Reviewerly/exHarmony huggingface dataset:
Multipath MMT-based approach for streaming high quality video over multiple wireless access networks

Demand for video streaming content and multimedia services have increased dramatically. Delivering high throughput and low delay video data are real challenges to provide and sustain high quality mobile video streaming services. One type of solution to provide quality of experience is to exploit multipath strategies by taking advantage of the multiple network interfaces that are currently available in most wireless devices. Our work aims at delivering contributions by exploring the advantages of multipath video streaming using the MPEG Media Transport protocol. MPEG Media Transport is a multimedia application layer protocol with the capability of hybrid media delivery. In this article, we propose a Content-Aware and Path-Aware (CAPA) scheduling strategy for this protocol, considering both video features and path condition. The performance of our CAPA proposal is evaluated using ns-3 DCE with different realistic multipath network scenarios. We evaluate the performance of CAPA over heterogeneous wireless networks under congested network and wireless lossy network conditions, which are common network situations with significant adverse effects on video quality. Our approach yields considerable video quality improvement in both scenarios compared to Path-Aware strategy and a simple scheduling strategy, called Evenly Splitting. For congested network scenario, CAPA could increase PSNR, respectively, by up to 4.25 dB (12.97%), and 7.22 dB (20.58%) compared to Path-Aware and Evenly Splitting strategies. It could also improve SSIM, respectively, by up to 0.033 (3.78%), and 0.102 (12.54%) compared to Path-Aware and Evenly Splitting strategies. For wireless lossy network scenario, CAPA increases PSNR, respectively, by up to 6.84 dB (20.30%), and 9.43 dB (30.32%) compared to Path-Aware and Evenly Splitting strategies. The proposed strategy also provides improvements in terms of SSIM, by up to 0.100 (12.72%), and 0.113 (14.23%) compared to Path-Aware and Evenly Splitting strategies, respectively. We also evaluate how proposed CAPA handles the streaming of different video bitrates under congested network conditions to provide sufficient video quality. Finally, we provide an initial validation of fairness of CAPA to confirm fair access to the resources available across all paths.
Mobile Technologies in the Electrical Engineers Training

The purpose of the research is to analyze the prospects for the widespread using of mobile learning technologies in the electrical engineers training. Based on the recommendations and wishes of the stakeholders of the educational process (representatives of leading enterprises, i.e. potential employers, students of engineering specialties and university representatives), it is concluded that the implementation of mobile learning technologies will significantly modernize the training of electricians, bringing it closer to modern conditions labor. Using various devices, an electrical engineer organizes and manages the production process, coordinates the team work, implements energy-saving projects, etc. These competencies must be formed at the stage of university training.
Analysis of Deep Learning Architectures for Object Detection - A Critical Review

In the research work, we have performed a comparative study on the deep learning architectures in context with object detection. We have selected 4 architectures specifically for the critical comparative analysis and the selection of architectures depend on the optimality and compatabilty. We have carried out experiments on the architectures by tuning the parameters and training them on different datasets to understand their behaviour. The study evidently proves, that the identification of optimal parameters for a network in context with a specific dataset is always independent in nature.
Bounding-Box Channels for Visual Relationship Detection

Recognizing the relationship between multiple objects in an image is essential for a deeper understanding of the meaning of the image. However, current visual recognition methods are still far from reaching human-level accuracy. Recent approaches have tackled this task by combining image features with semantic and spatial features, but the way they relate them to each other is weak, mostly because the spatial context in the image feature is lost. In this paper, we propose the bounding-box channels, a novel architecture capable of relating the semantic, spatial, and image features strongly. Our network learns bounding-box channels, which are initialized according to the position and the label of objects, and concatenated to the image features extracted from such objects. Then, they are input together to the relationship estimator. This allows retaining the spatial information in the image features, and strongly associate them with the semantic and spatial features. This way, our method is capable of effectively emphasizing the features in the object area for a better modeling of the relationships within objects. Our evaluation results show the efficacy of our architecture outperforming previous works in visual relationship detection. In addition, we experimentally show that our bounding-box channels have a high generalization ability.
Can Permissionless Blockchains Avoid Governance and the Law?

