Reviewerly/exHarmony huggingface dataset:
Image Transmission Performance Over UHF Channel Using Real Time Processing in LOS and NLOS Transmission

Data transmission from the transmitter to the receiver is influenced by several parameters including distance, obstacle and data size. In general, the farther the distance of the transmitter to the receiver, the longer the data will be sent. This relationship is also applied to data size parameter, where the larger the data sent, the longer the data received at the receiver. Therefore, this research aims to measure transmission performance of big size data such as image in real time processing unit applied in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) UHF radio module. Since image data processing takes a significant time, the design considered a highspeed processor. Payload, designed using Raspberry Pi Zero W processors and Raspberry Pi Camera Revl.3, has a function of capturing image data remotely. This payload will communicate with a receiver station over 3DR as Radio Transceiver Module. Receiver station set up to display the received image data. The proposed design performed that the process time of image data transmission is getting longer along with changes in data size and distance. The research results that NLOS data transmission takes longer time compared to LOS data transmission at around 18.483 seconds for 512×384 pixel image with quantization level of K=32. The test conducted to the same image showed that the data loss of NLOS transmission is 8% larger than LOS data transmission.
Public Service of Retired Personnel in Colleges and Universities in the Internet Era

At present, the most remarkable characteristic of social development are networking and informatization, which provide a new choice for retirement service in colleges and universities. Colleges and universities are important institutions undertaking the responsibility of old-age service. How to do the old-age service of retired personnel well is of great significance and has a long way to go. Based on the investigation of the life style, material needs as well as spiritual and cultural needs of retired personnel in colleges and universities, this work obtained the results, put forward the countermeasures, and gave full play to the subjective initiative of retired personnel, so as to help the retirement work office of colleges and universities improve the management style and enrich the elderly life, especially to meet the spiritual and cultural needs of retired personnel.
Algorithmic Pricing and Market Coordination – Toward a Notion of ‘Collusive Risk’

Over the past couple of years, many competition and antitrust scholars have feared the dawn of ‘algorithmic collusion’. Some have thus suggested expanding the notions of ‘collusion’ and ‘agreement’ in order to capture such coordination. Rather than using an expansive reading of ‘collusion’, the author of this article suggests an approach that works with the core and original intent of Article 101(1) TFEU: the fostering of independent conduct and prevention of market coordination. It finds this to be doctrinally undisputed and also consistent with longstanding competition policy debates, as well as an egalitarian notion of price that lays the foundation of the free market economy. On this basis, and considering given uncertainties, an operational notion of ‘collusive risk’ is put forward.
MDFN: Multi-path Dynamic Fusion Network for Face Reconstruction and Dense Face Alignment

3D face reconstruction based on single-view in the wild has been a long-standing challenging problem. In the complex and changeable unconstrained state, the traditional 3D Morphable Model (3DMM) parameters regression method lacks the ability to express local details, which is hard to reconstruct the accurate face shape. In this paper, we propose a multi-path pyramidal convolution network with the dynamic fusion of global and local information, which is able to recover richer detail 3D shapes from 2D images. Specifically, we design a multi-path network to extract global discriminative features and local detailed features, respectively. Then, we utilize the attention transformer module to enhance the network’s ability for capturing the correlation between local information. Finally, to boost the regression accuracy of the network, we dynamically fuse the global information that constrains the geometric shape of the face and the local information that enriches geometric details. Extensive experimental results on the AFLW and AFLW2000-3D datasets demonstrate that our MDFN achieves compelling performance in dense face alignment and reconstruction.
Multi-Feature Fusion via 2D and 3D Networks for Action Recognition

research-article Multi-Feature Fusion via 2D and 3D Networks for Action Recognition Share on Authors: Zijie Mo Macau University of Science and Technology, China Macau University of Science and Technology, ChinaSearch about this author , Sio-Long Lo Macau University of Science and Technology, China Macau University of Science and Technology, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 87–93https://doi.org/10.1145/3495018.3495034Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Impact of English-Language and French-Language Students and Faculty Populations on the Use of E-Monographs in a Bilingual Canadian University

This article reports the results of a quantitative and systematic analysis comparing usage rates between English-language and French-language e-monograph titles at Laurentian University, Canada. Full-text viewings were compared to faculty and student population sizes. The majority of e-monograph usage was recorded by the English-language e-monograph collection, with English-language e-monograph viewings accounting for 91.2% of the total number of viewings. Both English- and French-language doctoral students shared very strong linear relationships with their respective e-monograph collection. The English-language e-monograph collection demonstrated a greater level of relative use by English-language student and faculty populations. English- and French-language e-monograph collections are used at different rates by their respective language-based populations.
Task-Oriented Internet Assisted English Teaching and Learning in Colleges

