Reviewerly/exHarmony huggingface dataset:
A Question of Intimacy: Miniaturization and Figurines

The first chapter presents the complexities of miniaturization theory and the book’s intervention within that discourse, which is to challenge the notion that all tiny objects are straightforwardly and equally “intimate.” User desire for sensory engagements necessitated spatial proximity with figurines; yet, rather than invite the user completely into a private miniature world, Hellenistic Babylonian figurines made continual reference to the real-scale world – thus allowing community social mores to permeate private spaces and interactions.
Identifying Data Analysis Methodology

During a trip it is not unusual for a traveler to consider departing from their original travel plan in a serendipitous way. Depending on the way you travel, you may have some flexibility to make adjustments to the original travel plan.
Bi-Adversarial Discrepancy Minimization for Unsupervised Domain Adaptation on 3D Point Cloud

Domain Adaptation (DA) has brought significant progress to a wide range of computer vision tasks. However, there are few methods that explores domain adaptation on 3D point cloud data, which plays crucial role in 3D recognition tasks (i.e., autonomous driving and robotics). In a nutshell, the unique challenges of point cloud data consist of abundant spatial global and local geometric information, complicated data distribution, and more ambiguous margins. In this paper, we introduce a novel Bi-Adversarial Discrepancy Minimization network (BADM), which aligns the cross-domain point cloud data by adopting two adversarial learning units with respect to global representations. Based on PointNet, we also utilize local alignment to reduce the discrepancy of local structures. In addition, our BADM method helps define more accurate margin and decision bounds. For this 3D point cloud DA scenario, we use PointDA-10 as the benchmark, which is extracted from ModelNet, ShapeNet, and ScanNet for cross-domain 3D object classification (10 mutual classes). Extensive experiments demonstrate that our approach achieves state-of-the-art performance, outperforming other 3D DA approaches.
Research on Laser Measurement of 3D curved Glass Contour based on computer Vision

In the mobile phone glass industry, curved glass is widely used. Whether the curved glass is measured with high efficiency and precision and whether the curved edge profile error can be evaluated directly affects the quality and service cycle of glass products. In view of the difficulty of accurate measurement and evaluation of 3D mobile phones curved glass profile, a visual measurement method of 3D mobile phones curved glass profile error based on laser scanning and shape template matching is proposed in this paper. Accurate measurement of mobile phone curved glass greatly improves the fit between curved glass and mobile phone and the efficiency of production. Therefore, this paper proposes a method for evaluating the contour degree of curved glass on mobile phones based on Open Cascade and OpenCV. This method uses the lasers and camera equipment to extract the profile outline of the curved glass, then extract the profile outline of the mobile phone curved glass model that has been drawn in 3D software. The extracted contour lines are matched by methods such as translation and rotation, and finally the distance between the corresponding points is calculated as the contour error. Experimental results show that the method is able to accurately measure the profile error and greatly reduce the measurement instability and have good robustness.
Solar radiation prediction with deep learning and data augmentation

In this work a model for the prediction of daily solar radiation time series is proposed, this model is based on recurrent neural networks (RNN) and data augmentation. The RNN used is Long Short-Term Memory (LSTM), and the data augmentation technique is based on time-warping and jittering. It is experimented with data of 6 years,5 years for training and 1 year for testing. The results show that the proposal model with data augmentation (DA+LSTM) considerably outperforms an LSTM model without data augmentation, as well as other state-of-the-art models with which it was compared.
Modern data science with R. Benjamin S.Baumer, Daniel T.KaplanNicholas J.Horton(2021). CRC Press, Boca Raton. 631 pages, ISBN: 9780367191498

Biometrical JournalEarly View BOOK REVIEW Modern data science with R. Baumer, Benjamin S., Kaplan, Daniel T. Horton, Nicholas J. (2021). CRC Press, Boca Raton. 631 pages, ISBN: 9780367191498 Annika Hoyer, Corresponding Author annika.hoyer@stat.uni-muenchen.de orcid.org/0000-0002-0241-9951 Department of Statistics, Ludwig-Maximilians University Munich, Munich, Germany Correspondence Annika Hoyer, Ludwig-Maximilians University Munich, Department of Statistics, Munich, Germany. Email: annika.hoyer@stat.uni-muenchen.deSearch for more papers by this author Annika Hoyer, Corresponding Author annika.hoyer@stat.uni-muenchen.de orcid.org/0000-0002-0241-9951 Department of Statistics, Ludwig-Maximilians University Munich, Munich, Germany Correspondence Annika Hoyer, Ludwig-Maximilians University Munich, Department of Statistics, Munich, Germany. Email: annika.hoyer@stat.uni-muenchen.deSearch for more papers by this author First published: 13 September 2021 https://doi.org/10.1002/bimj.202100258Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
A Workflow-Based Support for the Automatic Creation and Selection of Energy-Efficient Task-Schedules on DVFS Processors