Permission-less (or public) block-chain networks are a new form of decentralized private governance in the digital sphere. Though legal scholars recognize the significance of law in the use of block-chain, existing research using legal and institutional perspectives leaves block-chain governance as something of a black box. We provide a more granular analysis, finding that block-chain governance operates on four distinct levels. Governance at the protocol layer involves discrete institutional design choices intended to constrain network members’ incentives in an ongoing sense. Subsidiary governance arises from the need for communities to draft protocol updates and from the fact that governance protocol design choices create discrete concentrations of political power within the network. Competitive governance forces arise because cryptocurrency networks are constrained by the possibility of exit of participants and users to other alternatives. Finally, in terms of superior governance, permission-less cryptocurrency participants and users are subject to a variety of laws and regulations due to how cryptocurrencies implicate property, contracts, tax, and securities law. Since the interaction of these governance aspects shapes the operation of any given network, legal and regulatory governance of block-chain ought to consider permission-less block-chains as confronting political and governance dilemmas much like any complex organization. Predicting the effect legal and regulatory treatment of permission-less block-chains requires an accurate understanding of the incentives of their members, all of which are greatly shaped by the governance forces we outline in detail here.
A Survey of Distributed and Parallel Extreme Learning Machine for Big Data

Extreme learning machine (ELM) is characterized by good generalization performance, fast training speed and less human intervention. With the explosion of large amount of data generated on the Internet, the learning algorithm in the single-machine environment cannot meet the huge memory consumption of matrix computing, so the implement of distributed ELM algorithm has gradually become one of the research focuses. In view of the research significance and implementation value of distributed ELM, this paper first introduced the research background of ELM and improved ELM. Secondly, this paper elaborated the implementation method of distributed ELM from the two directions: ensemble and matrix operation. Finally, we summarized the development status of distributed ELM and discussed the future research direction.
Competences of university teachers: Contemporary challenges and change of paradigm

The article examines issues relating to forming relevant competences of university teachers. Teacher competences adequate to the requirements of today are a key factor of the progress of higher education in the context of the changing paradigm of societal development with its unpredictable and dynamic character. International practices and new goals are analyzed that require effective answers to the ever-aggravating challenges of contemporary development addressing which will contribute to maintaining and consolidating the position of universities in the modern world. The article offers recommendations to enhance in-service and pre-service teacher training in the context of the new challenges, including the growing share of online teaching and learning. The conclusions and recommendations can be used for policy decisions and for training purposes.
Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound

Mammography and ultrasound are extensively used by radiologists as complementary modalities to achieve better performance in breast cancer diagnosis. However, existing computer-aided diagnosis (CAD) systems for the breast are generally based on a single modality. In this work, we propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. We present various approaches and show a consistent improvement in performance when utilizing both modalities. The proposed approach is based on a GoogleNet architecture, fine-tuned for our data in two training steps. First, a distinct neural network is trained separately for each modality, generating high-level features. Then, the aggregated features originating from each modality are used to train a multimodal network to provide the final classification. In quantitative experiments, the proposed approach achieves an AUC of 0.94, outperforming state-of-the-art models trained over a single modality. Moreover, it performs similarly to an average radiologist, surpassing two out of four radiologists participating in a reader study. The promising results suggest that the proposed method may become a valuable decision support tool for breast radiologists.
Knowledge Graph Representation Learning With Multi-Scale Capsule-Based Embedding Model Incorporating Entity Descriptions

A Knowledge Graph (KG) is a directed graph with nodes as entities and edges as relations. KG representation learning (KGRL) aims to embed entities and relations in a KG into continuous low-dimensional vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. In this paper, we propose a KG embedding framework, namely MCapsEED (Multi-Scale Capsule-based Embedding Model Incorporating Entity Descriptions). MCapsEED employs a Transformer in combination with a relation attention mechanism to identify the relation-specific part of an entity description and obtain the description representation of an entity. The structured and description representations of an entity are integrated into a synthetic representation. A 3-column matrix with each column a synthetic representation of an element of a triple is fed into a Multi-Scale Capsule-based Embedding model to produce final representations of the head entity, the tail entity and the relation. Experiments show that MCapsEED achieves better performance than state-of-the-art embedding models for the task of link prediction on four benchmark datasets. Our code can be found at https://github.com/1780041410/McapsEED.
Real-time video dehazing via incremental transmission learning and spatial-temporally coherent regularization