Task-Oriented Internet Assisted English Teaching and Learning (TIAETL) is a new English teaching and learning model which integrates the Internet-assisted and task-oriented teaching. This article analyzed the worldwide tendency of English teaching and prerequisites for TIAETL in colleges. The TIAETL has the following advantages: student-centeredness, convenience and flexibility, communicativeness and applicability, informativeness, comprehensiveness and motivation. In the TIAETL, human factor, technology and facility factor, and internet activity determine the success of TIAETL. The commonly-used internet activities include browsing the Internet, emailing, chatting, blogging, and so on. In the form of accomplishing Net tasks, students develop their English listening, speaking, reading, writing, translation and raise crosscultural awareness through the Internet. Finally the article expounded the implementation of TIAETL in detail from the aspects of preparation, learning resources, task, time, check and evaluation. It's expected that this article arouses the interest in the researches on and practice of TIAETL in foreign language education and thus promotes English teaching and learning.
Text Analytics

The main focus of this textbook thus far has been the analysis of numerical data. Text analytics, introduced in this chapter, concerns itself with understanding and examining data in word formats, which tend to be more unstructured and therefore more complex. Text analytics uses tools such as those embedded in R in order to extract meaning from large amounts of word-based data. Two methods are described in this chapter: bag-of-words and natural language processing (NLP). This chapter is focused on the bag-of-words approach. The bag-of-words approach does not attribute meaning to the sequence of words. Its applications include clustering or segmentation of documents and sentiment analysis. Natural language processing uses the order and “type” of words to infer the meaning. Hence, NLP deals more with issues such as parts of speech.
Fundamental Concepts in Video

In this chapter, we introduce the principal concepts needed to understand video. Here we consider the following aspects of video and how they impact multimedia applications: (1) analog video, (2) digital video, (3) video display interfaces, (4) \(360^\circ \) video, (5) 3D video and TV, and (6) video quality assessment (VQA). Knowledge of video must include historically important standards in analog video. Digital video has different ideas emphasized, and we go on to look at HDTV as well as ultra-high definition TV. After a discussion of video display interfaces, we enter into the exciting field of \(360^\circ \) video which is vital for multimedia applications such as virtual reality. We then introduce 3D video and TV where issues of both human vision and computer vision are discussed. Finally, several essential aspects of video quality assessment are addressed that include the classical metric of PSNR, BD-rate (short for Bjontegaard’s delta-rate), and SSIM.
Is It New Poetry or the Emperor’s New Clothes? —An Overall Investigation of Three Poetry Collections and a Man-machine Poetry Competition of Artificial Intelligence Poet Xiaobing

"Microsoft Xiaobing" is an "artificial intelligence framework girl" created by the Microsoft R & D team to explore the "emotional computing framework". The poet Xiaobing is just one of her versatile identities. The poet Xiaobing was declared to have gained "emotion" and "creative power", and published three poetry collections, and she also won a grand victory in the "man-machine poetry competition". Looking deeply and comprehensively at these three poetry collections, they seem to be the three evolutions of the artificial intelligence poet on the way to obtain "emotion" and "creativity". In fact, it was an unsuccessful attempt followed by two concessions. The "algorithm" in Xiaobing’s system can’t calculate the poetry of New Poetry, Xiaobing’s "deep learning ability" can’t help "Plain People" become real poets, and the big data of modern poetry can’t stimulate Xiaobing’s creativity. Xiaobing now stays at the stage of word selection and sentence making, and can’t do anything about the layout of poetry. In the future, as long as Xiaobing cannot integrate into human life as an emotional individual, she will never be able to write creative New Poetry. Xiaobing can still write poems all the time, but they are only poems in the robot world. For humans, they are just "the emperor’s new clothes".
Drawing Dreams