The performance of a task-based program depends strongly on the schedule and the mapping of the tasks to execution units. When the energy consumption is considered additionally, the selection of an advantageous schedule is time-consuming since many options have to be taken into account. The exploitation of frequency scaling increases the number of possible schedules even further. This article proposes a software framework which supports the selection of energy-efficient schedules for task-based programs by data analysis methods for performance data. The framework contains several components for the data acquisition and data management of performance data as well as components for analyzing the data and selecting a schedule based on the data. The software architecture of the framework is presented and the implementation is described. A workflow which selects a schedule for a given set of tasks on DVFS processors is presented. As example a set of task from the SPEC CPU 2017 benchmark is considered and the performance data for the resulting schedules is presented.
Preliminary Analysis of Interleaving PN-Sequences

Some pseudorandom sequences with good crytographic features can be obtained from the interleaving of other families of sequences with unsuitable properties. PN-sequences obtained from maximum-length Linear Feedback Shift Registers exhibit good statistical aspects, such as balancedness, large period, adequate distribution of 0s and 1s and excellent autocorrelation, although their linearity makes them vulnerable against cryptographic attacks. In this work, we present a preliminary analysis on the random features of the interleaving of shifted versions of a PN-sequence. The application of statistical and graphic tests and their corresponding results complete the work.
Penggunaan Metode System Development Life Cycle ( SDLC) dalam Analasis dan Perancangan Sistem Informasi Penerimaan Kas Sekolah

Implementation of Accounting Information System in educational institutions like school is important to do. With information system, delivery information accounting at school goes well on reach goals that determined before. School cash receipts with information accounting system should be support with information system for an effective administration process. Purpose of this research is give the solution about information system of school cash receipts to handle the problem in the system that has been applied. A plan with MYSQL and PHP application system passed through by DBMS (Database Management System),used for creating the management with accounting information system. With SDLC (System Development Life Cycle) method does for program school accounting of information system. Before used SDLC method,school used manual system that cause much trouble, one of them is data dissonance existence with cash receipts evidence. After the implementation of the SDLC method and a complete DBMS design can make it easier for programmers to implement into programming languages, can reduce errors and speed up work.
DNN based phrase boundary detection using knowledge-based features and feature representations from CNN

Automatic phrase boundary detection could be useful in applications, including computer-assisted pronunciation tutoring, spoken language understanding, and automatic speech recognition. In this work, we consider the problem of phrase boundary detection on English utterances spoken by native American speakers. Most of the existing works on boundary detection use either knowledge-based features or representations learnt from a convolutional neural network (CNN) based architecture, considering word segments. However, we hypothesize that combining knowledge-based features and learned representations could improve the boundary detection task's performance. For this, we consider a fusion-based model considering deep neural network (DNN) and CNN, where CNNs are used for learning representations and DNN is used to combine knowledge-based features and learned representations. Further, unlike existing data-driven methods, we consider two CNNs for learning representation, one for word segments and another for word-final syllable segments. Experiments on Boston University radio news and Switchboard corpora show the benefit of the proposed fusion-based approach compared to a baseline using knowledge-based features only and another baseline using feature representations from CNN only.
Data Comparison and Study of Behavioural Pattern to Reduce the Latency Problem in Mobile Applications and Operating System

As the range of telecommunications applications is increasing unprecedently, mobile phones will most likely enhance the quality of communication among human lives. On the other hand, this constitutes to extreme social implications. The mobile phones are further progressed to become as instruments for leveraging instant correspondence and enhanced cooperation. Furthermore, it has been innovatively utilized for business purposes and among youngsters. Meanwhile, the number of sensors integrated in mobile phones and the applications running on them are also increased considerably. In this paper, a detailed comparative study and analysis have been performed on behavioural patterns in order to reduce the latency-related challenges in operating system and mobile applications.
Framework to Analyze Twitter Data (Tweets) Using Deep Learning and NLP Techniques

