes the relationship between ethical leadership and task performance and the moderating effect of moral disengagement. The data in this study were 244 respondents who were taken by conducting a web-based questionnaire for employees in various industries in Indonesia. To process data and test hypotheses, researchers used the SEM-PLS approach. This study found that ethical leadership influences employee task performance. However, the relationship between ethical leadership and employee task performance was found to be positive and significant. This study also found that moral disengagement has a moderating effect, strengthening the relationship between ethical leadership and task performance. Keyword: Moral Disengagement, Ethical Leadership, Task Performance Abstrak Study ini menganalisis tentang hubungan etika kepemimpinan dengan kinerja tugas, dengan efek moderasi oleh pelepasan moral. Data dalam penelitian ini adalah 244 karyawan yang diambil dengan mengadakan kuesioner berbasis web di berbagai industri di Indonesia. Untuk mengolah data dan menguji hipotesis, peneliti menggunakan pendekatan SEM-PLS. Studi ini menemukan bahwa etika kepemimpinan memiliki pengaruh terhadap kinerja tugas karyawan. Hubungan antara etika kepemimpinan dengan kinerja tugas karyawan ditemukan positif dan signifikan. Pada penelitian ini juga menemukan bahwa pelepasan moral memberikan efek moderasi yaitu memperkuat hubungan etika kepemimpinan dan kinerja tugas. Kata Kunci: Pelepasan Moral, Kepemimpinan Etis, Kinerja
MOBILE SHOPPING ACCEPTANCE AMONG MILLENNIALS IN MALAYSIA PUBLIC UNIVERSITY

Mobile shopping has become a trend and popular way for people to make purchases in an easier and more convenient way. The present study rationalized that a facilitating condition construct would be a more critical construct than effort expectancy in mobile shopping. The study investigated factors that influence the mobile shopping acceptance among undergraduate students in public university in Malaysia. The study model consisted of six determinants which are utilitarian performance expectancy, hedonic performance expectancy, effort expectancy, social influence and facilitating conditions towards intention to adopt mobile shopping among millennials. A purposive sampling method were used with a total of 125 respondents of public university collected for data analysis. The findings found that utilitarian performance expectancy, hedonic performance expectancy, effort expectancy, and facilitating condition are positively related to intention to use mobile shopping however negatively supported for social influence. Utilitarian performance expectancy was found as the most significant factor that affect consumers adoption of mobile shopping through improving gains in shopping performance with hedonic performance expectancy as the most salient.
G LOBAL J OURNAL OF E NGINEERING S CIENCE AND R ESEARCHES SENTIMENT ANALYSIS ON TRAFFIC IN INDIAN CITIES

ABSTRACT In this paper, a Social Sentiment Analysis that looked at people‟s feelings about traffic in India‟s prominent metropolitan cities includes Bangalore, New Delhi and Mumbai is presented. We analyzed Sentiments of people about the Indian cities traffic with Real-time social media data. The sentimental analysis is the process of identifying and classifying views expressed in the form of text; in order the understood the writers view towards the particular subject or a product etc., in a negative, positive or a neutral way. This research is real-time traffic detection and monitoring system from twitter tweet stream analysis. It helps to discover the hidden value of text in order to penetrate the opinions of people. This system identifies the view about a particular topic and outperforms the traditional approaches.
Effectiveness of new algorithms for facial recognition based on deep neural networks

The applications of image classification occupied a wide field in the recognition of faces using the Convolutional Neural Network (CNN) with the mathematical aspect of deep learning based on the MATLAB program. This is not only facial recognition, but also recognizing the parts of the face, eye, mouth, and nose after designing programs in addition to the new algorithms with the theory of CascadeObjectDetector and trainCascadeObjectDetector. The benefit of this work emerges in the areas of security, airports, markets, and so on.