Dreams have been responsible for some major creative and scientific discoveries in the course of human history. Recording dreams in the form of images is an interesting and meaningful thing. Our task is to generate images based on the description of dreams. Recently, there has been exciting progress in generating images from descriptions of birds and flowers, but the dream scene is more fantasy than the real scene. The challenge to reproduce complex sentences with many objects and relationships remain. To truthfully reappear the dream scene, we process sentences into scene graphs that are a powerful structured representation for both images and language; then using a graph convolution network to obtain layout information, combining the layout information and a single feedforward network to generate the image. Subsequently, we apply Cycle-Consistent Adversarial Net (CycleGAN) to change the image into different styles according to the mood of users when dreaming. According to the experimental results, our method can generate complex and diverse dreams.
Reshaping Academic Librarianship in the Context of Twenty-First Century Scholarly Communications and Information Fluency: A Case Study of the Scholarly Communications Management Team at Imperial College London Library Services

Academic librarianship has had a traditional role encompassing various specialisms: subject support, training, journal subscription management, collection development and user engagement front of house. While many of these roles remain, they should be re-evaluated. New competencies and skillsets in teaching, publishing, bibliometrics, impact, data management, online communication and ethics are becoming the subject of considerable debate and concern within the profession. We cannot deliver existing and new services without acknowledging that scholarly communication support is the future of academic librarianship, and we must move towards effecting a shift from liaison as information literacy and subject expert to relationship management and understanding the impact of scholarly communication developments on both students and academics. This case study will examine how Imperial College London Library Services is striving to meet this challenge, through the creation of a Scholarly Communications Management team.
ARCHIVER - Data archiving and preservation for research environments

Over the last decades, several data preservation efforts have been undertaken by the HEP community, as experiments are not repeatable and consequently their data considered unique. ARCHIVER is a European Commission (EC) co-funded Horizon 2020 pre-commercial procurement project procuring R&amp;D combining multiple ICT technologies including data-intensive scalability, network, service interoperability and business models, in a hybrid cloud environment. The results will provide the European Open Science Cloud (EOSC) with archival and preservation services covering the full research lifecycle. The services are co-designed in partnership with four research organisations (CERN, DESY, EMBL-EBI and PIC/IFAE) deploying use cases from Astrophysics, HEP, Life Sciences and Photon-Neutron Sciences creating an innovation ecosystem for specialist data archiving and preservation companies willing to introduce new services capable of supporting the expanding needs of research. The HEP use cases being deployed include the CERN Opendata portal, preserving a second copy of the completed BaBar experiment and the CERN Digital Memory digitising CERN’s multimedia archive of the 20th century. In parallel, ARCHIVER has established an Early Adopter programme whereby additional use cases can be incorporated at each of the project phases thereby expanding services to multiple research domains and countries.
Geospatial Digital Rights Management

This Chapter introduces the need for general Digital Rights Management (DRM) requirements. Further, it intertwines DRM with its spatial counterpart, Geospatial DRM (GeoDRM). However, unlike DRM, GeoDRM is far much complicated due to issues such as the development of Web Mapping technology among other issues. The Chapter discusses the ability of GeoDRM to mitigate transgression of Intellectual Property Rights (IPR). Highlighting economical and environmental wellbeing and other benefits of Spatial Data Infrastructure (SDI) geared towards global sustainable developments, the Chapter focuses on challenges of National Spatial Data Infrastructures (NSDIs) and Regional SDIs and the need to harmonize their standards for the upward mobility of global SDI (GSDI). Emphasizing the undisputed need for Local, Regional and Global Spatial Data Infrastructures (SDIs), in the presence of various Geo-communities and different GeoDRM models, the Chapter concludes that capacity building need to be urgently but carefully harnessed across all levels in order to develop cohesive GeoDRM policies.
iPhone Forensics

Smartphone usage has increased in the recent past and has become an extension of the personal computer, so has the complexity of forensic investigation. Vital information on these devices makes them more critical especially when it is part of investigative evidences. The challenge here is the extraction of data, especially when the phone is logically or physically damaged. Chip-off is a niche technique, involving removal of Flash Memory chip with due sophistication, this then is subjected to direct extraction and analysis. Apple iPhones are robust and well locked; the study performed chip-off on model A1203 that revealed vital forensic evidences.
Web Data Classification using Support Vector Machine Back Propagation Neural Network