Background. Online platforms have now fully emerged as a platform to share their views on various subjects and issues with various viewers. Purpose. Though we have different ways of expressing ourselves, the most common is text. The dataset has texts, selected_text, punctuations, emoticons, etc. Methods. The paper targets to analyze sentiments from Twitter posts. We use the approach to train our model for detecting sentiment from tweets. Some interesting novelties in this paper are (i) including a hybrid approach for analyzing the sentiment of the tweets, (ii) introducing Jaccard similarity, and (iii) aiming to find which neural network (RNN or CNN) algorithm works better with our dataset. Results. With overall analysis, Bi-Directional LSTM performs better out of the three, and hence, the accuracy of the hybrid model enhances, indirectly. Conclusion. Finally, a concluding scope in which RNN and our hybrid approach perform better and are approximately 4–5% improved than the past models. The future implications related to the work are also given to improve the quality of the model.
An Experimental Analysis of the Effects of Different Hardware Setups on Stereo Camera Systems

For many application areas such as autonomous navigation, the ability to accurately perceive the environment is essential. For this purpose, a wide variety of well-researched sensor systems are available that can be used to detect obstacles or navigation targets. Stereo cameras have emerged as a very versatile sensing technology in this regard due to their low hardware cost and high fidelity. Consequently, much work has been done to integrate them into mobile robots. However, the existing literature focuses on presenting the concepts and algorithms used to implement the desired robot functions on top of a given camera setup. As a result, the rationale and impact of choosing this camera setup are usually neither discussed nor described. Thus, when designing the stereo camera system for a mobile robot, there is not much general guidance beyond isolated setups that worked for a specific robot. To close the gap, this paper studies the impact of the physical setup of a stereo camera system in indoor environments. To do this, we present the results of an experimental analysis in which we use a given software setup to estimate the distance to an object while systematically changing the camera setup. Thereby, we vary the three main parameters of the physical camera setup, namely the angle and distance between the cameras as well as the field of view and a rather soft parameter, the resolution. Based on the results, we derive several guidelines on how to choose the parameters for an application.
Evaluation of Compression Algorithms for 3D Pavement Image Data

A standard interchangeable data format was recently developed through the Transportation Pooled-Fund Study TPF-5(299) to meet different requirements and needs for two- and three-dimensional (2D/3D) pavement image data storage and exchange. Considering pavement images occupy large amounts of storage space, efficient compression algorithms for both 8-bit 2D and higher-bit 3D images become one of the key components for a successful implementation of the standard. Highway agencies and industry vendors exclusively use the Joint Photographic Experts Group (JPEG) or its successor JPEG 2000 standard for 2D image compression, while the compression of 3D 16-bit images remains to be proprietary. The goal of this paper is to evaluate suitable algorithms for compressing 3D pavement images. An overview of the state-of-the-art pavement image data collection technologies was performed, and the relevant image data formats were identified. Subsequently, five compression algorithms suitable for 16-bit image data were selected based on results from a comprehensive survey of the TPF-5(299) technical community. The compressions of several representative field images were implemented and evaluated in terms of the computation efficiency and retaining of image quality. The expected benefits of this study include facilitating standard and efficient pavement image data collection and accelerating the development of consistent pavement condition evaluation.
Decentralization using Blockchain Health Records Management

Blockchain has been the hype after the increase in mining bitcoin which was due to publicly accessible records, cryptographically secured from dangers of tampering. However many new innovations have been developed such as record keeping, Securities and Smart Contracts. Record keeping offers new forms of record keeping enabling services such as storing of data, maintenance of data and control of data. The essence of this Paper is to build a system using theory of blockchain to decentralize Healthcare and in doing so the focus of the paper has drifted into areas which is pros and cons of traditional technology and why blockchain is a more feasible and secure technology. This paper intends to identify why the use of blockchain should be encouraged and used to improve the productivity and minimize the risks in healthcare industry. In a blockchain each block has its own digital signature. This contains the signature of the previous block and its own digital signature. "Hash" is the technical term given to this "DIGITAL SIGNATURE". In order to achieve this Hyperledger Fabric has been used, a ledger system under Hyperledger umbrella Paper by the Linux Foundation. Two API’s were created to handle "DOCTOR" and "PATIENT", which was tested with POSTMAN before connection with front-end development was established. POSTMAN was used as the back-end testing between the API to check if correct data was accepted into the system. The front-end was developed using AngularJS for a more updated and user-friendly environment. With the two developments completed the whole system was hosted on a VPS server for public accessibility thus completing the system.
Research on the New Hybrid Blended Learning in the Post-epidemic Era Based on Blue Ink Cloud Class : —Take International Students Majoring in Software in Vocational University as Example

