Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence-Led Content Publishing, Metadata Creation, and Knowledge Discovery

Digital transitions have had strong headwinds in scholarly publishing for the past decade. It started with digitising content and is resting somewhere between tying up diverse content and catering to diverse end users. The goal is still to keep up with the changing landscape, and a demonstrable way of doing so is to actively participate by quickly adapting to standards. Artificial intelligence (AI) has a proven track record of helping with this and is an integral part of the solution frameworks. The chapter content includes a brief insight into some practices and workflows within scholarly publishing that stand to benefit from direct intervention of AI. These include editorial decision systems, metadata enrichments, metadata standardization, and search augmentations. The authors bring to light various developments in scholarly publishing and the status of some of the best implementations of AI techniques in aiding and upkeep of the ‘digital transformations'.
Movement Arrangement Auxiliary Systems Design for Cheerleading Complete Set

This paper takes the computer-aided system of Cheerleading choreography as the research object. By using the methods of literature review, questionnaire survey, expert interview, mathematical statistics and other research methods, this paper makes an in-depth study on the demand analysis, design and establishment of the cheerleading set action arrangement auxiliary system, as well as its feedback and improvement in the later stage. At the same time, the photo shop, beaver’s nest video format converter and other tool software are used to modify and edit the pictures and video resources of online courses, and finally complete the construction of the auxiliary system for cheerleading choreography.
Problem of Semantic Enrichment of Sentences Used in Textual Requirements Specification

In this paper, we describe our graph-oriented method used to find semantically similar sentences in external information sources that have a semantic enrichment potential in relation to sentences of textual functional requirements specification. Our motivation is to reduce the incompleteness of requirements that may be a source of inconsistency. We found there are some facts and rules so obvious for domain experts that they do not even mention them in requirements. We call such rules default consistency rules. These rules are often not implemented and can not be revealed from the requirements because they are not mentioned there.
Abnormal Event Detection in Public Places by Deep Learning Methods

Surveillance in public places has become an important aspect of modern lifestyle and security purposes. This is due to an increase in the number of crimes, mischievous activities and abnormal events. The monitoring process should be automated because of the excessive time consumption associated with the manual monitoring process. In general, there are many deep learning methods that exist for the classification process of abnormal events. This article provides a comparative analysis of three state-of-the-art deep learning methods used for abnormal event detection. The three methods, namely convolutional long short-term memory (CLSTM) autoencoders, convolutional autoencoders (CA) and one-class support vector machines (SVM) are tested on a benchmark dataset UCSD Ped1 which contains 34 training videos and 36 testing videos. Out of the three methods of implementation, one-class SVM offers the highest area under the curve (AUC) about 0.692 with the accuracy of 65.82%. By employing these deep learning strategies, the occurrence of abnormal events at public places like malls, roads and parks can be detected quickly, and immediate remedial measures can be implemented to enhance the public security.
Further potential applications of group theory in information security

Group theory, specifically the combinatorial group theory of finitely presented groups,has been utilized effectively in cryptology. Several new public key cryptosystems have been developed and this has ushered a new area in cryptography called group based cryptography. Braid groups have been suggested as possible platforms and this has led to what is called braid group cryptography. This has also had a profound effect on theoretical group theory as techniques have been found to analyse these group-based cryptosystems.The basic idea is that a finitely presented group can be described by a finite amount of data.This provides techniques to enormously compress and hide information. This suggests that we have only barely scraped the surface of using finitely presented groups for data control, security and storage. For example, we describe a far-reaching extension for controlling access to files which could be relevant in medical records.
Smart nanosensors for blockchain- and IoT-enabled sensing

There is recent advancement witnessed in the field of communication technology which is none other than Internet of Things (IoT), the IoT has become the need of the world and with the advancement of nanotechnology in the field of semiconductor the devices are also available of nanoscale which improves the working of the devices. One of the major developments which the world has witnessed is also the Blockchain, which the basis of the Bitcoin, one of the most popular cryptocurrencies. The blockchain is not only limited to the cryptocurrency but also in the field of finance, data handling, security management, improving the reliability of the data. The blockchain when combined with the IoT and nanotechnology will become a complete package of the technology which will be secure, fast and reliable way of transaction and communication of data. This chapter discuss mainly smart nanosensor, blockchain and the implementation of the blockchain with IoT on various cloud platform which improves the working of the technology and makes the process more reliable.
Speaker Recognition System Using Dynamic Time Warping Matching and Mel-Scale Frequency Cepstral Coefficients