Video dehazing is of crucial importance to the outdoor surveillance, and its main challenges mainly come from the spatial-temporal coherence and computational efficiency. This paper presents an efficient real-time video dehazing approach via incremental transmission learning and spatial-temporally coherent regularization, while explicitly suppressing the possible visual artifacts. First, we propose to initialize the transmission map frame-by-frame by a boundary constrained open dark channel model. Then, supposing a scene point yields highly correlated transmission values between adjacent frames, we impose a temporally coherent term to maintain the temporal consistency of consecutive transmission values, and simultaneously derive an incremental transmission adjusting term to adapt the abrupt scene depth changes between the adjacent frames. Accordingly, the highly correlated and spatial-temporally coherent transmission maps can be optimized for each video frame dehazing, whereby the flickering artifacts can be well reduced. Finally, we group each haze/haze-free images in pair and further utilize the guided joint bilateral filter to suppress the visual artifacts that possibly amplified during the dehazing process. The experimental results show that the proposed video dehazing approach is able to well preserve the spatial-temporal coherence, runs sufficiently fast, and also performs favorably compared to the state-of-the-art methods.
Centralized Access Point for Information System Integration Problems in Large Enterprises

The role of information systems is extremely important for a business, especially those operating in the field of technology. The information system works with people, information technology and processes to accomplish business objectives. In the development process of large companies, the member units create many applications to serve their purposes. Increasing the number of applications or portals consumes employees’ time, and they may eventually be unable to keep track of all the necessary or useful information. That is why it is necessary to build a centralized and intelligent information access point for large organizations. The paper presents such a solution for the largest software company in Vietnam, the FPT software company. The paper describes the architectures and technologies applied to build an access point that brings whole new experiences to all employees. By integrating smart features based on artificial intelligence, the work contributed to enhancing performance and quality of work for 28,000 internal users. The approaches and results of the paper are completely applicable to similar large company models.
A Security Problem Caused by the State Database in Hyperledger Fabric

Blockchain technology has been widely used in many applications, but its tamper proof capability is still a problem. Hyperledger Fabric is a typical alliance chain platform for reliable data sharing among multiple enterprises. To facilitate data query and transaction verification, Fabric introduces the concept of state data. However, the state data is not stored on the chain but in the local database. Through analysis of the Fabric source code, we found that the queried data from local data base will not be verified by comparing with the data on the chain, so it would be easy to modify the state data and issue ‘valid’ transactions based on the tampered data. This problem is confirmed by laboratory experiments. In the test, we modified the state data on the endorsing peers through the data base interface, and the transactions based on the tampered data were successfully synchronized to all the nodes in the same channel. This work reveals that the off-chain data could be a big security threat for small scale Fabric networks.
Multivariate <i>q</i>-Pólya and inverse <i>q</i>-Pólya distributions

An urn containing specified numbers of balls of distinct ordered colors is considered. A multiple q-Pólya urn model is introduced by assuming that the probability of q-drawing a ball of a specific color from the urn varies geometrically, with rate q, both with the number of drawings and the number of balls of the specific color, together with the total number of balls of the preceded colors, drawn in the previous q-drawings. Then, the joint distributions of the numbers of balls of distinct colors drawn (a) in a specific number of q-drawings and (b) until the occurrence of a specific number of balls of a certain color, are derived. These two distributions turned out to be q-analogues of the multivariate Pólya and inverse Pólya distributions, respectively. Also, the limiting distributions of the multivariate q-Pólya and inverse q-Pólya distributions, as the initial total number of balls in the urn tends to infinity, are shown to be q-multinomial and negative q-multinomial distributions, respectively.
Research on AUV Path Planning Based on Improved Ant Colony Algorithm

Because the traditional ant colony algorithm has the disadvantages of slow convergence speed and easy to fall into the local optimal solution in complex environments, so the paper improves the traditional ant colony algorithm. The improved method sets the initial pheromone concentration based on the position information of the current feasible target point and the end point, to improve the early convergence speed of the algorithm. And by optimizing the pheromone update method to speed up the global convergence speed of the algorithm and reduce the generation of local optimal solutions. Increase the convergence speed of the algorithm by increasing the transfer factor and optimizing the transfer mode. Combined with the motion characteristics of AUV, the path is re-planned to further optimize the path and improve the feasibility of the algorithm. Simulation results show that compared with the traditional ant colony algorithm, the improved algorithm conforms to the motion characteristics of AUV, has a faster convergence speed, and reduces the generation of local optimal solutions.
Two-stage deep regression enhanced depth estimation from a single RGB image

