Reviewerly/exHarmony huggingface dataset:
Artificial chemical reaction optimisation of recurrent functional link neural networks for efficient modelling and forecasting of financial time series

In contrast to multilayer neural networks, a functional link artificial neural network uses functional expansion units for transferring lower input space to higher dimensions. It achieves enhanced discrimination capability through generating hyperplanes in the input space. The feedback properties of recurrent networks made them more proficient and dynamic to model nonlinear systems accurately. This paper develops a recurrent functional link artificial neural network (RFLN)-based forecasting model where the optimal model parameters are efficiently searched with artificial chemical reaction optimisation (ACRO). The reason behind using ACRO is its faster convergence toward optimal solution with fewer tuning parameters. The optimal model is achieved through the process of artificial chemical reaction of potential RFLN structures, therefore termed as ACRRFLN. Also, three other optimisation techniques, i.e., particle swarm optimisation (PSO), teaching learning-based optimisation (TLBO), and genetic algorithm (GA) are employed to train RFLN separately. All the models are experimented and validated on forecasting closing indices of six stock markets. Results from extensive simulations clearly reveal the outperformance of ACRRFLN over other models similarly trained. Further, results from Deibold-Mariano test supported the statistical significance of the proposed model.
Functional Programming

Functional programming is about promoting functions to first-class citizens in a computer language. This means apart from primitive and object, reference-typed fields can also directly point to functions, and function arguments can be functions themselves. In addition, a language may also allow for special functional constructs like unnamed lambda calculus functions.
Supplementary material to &amp;quot;A new discrete wavelength BUV algorithm for consistent volcanic SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; retrievals from multiple satellite missions&amp;quot;

The TOMS is a fixed-grating Ebert-Fastie monochromator with a photomultiplier tube detector that measures solar backscattered ultraviolet (BUV) radiances (I m ) at six narrow wavelengths bands (Full Width at Half Maximum band width ~1.1nm) in the near ultraviolet (UV) spectral region, as well as the incident solar irradiances (F) (Heath, et al., 1975).The ratio of radiance to irradiance provides the spectral reflectivity parameter used in the retrieval.The wavelength band centers shown in Table 2 were selected to optimize column ozone retrievals assuming that two pairs of shorter, absorbing wavelengths would be needed to cover the full dynamic range of ozone and solar zenith angles encountered globally, similar to the Dobson Spectrophotometer design.Two additional non-absorbed longer wavelengths were provided to measure the surface or cloud reflectivity (R) and its spectral dependence.TOMS scans in the cross-track direction in 3° steps from 51° on west side of nadir to 51° on the east, for a total of 35 cross-track samples.The instantaneous field-of-view (IFOV) of 3° x 3° results in a effective field-of-view (EFOV) varying from a 50 km x 50 km square FOV at nadir to a 125 km x 280 km diamond-shaped FOV at the scan extremes.The total swath width is 3000 km covering Earth's surface in 14 orbits per day.
The Feynman Path Integral for a Scalar Field

Quantum Field Theory provides a theoretical framework for understanding fields and the particles associated with them, and is the basis of particle physics and condensed matter research. This graduate level textbook provides a comprehensive introduction to quantum field theory, giving equal emphasis to operator and path integral formalisms. It covers modern research such as helicity spinors, BCFW construction and generalized unitarity cuts; as well as treating advanced topics including BRST quantization, loop equations, and finite temperature field theory. Various quantum fields are described, including scalar and fermionic fields, Abelian vector fields and Quantum ElectroDynamics (QED), and finally non-Abelian vector fields and Quantum ChromoDynamics (QCD). Applications to scattering cross sections in QED and QCD are also described. Each chapter ends with exercises and an important concepts section, allowing students to identify the key aspects of the chapter and test their understanding.
Introduction

With energy being a premium resource for economy, society, and national security, ensuring an accurate, reliable, and efficient power generation, transmission, distribution, and consumption is of prime concern in the twenty-first century. Unfortunately, the massive blackout in northeastern North America in 2003 uncovered the ease with which the electric grids could be taken down. It is, indeed, not the end of the story for the security and reliability breaches of existing electric grids. To build a fully automated, resilient, and self-healing smart grid, a series of advanced technologies including information and communications technologies (ICTs), automation, distributed control, wide area monitoring and control, smart metering, to name a few, are rapidly incorporated into the existing electric grid over the recent years. Due to lack of strong and diligent security measures in place, however, these newly introduced technologies—exposing a great number of access points to the public—have been opening up possibilities for malignant penetrations.
A Ring Oscillator Based Random Generator for Cryptography Applications

