Reviewerly/exHarmony huggingface dataset:
ArchContract: using smart contracts for disposition

Disposition is one of the consequences of the appraisal archival function. Although appraisal writ large is a function that no technology could execute, disposition has been already supported by different tools. In this paper, we propose a blockchain-based application for disposition, a smart contract called ArchContract, using two different repositories. We discuss appraisal and disposition on blockchain systems, the use of smart contracts as a disposition tool and present the model of ArchContract. We conclude that blockchain and smart contracts have the potential to support some of the records management functions such as disposition.
A Real-Time Detection Drone Algorithm Based on Instance Semantic Segmentation

With the rapid development of drones, drones are widely used in various fields and bring convenience to people's production and life. However, they also bring security problems to society and the country. Especially in airports or military areas, the flight of drones can cause some problems. In order to effectively supervise the drone, this paper proposes a real-time detection drone algorithm HR-YOLACT which is based on instance semantic segmentation, and designed a new drone data set. The proposed algorithm combines the real-time instance semantic segmentation algorithm YOLACT with the deep high-resolution representation classification network HRNet. Firstly, feature maps are extracted by HRNet's backbone network. Secondly, the feature pyramid network is used to further extract image features, so that the network has better classification ability. Finally, the improved prediction head is utilized to detect the boxes of drones. In addition, this paper uses cross entropy instead of focal loss as the loss function to obtain better network training speed and quality. The experimental results show that HR-YOLACT has faster detection speed and higher detection precision than existing popular real-time object detection and real-time instance semantic segmentation algorithms.
A Survey on Effective Data Computation in Inter Cloud Environment

As the data-intensive applications are increasing at an unprecedented rate, the need of efficient data computation in the today's world emerges as the most challenging task. So in this case, the computation resources may be available on demand because of the cloud computing architecture. In this paper, an analysis based on the previous study have been discussed and analyzed on the methodological prospects. It includes the attributes like computational parameters, data handling mechanism, data security and authentication. This study also explores the mechanism for the effective data computation in terms of the inter cloud environment. It includes the environment variability between public and private clouds.
Dual Templates Siamese Tracking

In recent years, Siamese networks which is based on appearance similarity comparison strategy has attracted great attention in visual object tracking domain due to its balanced accuracy and speed. However, most Siamese networks based tracking algorithms have not considered template updates. The fixed template may cause tracking matching error and can even cause failure of object tracking. In view of this deficiency, we proposed an algorithm based on dual templates Siamese network using difference hash algorithm to determine the template update timing. First, we kept the initial frame target with stable response map score as the base template $$z_{r}$$, using the difference hash algorithm to determine the dynamic template $$z_{t}$$. We analyzed the candidate targets region and the two template matching results, meanwhile the result response maps were fused, which could ensure more accurate tracking results. The experiment results on the OTB-2013 and OTB-2015 and VOT2016 datasets showed that the proposed algorithm has achieved satisfactory result.
Abnormal Exposure Image Self-Adaptive Correction Algorithm

In the process of taking photos, there are often cases where the photo is not properly exposed: such as overexposure or underexposure, a large amount of information and details cannot be displayed on the photo in the event of an abnormally exposed photo. In order to self-adaptively correct the abnormal exposure image, an Abnormal exposure image self-adaptive correction algorithm is proposed. Firstly, by first measuring the brightness of the scene of the image and comparing it with the brightness threshold that we have measured in advance, the exposure state of the image is judged. Secondly, overexposure and underexposure correction are respectively performed according to different states, and finally the normal exposure state is synthesized. image. It is proved by experiments that the algorithm can self-adaptively judge the exposure state of the image and can perform different exposure state corrections.
Trusted Quantum Back Bone Leveraging Secure Communication

This chapter discusses the different use-cases of a trusted Quantum Back Bone (QBB) and their advantages, disadvantages and open problems. Furthermore, we try to provide insights why a QBB may also use PRNGs, seeded using quantum based random numbers, to increase the key bit generation rate.
Predictive Model of Solar Irradiance Using Artificial Intelligence