Automatic speaker recognition is to recognize speech given by speaker automatically. The difference between speaker recognition and speech recognition is that it does not focus on the information of text symbols and semantic content with the speech signal included but fixes on the personal features with the speech signal included, furthermore, extracts these personal information features of the speaker to achieve the purpose of identifying speakers. Research on speaker recognition began in 1930s []. The early stage research mainly payed attention to the ear of human hearing recognition experiment as well as discovering possibility of listening recognition. Research work has moved from the pure human ear. Later, with improvement of electronic technology and computer technology, there is the possibility of automatically recognizing human voices through machines.
Learning to Control of an Under-actuated Autonomous Surface Vehicle Based on Model-based Deep Reinforcement Learning

Control of autonomous surface vehicles is a challenging task due to their nonlinearities, imposed disturbances, strong couplings, under-actuation, and various constraints. Prevailing methods are basing on an explicit mathematical model. The paper presents a learning-based motion controller for an autonomous surface vehicle without any mathematical models. A model-based deep reinforcement learning approach is propose for achieving the trajectory following task. At first, a deep neural network is trained for approximating the dynamical model of the autonomous surface vehicle by using recorded input and output data only. Then, a model predictive controller based on the learned neural network together with a reward function is presented for the autonomous surface vehicle to follow arbitrary trajectories. It is shown that after learning with random data collected from the autonomous surface vehicles, the proposed learning-based controller is able to follow trajectories with excellent sample efficiency. Simulation results are given to illustrate the proposed model-based deep reinforcement learning method for trajectory following of an autonomous surface vehicle.
A Comprehensive Addressing and Erudition on Blockchain in IOT Fortification

Blockchain is the technology that provides security through its cryptography. IOT (Internet of Things) enhances the usage of software and hardware power in efficient way. The IOT Devices can be configured and controlled by blockchain. In this, the analysis of blockchain in IOT Security presented. The Key management is one of the biggest features for blockchain to be successful in the technology. As security is essential for any technology to be successful, the importance is considered and revealed about the security of IOT through blockchain technology. The features and considerations made would be useful for further research on blockchain in IOT Security.
Visual place recognition from eye reflection

The cornea in the human eye reflects incoming environmental light, which means we can obtain information about the surrounding environment from the corneal reflection in facial images. In recent years, as the quality of consumer cameras increases, this has caused privacy concerns in terms of identifying the people around the subject or where the photo is taken. This paper investigates the security risk of eye corneal reflection images: specifically, visual place recognition from eye reflection images. First, we constructed two datasets containing pairs of scene and corneal reflection images. The first dataset is taken in a virtual environment. We showed pre-captured scene images in a 180-degree surrounding display system and took corneal reflections from subjects. The second dataset is taken in an outdoor environment. We developed several visual place recognition algorithms, including CNN-based image descriptors featuring a naive Siamese network and AFD-Net combined with entire image feature representations including VLAD and NetVLAD, and compared the results. We found that AFD-Net+VLAD performed the best and was able to accurately determine the scene in 73.08% of the top-five candidate scenes. These results demonstrate the potential to estimate the location at which a facial picture was taken, which simultaneously leads to a) positive applications such as the localization of a robot while conversing with persons and b) negative scenarios including the security risk of uploading facial images to the public.
Data Mining, Applications Tools in Insurance Strategies

Insurance companies have been currently actualizing information warehousing innovation, which encourages tremendous enterprise-wide databases. Subsequently, the measure of information that associations have is developing at an amazing rate. The following test for these associations is the manner by which to translate the information and how to change it into valuable data and learning. Data mining is one innovation utilized for addressing this difficulty. This article gives a far-reaching perspective on the innovation's techniques, bolster devices, and applications.
Strategic Analysis of Smart University Resource Potential for Management Objectives

Currently, solving the problem of limited resources requires a constant search for possible reserves. The availability of sufficient resources forms the resource potential. The effectiveness of its use predetermines the success of the Smart University development not only in the current period, but also in the long term. Strategic analysis of the resource potential, taking into account changes in the external and internal environment of the Smart University, is of particular importance for management. The studies have shown that the existing organizational and methodological approaches to the strategic analysis of the resource potential do not take into account the specifics of the Smart University, which confirms the relevance of the scientific article. The article reveals the concept of resource potential in relation to a Smart University. The result of the study is the proposed stages of strategic analysis of the resource potential of Smart-University, as well as the development of a logical model for strategic analysis of the resource potential of Smart-University for management purposes. The results obtained on the strategic analysis of the resource potential for management purposes were tested in the Smart divisions of the university using the example of the department.
The Effectiveness Of Health Education On The Behavior Of Choosing Tracks On Students In Sd Negeri 03 Kota Baru Lahat

