Reviewerly/exHarmony huggingface dataset:
Towards Constructing HMM Structure for Speech Recognition With Deep Neural Fenonic Baseform Growing

For decades, acoustic models in speech recognition systems pivot on Hidden Markov Models (HMMs), e.g., Gaussian Mixture Model-HMM system, Deep Neural Network-HMM system, etc., and achieve remarkable results.However, the popular HMM model is the three-state left-to-right structure, without the superiority certainty.There are multiple studies on the HMM structure's optimization, but none of them addresses this problem leveraging deep learning algorithms.For the first time, this paper proposes a new training method based on Deep Neural Fenonic Baseform Growing to optimize the HMM structure, which is concisely designed and computationally cheap.Moreover, this data-driven method customizes the HMM structure for each phone precisely without external assumptions concerning the number of states or transition patterns.Experimental results on both TIMIT and TEDliumv2 corpora indicate that the proposed HMM structure improves both the monophone system and the triphone system substantially.Besides, its adoption further improves state-of-the-art speech recognition systems with remarkably reduced parameters.
Regulatory Responses to Cryptoderivatives in the UK and the EU: The Future of Cryptoderivatives in the US

This paper puts forth a comparative analysis of the US regulatory responses to cryptocurrency-related derivatives ("cryptoderivatives") with specific references to the UK’s and the EU’s approaches and motives towards cryptoderivatives regulations in their respective regions. It discusses that the UK and the EU regulators primarily focus on protecting the retail investors from monetary losses arising from investment in cryptoderivatives products. In contrast, the US regulatory efforts are limited to interpreting cryptocurrency in light of the existing legal and regulatory framework, despite cryptocurrency’s novel risks of price volatility and susceptibility to spot market manipulation, and the CFTC’s lack of oversight over the cryptocurrency spot market. It explores the possibility of imposing an outright ban on cryptoderivatives like the UK but concludes that an outright ban on the cryptoderivatives market is likely to jeopardize financial innovation growth in the US. Furthermore, some cryptocurrency trading platforms are already complying with the existing laws and regulations, and an outright ban will put set them back. Therefore, to protect market integrity and safeguard retail investors’ interest, the paper proposes that Congress enact a comprehensive cryptocurrency regulation recognizing the novelty of the cryptocurrencies’ market risks and introducing effective regulatory treatments to curb market manipulation in the cryptocurrency spot market vis-à-vis cryptoderivatives market. This paper envisions a comprehensive cryptocurrency regulation, which would include (1) centralization of cryptocurrency trading platforms, (2) a mandatory registration requirement for all cryptocurrency exchanges, and (3) a federal cryptocurrency agency, having exclusive jurisdiction over cryptocurrencies and oversight authority on the cryptocurrency spot markets.
Efficient Mobile User Authentication Service with Privacy Preservation and User Untraceability

Security questions and answers for authentication are a common approach to enable the user to reset forgotten passwords. Moreover, they are also sometimes used as alternative for the classical username-password system, which fails in offering a good balance between user friendliness and security as long and complex passwords are required. However, in order to guarantee the privacy of the user as imposed by the new General Data Protection Regulation (GDPR), it should be impossible to derive the answer of the user by any other entity, including the server provider or the server managing the authentication.In this paper, we present an efficient mobile based security mechanism to realise this goal. The proposed scheme can be applied on top of any type of question-answer based authentication system. In addition, our solution also offers anonymity and untraceability of the user, such that no activity patterns can be drawn by simply eavesdropping on the communication channel to the service provider or the authentication server. We show that our proposed mechanism not only offers more security features compared to related work, but it is also significantly faster, in particular at the side of the user.
Siamese Network for Single Image Rain Removal

This work focuses on removing rain streaks from the single image using the Siamese network. The Siamese network with an unique structure, where the inputs are paired images, are utilized to obtain the discriminative features of the input. We explore a method using the Siamese neural network to learn the difference between rainy images and clean images. The convolutional architecture is used for feature learning. Once the network has been tuned, the key feature of the rain is learned, then the rain streaks can be removed. By training the network on synthetic data, our framework can be generalized in the real rainy image tests. Numerical experiments show that the proposed method performs well in removing rain for the synthetic rainy images in terms of visual and quantitative measures.
Optimizing Fund Allocation for Game-Based Verifiable Computation Outsourcing