A ring oscillator based Random number generator (RNG) for Cryptography applications is presented. The paper explains about the requirements and generation of high randomness based codes, used to improve the security in data communication. The methodology used is sampling technique, adopted in the oscillator for the random number generation. To get better randomness in the output bits of RNG, a processor module based on Linear feedback shift register is used. The proposed RNG is designed with bit rate is 100kb/s, with a power consumption of 37μw. After implementation in hardware, this can be used for cryptography encryption applications to enhance the security. The system is simulated and synthesized with Xilinx ISE and the results are compared with the existing system based on randomness, power consumption, etc.
Capacity Building in Cyber Security to Make India Secure to go Cashless

Recent developments have accelerated to move in India towards a Cashless economy. The Jan Dhan Yojana (Jan Dhan Government Scheme) announced in 2014 is providing for the cost free creation of a bank account for every Indian family. Each bank account is also accompanied by a Rupay Debit card for withdrawing money deposited in the account. The currency demonetization at the end of the year 2016, with the removal of certain denomination of bank notes. The additional initiatives and service offerings are also now encouraging the use of cashless transaction via, online banking, payment cards, e-wallets, direct carrier billing and crypto currency. A study said, as India changes to a large scale economy, cyber dangers are in a new high with the amount of these incidents happening in banking methods rising in the previous five decades. The research demonstrated that demonetization has provided an impetus into e-wallet providers and cellular wallets have seen a huge growth in downloads. Additionally, cyber- risks will only grow as India will be seeing a change towards a strong economy. The kinds of cyber security events like Phishing, scanning, site intrusions and defacements, virus code and denial of service attacksmay keep growing. The joint research by Assocham and PWC stated on ATM card hackstruck on the Indian banks in October this past year, impacting approximately 3.2 million debits. The cyber strikes on Indian sites have increased almost five times before four decades.
Fine Grained Access Control on Information Retrieval with Collaborative Fusion and Active Feedback

Enterprise information retrieval is quite challenging than web based information retrieval due to retrieval from heterogeneous distributed data sources and need for higher accuracy in retrieval [17]. In our earlier work, collaborative fusion guided by active feedback is proposed for information retrieval in enterprise environment. Applying three dimensions of user similarity, user-document search history and document similarity, collaborative information fusion based retrieval was proposed in that work However the work did not consider enforcing fine grained access control on the retrieval which is very important requirement in enterprise environment. In this work, fine grained access controlled information retrieval on enterprise environment, with zero leakage assurance thorough direct or inference is proposed. Fine grained access control is ensured with help of CP-ABE. Concept masking is done with CP-ABE and unmasked when user satisfies the query access right criteria.
Introduction

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book explores a number of these important areas where technology impacts society and suggests ways that human communication can facilitate the use of that technology. It summarizes a study comparing and contrasting advice form books and journal articles on oral presentations, published in English and Japanese, with actual presentations given by nonnative English speakers. Biotechnology is arena that has provided new roles for technical and professional communicators. In “Making Connections: Teaming Up to Connect Users, Developers, and Usability Experts,” Carol Barnum and her colleagues tell the story of a collaboration of stakeholders in redesigning a product to make it more accessible to less technical users. To demonstrate how usability studies can leverage technology, seven researchers at the University of Washington analyzed the logs of remote users’ Web-browsing behaviors.
An Efficient Storage Mechanism for Non Reference Elements in Cloud Utilizing Tversky Co-Efficient, K-Means and SVM

Topical technological advances have prompted the success and popularity of the cloud. This novel hypothesis is becoming more interesting as it provides economical architectures that support storage, transmission,and intensive data computing. Though, these capable storage services pose many challenging design problems because of data control loss with cloud’s abstract nature. The aspiration behind this research is to propose a novel mechanism that could lessen the cloud storage using Tversky Co-efficient technique by means of K-mean clustering. For the cross-validation process, the SVM classification algorithm is considered. For the computation, QoS parameters, such as Formation Time and Formation Time are considered that would enlighten the efficacy of the research work.
To RFP or Not to RFP: That is the Question