School age children need sufficient food in quantity and quality in order to have a good nutritional status. The habit of children not having breakfast at home coupled with the habit of parents giving pocket money to children is a triggering factor for the emergence of unhealthy snacking behavior at school. Children often buy snacks. Carelessly and choose interesting types of food with low quality snacks and poor food ingredients. This study aims to determine the differences before and after being given health education on the behavior of choosing snacks for fifth grade students at SD Negeri 03 Kota Baru Lahat in 2019.  This study is a quantitative study using a quasi experimental design with one group pre-test and post-test models. The sample in this study amounted to 46 people, taken through a total sampling method to all students in class V (Five) SD Negeri 03 Kota Baru
Energy-Efficient Algorithms Used in Datacenters: A Survey

The count of datacenters is drastically increasing as the need for cloud computing is increasing day by day. Datacenters are the major electricity consumers in the world (Khan et al. in IEEE Trans Cloud Comput, 2019 []). Successful cloud service providers (CSP) such as Google, Amazon, and Microsoft are having a huge number of datacenters worldwide with hundreds of servers (PMs) to satisfy their customers’ requirements (Wang et al. in Comput Netw 161, 2019 []; Cheng et al. in Heterogeneity aware workload management in distributed sustainable datacenters. In: IEEE 28th International Parallel and Distributed Processing Symposium, 2014 []). Servers along with their network and cooling equipment require a huge amount of energy to operate. A study has shown that data centers will use around 3–13% of global electricity in 2030 compared to 1% in 2010 (Anders et al. in MDPI Challenges, 2015 []). Data centers not only consume a lot of energy but also release a lot of greenhouse gas (GHG). Energy consumption and carbon dioxide emission of datacenters are the major concerns of cloud service providers (CSPs). The benefits of cloud datacenters are given to us by the cost of global warming since the servers used in datacenters dissipate a lot of heat (Xu et al. in IEEE Trans Sustain Comput 4(1), 2019 []). Implementation of holistic approaches such as advanced frameworks, power-efficient electronic devices, and well-designed cooling and heating equipment are not sufficient to address the problem of energy consumption. Effective use of servers in datacenters will help the CSPs to face the energy efficiency challenge to a greater extent. Allocating services to power-efficient PMs and reducing the count of running PMs are the best practices followed in datacenters. The purpose of this survey is to study different kinds of algorithms used in datacenters in recent years to achieve energy efficiency targets and to minimize carbon dioxide emissions.
Towards Co-Evolution of Random-Walk-Based Embedding and Label Propagation for Node Classification

We present a looped framework consisting of a task-specific node embedding module (XLAE) and a label propagation module (LP), to meet the challenges of node classification tasks. XLAE incorporates intermediate labels into node representations towards capturing local context information of both features and labels by running a Skip-gram model; LP leverages task-specific node embeddings to improve pairwise node proximity metric towards more accurate approximation of actual node labels. By this means of mutual promotion, XLAE and LP co-evolve in the loop and iteratively improve predictive accuracy. Our algorithm outperforms node embedding baselines on node classification over small subsets of labeled instances in both graph benchmark and social network datasets. With less parameters in the model and higher computational efficiency preserved, XLAE-LP closely matches or outperforms the state-of-the-art graph convolutional neural network approach on node classification.
Towards instrumental catalogs of gravitational instabilities at local and regional scales by a combined seismology and machine learning approach