Affected by the COVID-19, some international students could not return to school on time. The blended teaching method, which combines online and offline advantages with teachers as the guidance and students as the main body, was restricted to a certain extent. This paper focuses on students majoring in software in vocational colleges and explores the new hybrid teaching methods in the post-epidemic era. The practice is oriented by talent cultivation program, based on course standards, using cloud teaching management platform --"Blue Ink Cloud Class" as the main teaching platform, JIRA as the course project management tool, WeChat group as the main communication channel, from the course progress and project progress, the teaching evaluation is carried out from the multi-dimensional aspects such as the achievement of course objectives and project requirements and students’ satisfaction. This teaching model can effectively guide students, fully stimulate the enthusiasm of students, overcome the impact of the epidemic in time and achieve the desired effect.
Advanced Mel-GAN for Speech Synthesis by Improving Receptive Field for Speech Generation

Speech Synthesis is nothing but generation of multiple speeches, if input speeches are provided from databases to speech synthesis technique; it gives multiple speeches of same speech for which input is provided. While considering GAN, Mel-GAN gives best accuracy as compared with Wave-GAN, Wave-Net. Still there are some drawbacks are there for Mel-GAN. Therefore, this paper proposes advanced or modified Mel-GAN, which can give best quality of speech as compared to original Mel-GAN. Two Modifications are made like first we increase receptive field for speech generation. Second is instead of taking feature matching loss, we substitute Spectral Loss for Training (STFT) to find out difference between real and fake data. Here Mel spectrogram takes inputs and generates sub Band signals and at last combined  back to generate full complete band signals. The proposed Advanced Mel-GAN has achieved Minimum Optimum Score as 4.34.
Journal Evaluation Methods of Indexing Agencies: A Comprehensive Study

Indexing is considered as a mark of Journal Quality which in turn will reflect on the Authors research citations in the future.These critical affairs have called in for understanding the basics of Journal Indexing. This paper is an attempt to mainly analyze the Indexing evaluation method of the various indexing agencies. This work will facilitate in providing clarity to the Researchers as to what are the significant areas to concentrate while indexing your own article.This work is from the point of view of the agencies and Authors should have a deep understanding of the process so as to produce relevant research articles.
Diagramming Imprecise and Incomplete Temporal Information

Incomplete and imprecise temporal data is abundant in various branches of science and technology as well as everyday life (e.g., “\(A\) began after 1066 but before 1069 and ended after 1245”, “\(B\) took place no later than 156 BC”). While point-circles and lines/bars have been traditionally used to depict precise temporal points and intervals, it is unclear how imprecise and incomplete temporal data can be effectively visualized or even represented. This paper suggests an intuitive diagrammatic notation for visualizing both imprecise and incomplete temporal information. It suggests using traditional whiskers with edges to depict temporal imprecision and whiskers without edges to depict incomplete temporal entities. This notation can be easily incorporated into linear temporal visualizations, such as historical timelines, Gantt charts, and timetables, to identify gaps in temporal information. The paper lays down the diagrammatic elements of the notation and illustrates their applicability to all standard relations between temporal entities. It also shows how these elements can be combined to produce complex timelines. Some possible future directions are also outlined.
A fast and secure MP3 steganographic scheme with multi-domain

The problem of single domain embedding and encoder dependence exists in almost all adaptive MP3 steganographic schemes, leading to relatively concentrated modification, limited embedding capacity, long execution time, and re-compression. Therefore, a fast and secure MP3 steganographic scheme with multi-domain is proposed in this paper. First, to enlarge the embedding capacity and spread the modifications, we extract the linbits and sign bits of quantized modified discrete cosine transform (QMDCT) coefficients to combine multi-domain. Second, a distortion function is designed based on the statistical properties of the inter and intra frames for better security. The message is embedded into the multi-domains by incorporating the syndrome trellis coding (STC) with the distortion function. Finally, the embedding method is able to get rid of the MP3 encoder and further avoid the problems of re-compression. Experiments illustrate that our scheme achieves a significant improvement in imperceptibility and statistical undetectability. In addition, the execution time is decreased by 63.6% ∼ 76.7%, and the embedding capacity is increased by at least 47.4%.
Certificateless Peer-to-Peer Key Agreement Protocol for the Perception Layer of Internet of Things

Due to the computing capability limitation of the Internet of things devices in the perception layer, the traditional security solutions are difficult to be used directly. How to design a new lightweight, secure and reliable protocol suitable for the Internet of Things application environment, and realize the secure transmission of information among many sensing checkpoints is an urgent problem to be solved. In this paper, we propose a decentralized lightweight authentication key protocol based on the combination of public key and trusted computing technology, which is used to establish secure communication between nodes in the perception layer. The various attacks that the protocol may suffer are analyzed, and the formal analysis method is used to verify the security of the protocol. To verify the validity of the protocol, the computation and communication cost of the protocol are compared with the existing key protocols. And the results show that the protocol achieved the promised performance.