There are several methodologies for selecting an integrated library system: sole source, Request for Information (RFI), and Request for Proposal (RFP), for example. Some libraries are moving away from the latter, arguing that it is time-consuming, takes too many staff resources, includes more detail than is needed, and results in a final outcome that is no different than if the system was selected in a more streamlined fashion. This article argues that the benefits of the RFP greatly outweigh other approaches. It also explores the University of Rochester’s adoption of the RFP methodology and highlights successes with the model as well as aspects that could be strengthened. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678, E-mail address: getinfo@hawortlipressinc.com]
Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces

Offline handwritten character recognition system has been a challenge for Indian scripts, especially for South Indian languages. Huge number of characters of local languages including alphabets, consonants and composite characters make the recognition system more complicated. A good recognition system for subset of Tamil script, a famous South Indian script, is proposed in this work. Variable length feature vector is extracted from the thinned character image. This extracted feature is given to a novel simple classification algorithm which works based on probability. A subset of Tamil script, 20 character classes, is considered for experiment. The samples were taken from HP Labs dataset for Tamil language and a recognition accuracy of 88.15% has been produced.
Introduction to the Minitrack on Data Analytics, Data Mining and Machine Learning for Social Media

Social media continues to influence the way we work, play, and interact with each other.We are on the other side of a U.S. presidential election with a sitting president who has clearly shown the power of utilizing social media to drive public discourse.Never before has a U.S. president communicated more directly with the public.Our online communities continue to struggle with the conflicting challenge of being free and open, allowing anyone to express their thoughts and ideas, while at the same time working to combat misinformation and deep fakes.We praise social media when it is used as a tool to rally "our side" or confirm our bias while condemning it when it is used by groups we believe are harmful to the public good.In the midst of all of this, social media scholars continue on various paths of sense-making to better understand the impact social media has on society.Finally, we note that this minitrack is broad and inclusive.We welcome papers on techniques and tools to transform unstructured data into information that can be used to better drive theory, while also accepting papers on the impact that social media has on our society.
Medical knowledge extraction scheme for cloudlet-based healthcare system to avoid malicious attacks

The medical information sharing involves information collection, information storage, and sharing of this information. Protection to medical information against malicious attacks is an important concern due to its storage on remote cloud. In current online question answering system for health-related issues, extraction of medical knowledge from the clamorous question- answers pair is a challenge. To overcome these challenges, medical knowledge extraction scheme for cloudlet based healthcare system is proposed. The proposed and developed medical knowledge extraction (MKE) scheme finds valid remedial Triples from clamorous question-answer (Q-A) pairs and evaluate the reliability along with doctor's proficiency using truth discovery method the modified number theory research unit algorithm (NTRU) and collaborative intrusion detection system (CIDS) is used to avoid and detect malicious attacks. The response time of the proposed system is 6 to 8 seconds which results in a substantial reduction in time and cost for the end-user.
Deep Learning Model for A Driver Assistance System to Increase Visibility on A Foggy Road

For many years, a lot of researches have been made to develop Advanced Driver Assistance Systems (ADAS) that are based on integrated systems.The main objective is to help drivers.Hence, keeping them safe under different driving conditions.Visibility for drivers remains the biggest problem faced on the road in an atmosphere of fog.In this paper, we examine a system that can be employed to substantially enhance visibility through using deep neural networks.Researches done recently-which are based on deep learning for eliminating image fog-have made clear that an end-to-end proposed system is such an effective model.However, it becomes a must to extend the idea to end-to-end real-time video deshazing.In this paper, we introduce a model of image dehazing.It is based on Convolutional Neural Networks (CNN) as a basis for developing the video dehazing model.As in addition, we concatenate our model with the faster RCNN for detecting objects on the road in real time.The experimental results on our image datasets shows the performance of our model with regard to Peak Signal to Noise Ratio (PSNR=19.823)and Structural Similarity (SSIM =0.8501).On the dataset of the synthesized videos, our model achieved a performance of PSNR = 21.4032 and SSIM = 0.9354.Moreover, with the concatenation of our dehazing model with Faster R-CNN (regions with convolutional neural networks), our proposed system displays desirable visual quality and a remarkable progress of the object detection achievement on blurred images with mean Average Precision (mAP) equal to 0.933 during the day and 0.804 at night.
Going back to school