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Seismology allows continuous recording of the activity of gravitational instabilities whatever the context, and is therefore able to provide a tool for the study of the spatio-temporal evolution of the activity of gravity instabilities with a unique resolution. Due to the considerable fall in the costs of the means of acquiring seismological data and the increasing densification of global, regional and local networks observed in recent years, the amount of data to be processed is growing exponentially. Thus access to information is more and more complete but in return the volume of data to be processed becomes considerable. To analyze this volume of data and extract relevant information, it is necessary to develop automatic methods of identification of seismic sources and location to quickly build the most complete seismicity catalogs possible. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;We present a new machine-learning based method for automatically constructing catalogs of gravitational seismogenic events from continuous seismic data. We have developed a robust and versatile solution, which can be implemented in any context where seismic detection of landslides or other mass movements is relevant. The method is based on spectral detection of seismic signals and the identification of sources with a machine learning algorithm. Spectral detection detects signals with a low signal-to-noise ratio, while the Random Forest algorithm achieves a high rate of positive identification of seismic signals generated by landslides and other seismic sources. The processing chain is implemented to operate in parallel in a high-performance data center, which allows years of continuous seismic data to be explored and a database of events to be rapidly built up. This solution is also deployed for near-real time seismicity catalogs construction in the framework of slow moving landslides monitoring done by the Observatoire Multidisciplinaire des Instabilit&amp;amp;#233;s de Versants (OMIV). Here we present the preliminary results of the application of this processing chain in different contexts, locally for the monitoring of slow-moving landslides (La Clapi&amp;amp;#232;re, Super-Sauze, S&amp;amp;#233;chilienne), and at the regional level for the detection of large landslides field (Alaska and Alps).&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
Application of Artificial Intelligence Technology in the "Four in One" Accounting Talent Training Model

Artificial intelligence technology not only promotes major changes in the economy and economic paradigm and economic environment, as well as business models, production models and management models, but also promotes the development of data intelligence from the "four in one" to an accounting talent training model. Based on the current background of continuous improvement and rapid development of artificial intelligence technology, this article studies and applies it in the current "four-in-one" accounting professional training model. This article researches the training model of accounting professionals in polytechnic universities. First, it studies the current situation of accounting talent training at home and abroad; secondly, it explains the relevant theories of talent training. Through the use of questionnaire surveys for empirical research methods, it analyzes and studies 120 sample data from accounting majors in the University of Science and Technology, and analyzes the accounting majors. Students’ own abilities, and then to reflect on the problems of artificial intelligence technology in the "four-in-one" accounting talent training model, and further propose countermeasures to reform and perfect the "four-in-one" accounting talent training model based on artificial intelligence technology. The experimental research results show that the use of artificial intelligence technology to enrich the teaching channels of accounting, the rapid development of artificial intelligence technology has promoted the continuous innovation of higher education reforms, and university scientific research uses Internet technology to carry out online and offline integrated teaching and adopt various methods to train accounting Talent.
Deep Reinforcement Learning: A New Frontier in Computer Vision Research

Computer vision has advanced so far that machines now can think and see as we humans do. Especially deep learning has raised the bar of excellence in computer vision. However, the recent emergence of deep reinforcement learning is threatening to soar even greater heights as it combines deep neural networks with reinforcement learning along with numerous added advantages over both. This, being a relatively recent technique, has not yet seen many works, and so its true potential is yet to be unveiled. Thus, this chapter focuses on shedding light on the fundamentals of deep reinforcement learning, starting with the preliminaries followed by the theory and basic algorithms and some of its variations, namely, attention aware deep reinforcement learning, deep progressive reinforcement learning, and multi-agent deep reinforcement learning. This chapter also discusses some existing deep reinforcement learning works regarding computer vision such as image processing and understanding, video captioning and summarization, visual search and tracking, action detection, recognition and prediction, and robotics. This work further aims to elucidate the existing challenges and research prospects of deep reinforcement learning in computer vision. This chapter might be considered a starting point for aspiring researchers looking to apply deep reinforcement learning in computer vision to reach the pinnacle of performance in the field by tapping into the immense potential that deep reinforcement learning is showing.
Probability distribution based resource management for multitenant cloud clusters

Abstract Accurate resource allocation and colocating jobs are effective ways to increase resource utilization and reduce costs in modern datacenters. The main challenges are fluctuations in resource consumption and interference among colocated jobs. Therefore, we propose a resource management schema based on the probability distributions of resource consumption and completion time for multitenant cloud clusters. First, we found that the characteristics of the task can be well described by the probability distribution of resource consumption and completion time, and the probability distribution function can be obtained by Gaussian fitting. Second, we propose a probability distribution based resource allocation (PDRA) strategy for batch jobs. Third, we design a tail latency aware allocation (TLAA) strategy to use transient resources efficiently while ensuring tail latency requirements. Finally, we design a cost‐effective resource revocation (CERR) strategy to revoke transient resources with minimal eviction costs. Experimental results demonstrate the efficiency of our resource management. Our resource allocation strategies (PDRA and TLAA) can effectively improve resource utilization and reduce job completion time. CERR can reduce the impact of resource revocation on batch jobs and perform better than existing resource revocation strategies.