Depth estimation plays a significant role in industrial applications, e.g. augmented reality, robotic mapping and autonomous driving. Traditional approaches for capturing depth, such as laser or depth sensor based methods, are difficult to use in most scenarios due to the limitations of high system cost and limited operational conditions. As an inexpensive and convenient approach, using the computational models to estimate depth from a single RGB image offers a preferable way for the depth prediction. Although the design of computational models to estimate the depth map has been widely investigated, the majority of models suffers from low prediction accuracy due to the sole utilization of a one-stage regression strategy. Inspired by both theoretical and practical success of two-stage regression, we propose a two-stage deep regression model, which is composed of two state-of-the-art network architectures, i.e. the fully convolutional residual network (FCRN) and the conditional generation adversarial network (cGAN). FCRN has been proved to possess a strong prediction ability for depth prediction, but fine details in the depth map are still incomplete. Accordingly, we have improved the existing cGAN model to refine the FCRN-based depth prediction. The experimental results show that the proposed
Constituent polysemy and interpretational diversity in attested English novel compounds

Abstract We explore variation in the interpretation of attested novel compound nouns in English, especially the contribution of constituent polysemy to this diversity. Our results show that effects of polysemy are pervasive in compound interpretation, contributing both to interpretational diversity and to perceived difficulty of interpretation. The higher the uncertainty about the concept represented by the head noun, based on existing compounds with that head, the greater the diversity of interpretations across speakers and the more difficult, on average, they find it to come up with a meaning.
RETRACTED: The development of a systems mindset in the first-year students studying online at a vocational pedagogical university while mastering the basics of working with digital technologies

The purpose of the study is to show the possibility and feasibility of developing a systems mindset in the process of shaping competencies in information technology while the educational process is based on online learning. The features of the limitations of perception by first-year students in the field of systems that use digital technologies have been analyzed. An experiment based on the methodology for the development of a systems mindset in first-year students on the basis of a project-based approach in the process of online learning has been carried out. The study involved 250 students. The number of students who received average grades increased by 14.9 %; the number of students that received high grades increased by 15.65 % and 42.83 %, respectively. In the post-test of the experimental group, there were no participants who failed all tests. In the control group, there were no changes in the quality of a systems mindset. The novelty of the research concept is the fact that students learn the concepts of interactions in different systems in their first year of study that takes place in the online learning environment. The finding of the study can be useful for teachers of vocational universities that train first-year students.
Determining Bug Prioritization Using Feature Reduction and Clustering With Classification

Assigning accurate and timely priorities to bugs manually is resource consuming and effects addressing important bugs.In the existing work single feature is used which leads to information loss because bugs have a lot of features including ''severity '', ''component'', ''operation system'', ''owner'', ''status'', ''assigned to'', ''summary'' etc.In this research, the authors proposed an improved model based on problem title, severity, and component for bug prioritization.We converted these textual features to numeric features using Term Frequency Inverse Document Frequency.During conversion, 5591 new features are generated, which increase complexity and running time of algorithms.To minimize these aspects, non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA) algorithms are used.Our proposed model is a combination of feature reduction, clustering, and classification algorithms.Clustering is performed on all and reduced features.For clustering X-Mean and K-Mean algorithms are used.SVM and Naive Bayes classifiers are applied on all features, reduced features, and on clustered features.For experiments chromium, eclipse, net beans, mozilla, and free desktop datasets are used.Experimental results reveal better performance of model, both with all features and with reduced features in terms of precision, recall, f-score, and accuracy.Maximum improvement is achieved with reduced features.With all features chromium, eclipse, free desktop, mozilla and net beans achieved 22.46%, 8.32%, 30.93%, 25.79% and 37.78% respectively improvement in accuracy.With reduced features chromium, elipse, free desktop, mozilla, net beans achieved 14.64%, 8.81%, 33.22%, 34.37% and 41.01%accuracy respectively.Overall classification with clustering and reduced features performed better than classification on all features, classification with clustering on all features, and classification on reduced features.In all the approaches SVM classifier outperformed Naive Bayes in terms of precision, recall, f-score, and accuracy.On average maximum accuracy is achieved by SVM with NMF and X-Mean clustering.