Solar power in India is growing at a tremendous pace. India's solar power capacity is 20 GW and has grown 8-fold since 2014. Assessing the solar potential in India is thus the need of the hour. The objective of this study is to make an optimized prediction model of the monthly potential of solar irradiance of the Indian Subcontinent, by utilizing hour-wise unstructured voluminous (80 million line item) satellite-based data from 609 locations for 15 years. The variables chosen are temperature, pressure, relative humidity, month, year, latitude, longitude, altitude, DHI, DNI, and GHI. Combining predictive models using combinations of SVM, ANN, and RF for factors affecting solar irradiance. This model's performance has been evaluated by its accuracy. Accuracy for DHI, DNI, GHI values on testing data evaluated through the SVM model is 95.11%, 93.25%, and 96.88%, respectively, whereas accuracy evaluated through the ANN model is 94.18%, 91.60%, and 95.90%, respectively. The achieved high prediction accuracy makes the SVM, ANN, and RF model very robust. This model with a sustainable financial model can thus be used to identify major locations to set up solar farms in the present and future and the feasibility of its establishment, wherever local meteorological data measuring facilities are not available in India. Along with the air temperature, air pressure, and humidity predictive interrelation model created to aid the irradiance model this can be used for climate predictions in the Indian sub-continental region.
Nonsmooth Optimization Based Clustering Algorithms

In this chapter, we describe the clustering algorithms based on nonsmooth optimization approaches. First, we present the modified global k-means algorithm. Then the fast modified global k-means algorithm and its modifications are discussed. The limited memory bundle clustering and the discrete gradient based clustering algorithms are also described. Finally, we present the smooth incremental clustering algorithm. The detailed descriptions of these algorithms as well as their flowcharts are given.
System for Detection of Vehicles in Multiple Video Streams in Road Infrastructure Monitoring

This article describes hardware and software architecture of system for road infrastructure monitoring. The specific emphasis is on camera based vehicle detection based on information coming from multiple sensors. Extensive overview of available visual vehicle detectors is provided and choice made for the Roadsens+ system development is explained. Presented experimental results includes quality rates for two different detection implementations and samples of positive detections as well as false positives. Achieved results allow to assess system usefulness in practical conditions.
Assessment criteria of competence formation of organizers in the educational process of the agrarian university in the field of using information and communication technology

The article defines the evaluation criteria for the formation of the competence of the organizers of the educational process at the Agrarian University in the use of information and communication technology tools. It is shown how to estimate this level. Measurable indicators of evaluation criteria are highlighted and methods for measuring these indicators are disclosed. The scale of competence levels of the organizers in the educational process at the Agrarian University in the use of information and communication technology tools is given.
Weighted N-grams CNN for Text Classification

Text categorization can solve the problem of information clutter to a large extent, and it also provides a more efficient search strategy and more effective search results for information retrieval. In recent years, Convolutional Neural Networks have been widely applied to this task. However, most existing CNN models are difficult to extract longer n-grams features for the reason as follow: the parameters of the standard CNN model will increase with the increase of the length of n-grams features because it extracts n-grams features through convolution filters of fixed window size. Meanwhile, the term weighting schemes assigning reasonable weight values to words have exhibited excellent performance in traditional bag-of-words models. Intuitively, considering the weight value of each word in n-grams features may be beneficial in text classification. In this paper, we proposed a model called weighted n-grams CNN model. It is a variant of CNN introducing a weighted n-grams layer. The parameters of the weighted n-grams layer are initialized by term weighting schemes. Only by adding fixed parameters can the model generate any length of weighted n-grams features. We compare our proposed model with other popular and latest CNN models on five datasets in text classification. The experimental results show that our proposed model exhibits comparable or even superior performance.
Scientific Communication in the World of Digital Technologies: Conditions and Perspectives Regarding the Development of Scientific Journals