This paper considers the setting where a cloud server executes tasks submitted by multiple clients. Every client wishes to assure honest execution of the tasks by employing a trusted third party (TTP) to verify with a probability. The cloud server makes a deposit for each task it takes, each client allocates a budget for each task submitted, and every party has its limited fund. We study how to allocate the funds optimally such that: a economically-rational cloud server honestly computes each task; the server’s wage is maximized; the delay for task verification is minimized. Game theory is applied to formulate these problems, and optimal solutions are developed. Each solution is evaluated through rigorous proofs. To the best of our knowledge, this is the first work on optimizing fund allocation for verifiable outsourcing of computation in the setting of one server and multiple clients, based on game theory.
Research on the Application of Artificial Intelligence Technology in the Internet in the Era of Big Data

In the era of big data (BD), artificial intelligence, as a direction of international development in the next few years or even decades, has a range of applications, applicable to all fields of life. Today, AIT is also integrated into production and life. Especially in the Internet, the application of artificial intelligence technology (AIT) is of great significance. Computer network technology has experienced continuous technical reform and improvement from the beginning to the present. Now, the era of BD is coming. With the help of artificial intelligence, we can realize the management and control of computer network more effectively, and improve the application effect of computer. This paper first introduces the concepts of BD and AIT, and then describes the advantages of AIT in the era of BD. Finally, it offers advice for the network management of artificial intelligence application in the era of BD. The results show that the strategy can ameliorate the intelligent level of network management, greatly reduce the artificial participation, reduce the cost of network management, and play a part in the efficiency of network management.
Opportunities, barriers, and enablers of blockchain in supply chains

In this chapter, we provide details of key enablers of blockchain in supply chains. It will discuss major opportunities that can result from blockchain’s use in supply chains. It provides a perspective on the use of blockchain-based solutions to enhance branding, empower consumers, and increase their satisfaction with services. We also offer an overview of barriers in implementing blockchain in supply chains. These issues are discussed from the perspective of blockchain’s potential to act as an effective governance mechanism, so that it can contribute in producing supply chain trust. We also explain why deployment of blockchain-based solutions to track mineral in many African countries is difficult due to complex socioeconomic, political, and infrastructural challenges facing them.
Novice Teachers’ Difficulty in Teaching English at Language Development Unit of UIN Antasari Banjarmasin

This research aimed to describe the difficulties faced and the problem solving obtained by novice teachers for teaching in the English class at Language Development Unit. Benefit of this research are to be an evaluation and improvement of teaching quality and can be considered and illustrated for students who have plan to become an English teacher at UPB UIN Antasari Banjarmasin in the future.Researcher used a descriptive technique and qualitative research type for the method of this research. Researcher used questions in the form of interviews as the instrument of this research to collect the data. This research was conducted online due to the Covid-19 health protocol.The result of this research showed that novice teachers at UPB UIN Antasari Banjarmasin faced many difficulties, such as: students often make the noisy, some of novice teachers used inappropriate methods with the learning material and others. The problem solving used by the novice teachers is varies depending on the difficulties they face like giving briefings to students at the first meeting for discipline rules in class, sometimes teachers get involved in debates with students and use media which takes a lot of time for teachers to give lessons and so on.
Siamese Networks-Based People Tracking Using Template Update for 360-Degree Videos Using EAC Format

Rich information is provided by 360-degree videos. However, non-uniform geometric deformation caused by sphere-to-plane projection significantly decreases tracking accuracy of existing trackers, and the huge amount of data makes it difficult to achieve real-time tracking. Thus, this paper proposes a Siamese networks-based people tracker using template update for 360-degree equi-angular cubemap (EAC) format videos. Face stitching overcomes the problem of content discontinuity of the EAC format and avoids raising new geometric deformation in stitched images. Fully convolutional Siamese networks enable tracking at high speed. Mostly important, to be robust against combination of non-uniform geometric deformation of the EAC format and partial occlusions caused by zero padding in stitched images, this paper proposes a novel Bayes classifier-based timing detector of template update by referring to the linear discriminant feature and statistics of a score map generated by Siamese networks. Experimental results show that the proposed scheme significantly improves tracking accuracy of the fully convolutional Siamese networks SiamFC on the EAC format with operation beyond the frame acquisition rate. Moreover, the proposed score map-based timing detector of template update outperforms state-of-the-art score map-based timing detectors.
Deep Learning Platforms for High-Performance and Distributed Computing Object Detection