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds his/her own impartial comments and places the articles in context. Findings Organizational cognitive neuroscience (OCN) can be used in institutional contexts, but direct applications for leadership and organizational development are not yet clear. Practical implications This paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Semi-supervised optimization algorithm basedon Laplacian Eigenmaps

As a member of many dimensionalityreduction algorithms, manifold learning is the hotspot ofrecent dimensionality reduction algorithm. Despite it isgood at retaining the original space structure, there is nodenying that its effect of classifying still has room forimprovement. Based on Laplacian Eigenmap, which is oneof the manifold learning algorithm, this paper committed tooptimize the algorithm combined with a semi-supervisedlearning ideas, which can improve the recognition rate.Finally, the better method of two forms is tested in thesurface electromyography system and plant leafidentification system. The experimental results show thatthis semi-supervised method does well in classifying.
An anonymous electronic voting scheme for multiple elections using a variant of the ElGamal digital signature

In this paper we present an electronic voting protocol valid for multiple elections. The work is based on a variant of the ElGamal digital signature. Our proposed method allows to run t elections using the same parameters. We use the RSA blind signature for all transactions in the voting process. Security and complexity of the presented scheme are analysed.
Multiple Classifiers for Age Prediction Against AAM and ASM

In recent years, the researchers on age prediction relied on face pictures to get more attention, due to their important applications in security control and human computer interaction. Age prediction incorporates two processes: traits elicitation and prediction of machine learning. In the aspect of face traits elicitation, accurate and robust location for the trait point is convoluted and becoming a challenging issue in age prediction. Active Shape Model (ASM) can elicit the facial shape effectively and correctly. Furthermore, as the improvement of ASM, Active Appearance Models (AAM) is proposed to elicit both shape and texture traits from facial images simultaneously. In this paper, the two models are tested and compared for their performance against 6 algorithms which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Canonical Correlation Analysis (CCA), Linear Discriminant Analysis (LDA), and Projection Twin Support Vector Machine (PTSVM). The experiments show that ASM is faster and gains more precise result than the AAM
Causal Relationship between Financial Structure and Economic Growth in Contemporary African Economy: A Case Study of Nigeria from 1990-2018

This study examined financial structure and economic growth of contemporary African economies; evidence from Nigeria.The specific objectives of this study are to investigate the effect of financial structure in bank credit to the private sector ratio (BC), market capitalization ratio (MC), liquid liability ratio (LLR), turnover ratio (TR) and value of traded share (VTS) on economic growth variable in Gross Domestic Product (GDP).The study was anchored on bank based and market-based theory.The study used secondary data obtained from World Bank Data Atlas and subjected them to Granger Causality technique to test the interaction between independent variables and the dependent variable at the 5% level of significance.The findings show that financial structure in BC, MC, LLR, TR and VTS had no significant effect on GDP in the contemporary African economies.The result further discovered that there was absence of long run relationship in the study.Thus, the study concludes that financial structure does not have significant effect on economic growth in the contemporary African economies.Hence, the study recommends that financial structure should strengthen and enhance availability of money supply to key sector of the economy thereby improving economic growth by ensuring financial deepening within the economies and providing viable economic environment for financial enhancement to boost investment activities within the Nigerian economy.
Simulation and Performance Enhancement of High Efficiency Video Coding Standard for Video Compression

In the era of modern communication, the transmission of video is the most demanded feature and that makes the bandwidth issues crucial. The only solution to fight with is the video compression techniques/ standards.The High efficiency video coding standard(H.265/ HEVC) is newly evolved standard that is popularly used. this standard is better in saving bandwidth giving more compression. This research work deals with narrates the steps of implementation, simulation with MATLAB and the results obtained. from the obtained results, the 4G technique for wireless communication has been obtained in an enhanced way from the compression perspective.