Purpose. The presentation of challenges with which scientific journals and – more broadly – scientific communication, will have to face in the substantive, technological and financial sphere in the conditions of constant evolution of the digital world, dynamic development of new information systems in science (e-library, library 2.0) and new phenomena conditioning the behaviour of Internet users. Method. The views presented in the article are the result of a review of literature regarding scientific journals and empirical research, which was conducted from April to August 2018 among 132 authors representing the world of science and economic practices. Findings. The open access (OA) movement has caused significant changes in the behaviour of people of science in publishing and depositing research results. The prospect of taking over all the functions of scientific journals by scientific repositories still seems to be distant due to the lack of alternative methods for assessing the quality of scientific publications. There are doubts about the division into scientific, institutional repositories and the repositories belonging to scientific journals, which results from unclear business models of individual solutions. The phenomenon of self-publishing is stimulated by the dynamic development of research carried out by business units, skilfully analysing the resources of large data sets and successfully popularising research results in social repositories. This is new quality in the area of information exchange, which requires rapid adaptation on scientific grounds. An insufficiently implemented postulate to popularise scientific knowledge and its transfer to business practice remains an equally important challenge. Research and conclusions limitations. The results of the survey based on the respondents’ subjective assessment should be treated with caution and do not allow to draw general conclusions. The research revealed significant discrepancies in respondents’ opinions regarding the future of scientific journals and their prospects for functioning in new information systems. The highest doubts concern the quality assessment system of scientific publications and the business model of scientific repositories: the significant number of stakeholders of the scientific communication system, dispersed in various scientific, political and economic systems, further limits the possibility of formulating unambiguous decisions in this respect. Originality. The presented article formulates challenges for scientific journals whose functions are being increasingly taken over by scientific and social repositories. In contrast to the previously published works, this suggests solutions in the field of artificial intelligence, which will enable complete change in the way of publishing and validation of knowledge as well as quality control of scientific research. Type of work. Review article.
Implementations and Data Sets

In this chapter, we discuss the implementations of the incremental clustering algorithms described in previous chapters. We also provide some recommendations on the choice of parameters of the algorithms. In addition, the choice of parameters in the algorithm for finding starting cluster centers, which is an important part of all nonsmooth optimization based clustering algorithms, is discussed. Finally, data sets used in numerical experiments are described and grouped into different subclasses according to their size.
Review: Hush: Media and Sonic Self-Control by Mack Hagood

Book Review| March 01 2020 Review: Hush: Media and Sonic Self-Control by Mack Hagood Mack Hagood, Hush: Media and Sonic Self-Control. Durham, NC: Duke University Press, 2019. 288 pp. Justin Adams Burton Justin Adams Burton Rider University Email: jburton@rider.edu Justin Burton is the author of Posthuman Rap and co-editor of the Oxford Handbook of Hip Hop Music. Justin's work revolves around pop, hip hop, and dance genres primarily as they relate to race and gender. Justin is associate professor of music at Rider University. Search for other works by this author on: This Site PubMed Google Scholar Journal of Popular Music Studies (2020) 32 (1): 133–136. https://doi.org/10.1525/jpms.2020.32.1.133 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Justin Adams Burton; Review: Hush: Media and Sonic Self-Control by Mack Hagood. Journal of Popular Music Studies 1 March 2020; 32 (1): 133–136. doi: https://doi.org/10.1525/jpms.2020.32.1.133 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentJournal of Popular Music Studies Search In Hush, Mack Hagood traces the modern history of noise-producing devices that are used to combat unwanted noise. Hagood places a chapter on tinnitus at the beginning of the book to establish a listening praxis that is active—the internally produced sound of tinnitus tends to intensify or wane depending on one's external soundscape—then lays out a narrative of technological developments that use sound to mask or cancel out undesirable sound. This framing draws the reader's attention to modes of listening that position the body in an affective relationship both with its surroundings and with the media that either convey or block out the vibrations of those surroundings. At root in... © 2020 by the Regents of the University of California. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press's Reprints and Permissions web page, https://www.ucpress.edu/journals/reprints-permissions.2020 You do not currently have access to this content.
Understanding and Improving Neural Ranking Models from a Term Dependence View

Recently, neural information retrieval (NeuIR) has attracted a lot of interests, where a variety of neural models have been proposed for the core ranking problem. Beyond the continuous refresh of the state-of-the-art neural ranking performance, the community calls for more analysis and understanding of the emerging neural ranking models. In this paper, we attempt to analyze these new models from a traditional view, namely term dependence. Without loss of generality, most existing neural ranking models could be categorized into three categories with respect to their underlying assumption on query term dependence, i.e., independent models, dependent models, and hybrid models. We conduct rigorous empirical experiments over several representative models from these three categories on a benchmark dataset and a large click-through dataset. Interestingly, we find that no single type of model can achieve a consistent win over others on different search queries. An oracle model which can select the right model for each query can obtain significant performance improvement. Based on the analysis we introduce an adaptive strategy for neural ranking models. We hypothesize that the term dependence in a query could be measured through the divergence between its independent and dependent representations. We thus propose a dependence gate based on such divergence representation to softly select neural ranking models for each query accordingly. Experimental results verify the effectiveness of the adaptive strategy.
Object Class Recognition using Combination of Colour Dense SIFT and Texture descriptors