Deep learning developed in the last decade and has been established as recent, modern and very promising technique with large potential to be successfully applied in various domains. Despite deep learning outperforms alternative techniques in providing solution to many different problems there are several concerns and limitations of the its usage, one of them being the computational complexity of the training process. The paper discusses the possibilities of taking advantage of high-performance and distributed computing for efficiently and effectively training deep learning models for computer vision applications. A comparison of deep learning platforms and frameworks utilized on high-performance computer systems is presented and their application for computer vision problem of object detection is evaluated based on a suggested parallel computation architectural model and utilization of RetinaNet model.
Creative Cards on Chemistry in the Mental Development of Students

Abstract The change in the professional positioning of a modern university graduate — from the narrow development of one specialty to the ability to adapt to the changing conditions of the innovation economy; reliance on creativity and mental development of an individual identified the research problem — the lack of methodological support to develop teaching aids at the university. The theory comprehension and practice of the educational process organization in the chemical direction made it possible to affirm that there are large reserves for the students’ mental development due to the completeness and depth, consistency, and logic of a high theoretical level in the chemical disciplines. The study purpose is to develop and determine the effectiveness of the use of creative cards in the study of chemical disciplines to improve the level of students’ mental development. The research methodology included an analysis of the essence of the concept «creative card», identification of criteria and indicators of students’ mental development, development of a set of creative cards in chemistry, determination of their influence on the mental development level, development of a methodical system of students’ mental development using creative cards, and testing the effectiveness of the system in a teaching experiment. The basis of the chosen approach is an idea of cognitive psychology D. P. Ausubel. Presentation of new concepts and judgments through logical structures and concepts are already known; accumulated by students, identifying the main ideas of the subject, further differentiation, detailing; combining new material with previously presented information by comparing, comparing and finding connections between them is one of the effective tools to organize and present knowledge. However, the study made it possible to substantiate creative cards as means of learning that affects mental development. Developed methodological support for the use of creative cards to improve the level of students’ mental development, confirmed its effectiveness.
Web service composition in the cloud environment Based on Modified Beetle Antennae Particle Swarm Optimization

Based on the fast development of Internet and big data, how to pick out a high-quality service composition quickly from large qualities of services, and meet users' needs have become a hotspot in service composition research. Service composition optimization is a classic NP hard problem. A modified Beetle Antennae Particle Swarm Algorithm was proposed in this paper and it was applied in web service composition optimization. The author introduced Beetle Antennae Searching Algorithm into the modified Particle Swarm Algorithm, modified position update formula, adopted the modified Nonlinear Dynamic Trigonometric Learning Factors to control the particles' expanding capacity and global convergence capability and incorporated the modified Secondary Oscillation factors which increased the searching precision of the algorithm and global searching ability. The experimental results under real data set indicated that the modified Beetle Antennae Particle Swarm Algorithm proposed in this paper could settle large-scale web service composition optimization problems in cloud environment, had good global searching ability, comparatively faster convergence speed and needed less time cost.
The 9th ACM SIGSPATIAL International Workshop on Analytics for Big Spatial Data (BigSpatial 2020)

Since the "Big Data Research and Development Initiative" launched by the White House in 2012, big data has received great attention from industry and federal agencies alike emerging as an important area of research for scientists worldwide. Within the realm of big data, spatial and spatiotemporal data continues to be among the fastest-growing types of data. With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in the ground, air- and space-borne sensor technologies have led to unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. Today, analyzing this data poses a massive challenge to researchers.
Using Conditional Sentence Representation in Pointer Networks for Sentence Ordering

Sentence ordering aims to arrange sentences in a coherent manner and hence has important applications in Natural Language Generation. Recently, several approaches have used Pointer Networks for this task. Such networks arrange a list of sentences based on fixed sentence representations, where these representations are independent of the sentence's position in the text and its relation to the previously selected sentences. In this work, we propose a conditional sentence representation, which incorporates the information of the previously selected sentences into the candidate sentence representations. By using such information, the Pointer Network is able to better capture dependencies among sentences. Experiments indicate that our proposed model achieves state-of-the-art performance on most sentence ordering benchmarks and achieves a significant improvement over state-of-the-art performance on short stories datasets.
Comparative Analysis and Implementation of Popular Sorting Algorithm