These days, the development of World Wide Web has surpassed a lot with extra desires. Extraordinary arrangement of content reports, transmission records and pictures were reachable inside the web it's as yet expanding in its structures. Information handling is that the style of removing information's realistic inside the web. Web mining could be a piece of information preparing that identifies with differed examination networks like data recovery, bearing frameworks and artificial insight. The data's in these structures are very much organized from the beginning. This web mining receives a great deal of the date mining procedures to discover most likely supportive data from web substance. The ideas of web mining with its classifications were examined. The paper chiefly focused on the web Content mining undertakings along the edge of its procedures and calculations. In this paper we proposed AI calculation based order .SVM_BPM calculation grouped the web content information and thought about existing calculations our proposed arrangement calculation is high effective and less time calculation.
A Dynamic and Scalable Decision Tree Based Mining of Educational Data

The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.
Classification of soil quality using K-Nearest Neighbors methods

Abstract The damage or deterioration of the material due to reaction with the environment is called corrosion. The corrosive of soil can cause natural disasters such as landslides. Soil corrosivity is a serious problem in industrial and infrastructure activities. Soil corrosivity is also a problem at PT. IPMOMI, because of the large number of metal pipes that are planted underground which functions to drain fluid in the form of seawater as the main material for steam production. PT. IPMOMI wants to install metal pipes underground, Therefore PT. IPMOMI requires information about the status of soil corrosivity at that location. To overcome soil corrosivity, PT. IMOMI will install cathodic protection on the soil with high corrosivity. One of the efforts to deal with this problem is by mapping the subsurface corrosion zone. Before mapping the corrosion zone, it is necessary to know the level of soil corrosivity whether it is very high, high, medium, low, or very low. So that to determine the level of soil corrosivity whether very high, high, medium, low, or very low, a classification method is needed. In this study, the classification method used is the nonparamteric classification method, namely K Nearest Neigbors. The results of classification accuracy were 83.3% and variable chargebility had a higher contribution than the depth level in the classification model.
Machine Learning Model for IRIS Flower Classification using Tensor Flow and PyTorch

Machine learning is a subfield of artificial intelligence, which is learning algorithms to make decision-based on data and try to behave like a human being. Classification is one of the most fundamental concepts in machine learning. It is a process of recognizing, understanding, and grouping ideas and objects into pre-set categories or sub-populations. Using precategorized training datasets, machine learning concept use variety of algorithms to classify the future datasets into categories. Classification algorithms use input training data in machine learning to predict the subsequent data that fall into one of the predetermined categories. To improve the classification accuracy design of neural network is regarded as effective model to obtain better accuracy. However, design of neural network is usually consider scaling layer, perceptron layers and probabilistic layer. In this paper, an enhanced model selection can be evaluated with training and testing strategy. Further, the classification accuracy can be predicted. Finally by using two popular machine learning frameworks: PyTorch and Tensor Flow the prediction of classification accuracy is compared. Results demonstrate that the proposed method can predict with more accuracy. After the deployment of our machine learning model the performance of the model has been evaluated with the help of iris data set.
Seismic Pitfalls

All seismic anomalies are not related to geology. Spurious anomalies and misinterpretation of seismic images often lead to drilling results, substantially different from that predicted, leading to exploration setbacks. This is commonly referred as ‘seismic pitfalls’. Seismic pitfalls can originate due to inadequacies in data acquisition, processing, and interpretation of subsurface geology. Pitfalls can also be caused due to natural fetters such as technological system limitations such as of seismic wave propagation, time-domain recording and complex geological subsurface in nature. Awkwardly, pitfalls are also at times caused by misinterpretation of data due to deficient workflow, personal bias and eagerness for quick-fix solutions given by the work-station. While seismic successes are widely publicized, failures for some reason, are scarcely reported. This chapter elaborates the various sources of seismic pitfalls and highlights some of the failures with case examples. Pitfalls, however, will continue to be an upshot of seismic prediction, though can be mitigated to a large extent by synergistic interpretation of multi-set data by experienced and skilled interpreter.
GIS-Based Decision Support System for Village Level

Decision support system (DSS) plays a vital role especially in rural areas to develop rural sector for sustainable development and socio-economic uplifting of the country. To make appropriate decisions and to develop village economy, decision support system is useful for the mandal revenue officer, collector, Surpanch, and different administrators. It deals both spatial and non-spatial data at village level and comprises various ancillary information including mandal maps and village-wise information of Anantapur and Kadapa districts of Andhra Pradesh such as number of houses, male and female, SC/ST/OBC/general (or) OC population, literate and illiterate population, total working and non-working population. Datasets are collected from district collector office, mandal revenue officer (MRO) and inserted in GIS database. This chapter makes an attempt to build features of various decisions at Anantapur and Kadapa districts by integrating various layers of information at village level.