Object class recognition has recently become one of the most popular research fields. This is due to its importance in many applications such as image classification, retrieval, indexing, and searching. The main aim of object class recognition is determining how to make computers understand and identify automatically which object or scene is being displayed on the image. Despite a lot of efforts that have been made, it still considered as one of the most challenging tasks, mainly due to inter-class variations and intra-class variations like occlusion, background clutter, viewpoint changes, pose, scale and illumination. Feature extraction is one of the important steps in any object class recognition system. Different image features are proposed in the literature review to increase categorisation accuracy such as appearance, texture, shape descriptors. In this paper, we propose to combine different descriptors which are dense colour scale-invariant feature transform (dense colour SIFT) as appearance descriptors with different texture descriptors. The colour completed local binary pattern (CCLBP) and completed local ternary pattern (CLTP) are integrated with dense colour SIFT due to the importance of the texture information in the image. Using different pattern sizes to extract the CLTP and CCLBP texture descriptors will help to find dense texture information from the image. Bag of features is also used in the proposed system with each descriptor while the late fusion strategy is used in the classification stage. The proposed system achieved high recognition accuracy rate when applied in some datasets, namely SUN-397, OT4N, OT8, and Event sport datasets, which accomplished 38.9%, 95.9%, 89.02%, and 88.167%, respectively.
Design of Desktop Electronic Metal Coin Sorting Device

This paper proposes a desktop electronic metal coin sorting device design scheme, which realizes the sorting of different denomination currencies according to the physical properties of coins and increases the counterfeit currency identification function, thus improving the practicability of the design scheme. The physical model of the coin automatic sorter was made by the design scheme. The experiment shows that the coin sorter has good coin sorting and sorting and storage functions.
Exploring the Potentiality of Semantic Features for Paraphrase Detection

Paraphrase is defined as the repetition of something written or spoken using different words. In this paper, we adopt a feature engineering strategy to perform paraphrase detection at the sentence level. In particular, we explore the potentiality of semantic features, as the similarity between two semantic graphs, a distance function between sentences and the cosine similarity between embedded sentences, using them within several machine learning-based classifiers. We evaluate our approach on the ASSIN benchmark corpus and achieve $$80.5\%$$ of F-score, outperforming some other detection methods for Portuguese.
Image Enhancement Using Surveillance System for Dead Zone

This paper presents a surveillance system that is capable of transmitting its geo location and the information from real time images to a central unit in no network zone. The system is further configured for using image processing and machine learning algorithms to deduce any suspicious object from the captured image. Thereby, the system is helpful in identifying threats in all kind of weather conditions on land and in oceans. The system is very efficient, cost effective and is simple in design. Once the threat is identified, the system transmits the information to the central unit through the LoRa by using certain communication protocols. Compared to existing devices, this system has the potential to reduce causalities by enhancing navigational and observational features in the surveillance sector.
Quick Response (QR) Code Image Pattern Recognition: Time Utilization Tracking Application

Abstract - Time utilization is an essential number for firms that charge the maximization of productivity of employees’ efficiency. QR code store the identity of mobile employees/students of whereabouts. QR code recognition is a pattern identification presence in image orientation. Automating the time tracking method holds employees/students responsible for their locations, does eliminate time and error estimation, and ensures its accuracy. The goal is to develop an application using the technology of QR code for the time utilization tracking process. Some companies today still using Bundy clock or Radio Frequency Identification (RFID) for time tracking, and due to the restricted transaction and access mobility, the problems encountered are time recording, monitoring, and manual computing. The methodology is the V-model agility process in the System Analysis, Design, and Development (SADD) phases. Quantitative data analysis was resulted in a significant level and qualitative on the Reliability Test of the system functionality. Twenty-five respondents perceive significant, and five IT experts calibrated, tested, and evaluated, which resulted in a mean of 3.10 as Average. The coefficient of variation of <10 is Very Good shows the precise estimate proves that QR code provides efficiency in record management, accurate computational arithmetic, and accessibility of the mobile app.  Keywords: QR code, V-model, app, RFID, agility