Comparative Analysis and implementation of Sorting Algorithm aims at introducing five different algorithms, which sort the elements of an array in place. Sorting is an efficient algorithm to perform it has been developed in many applications. Comparative Analysis helps us in sorting sequences lookup for search, merging of sequences and processing of data. A comparison has been made between this particular idea and with the help of other popular implementations. We introduce five different algorithms which give us best time complexity for sorting of one Data set which we introduce during our program. It arranges the rundown of components with the goal that beginning anyplace considering each nth component gives an arranged rundown. With the proposed work we have done we finally reach out the conclusion and see that the situations where this outperforms and also look at the shortcomings which going to be occur and list the scope for future improvements that that could be made.
Particle swarm stepwise (PaSS) algorithm for information criteria-based variable selections

A new stochastic search algorithm is proposed for solving information-criterion-based variable selection problems. The idea behind the proposed algorithm is to search for the best model for the previously specified information criterion using multiple search particles. These particles simultaneously explore the candidate model space and communicate with each other to share search information. A new stochastic stepwise procedure is proposed to update the model during the search for the best model by adding or deleting variables. The proposed algorithm can also be used to generate variable selection ensembles efficiently. Several examples are used to demonstrate the performances of the proposed algorithm. A parallel version of the proposed algorithm is also introduced to accelerate the performance in terms of computation time.
Analytical and Instrumental Framework for The Analysis and Resolution of Stakeholder Interest Conflict Using Cognitive Maps

Abstract The statement of the basic control problem of a situation with many active stakeholders using cognitive maps is presented. Approaches to the analysis and resolution of interest conflicts in such situations based on the basic control problem are proposed. These approaches make it possible to take into account different interests, identify contradictions and, if possible, find ways to overcome these contradictions by choosing different strategies for interaction between stakeholders. The problems systematization of conflict analysis and control in such situations at different phases of the conflict is presented. The systematization purpose is to organize monitoring, analysis and control of conflict situations using cognitive maps and scenario modeling tools based on them.
Criteria for the effectiveness of teaching technologies in modern practices of professional training of teachers

The study presents results of the “Study of the features of the influence of teaching technologies at a university on the formation of professional competencies of students and graduates of a pedagogical university”, carried out based on the Moscow City University in 2018–2019. The prerequisite for the study is a situation when education is increasingly beginning to be built around requests from the future, in which people study all their lives and act in a constantly changing world. Therefore, the educational process in a modern pedagogical university is focused on such requirements as complexity, adaptability, practicality, integration into society and the economy, and focus on the needs of students and their future employers. In a university that implements an innovative teaching model, a variety of teaching technologies are used; teachers have the right to freely choose the forms and means of teaching, in connection with which the problem of the effectiveness of the use of teaching technologies is actualized. One of the purposes of the study is to identify criteria for the effectiveness of teaching technologies in the process of forming the professional competencies of future teachers. As a result of the use of survey methods, content analysis, focus groups, and case analysis, 12 criteria were identified: feasibility of using teaching technology, flexibility in application, portability of practice, development of pedagogical thinking, development of soft skills, focus on “self-processes” and “proprocesses”, interactivity, practical orientation, as well as digitalization, resource intensity, and efficiency. The results make it possible to analyze and evaluate the effectiveness of teaching technologies in the practice of training future teachers. The novelty of the work and its results are determined by an original approach to identifying criteria for the effectiveness of learning technologies in the educational process of a pedagogical university.
Benchmarking of University teachers’ professional development: successful European, Chinese, and Russian practices

In the context of socio-pedagogical transformations of higher education organizations, the modernization of the system of teachers’ professional development becomes an integral component of the reforms. Today university teachers have to be ready for continuous development and advanced training throughout their lives. Thus, the study of the problem of university teachers’ professional development in Russia and abroad is very relevant and timely as a response to the modern requirements of civil society for the personality of the teacher. The need for professional development among university teachers is closely related to his (her) desire for more successful indicators in teaching activities. To do this, it is necessary to fulfill several pedagogical conditions, including the teacher's own awareness of the need for professional development; the interest of the university administration and the availability of resources to organize an effective professional development system, etc. The purpose of the article is to show some successful practices of the teachers’ professional development in Russian, Chinese and European universities which the author observed while visiting those universities within the realization the project “Enhancing teaching practice in the universities of Russia and China”. The article presents the possibilities of benchmarking in higher education, in particular, the use of the benchmarking technology as a method of studying the effective practices of organizing the teachers’ professional development in a modern university.