Reviewerly/exHarmony huggingface dataset:
Video compression based on Multiwavelet and Multi Stage Vector Quantization using Adaptive Diamond Refinement Search Algorithm

Due to the advances in the digital technology, multimedia processing has become the essential requirement in many applications. These applications find wide use in mobile, personal computer(PC), TV, surveillance and satellite broadcast. Also it is necessary that the video coding algorithms to be updated in order to meet the requirements of latest hardware devices. The processing speed and bandwidth are essential parameters in these applications. A good video compression standard can achieve these parameters adequately. In the proposed system, the video coding standard is implemented using the three important stages. In which the first sage uses multiwavelets to achieve good compression rate. Also it reduces the memory and bandwidth requirement. Second stage is the Multi Stage Vector Quantization(MVSQ) which reduces the complexity of searching process and the size of codebook. Third stage uses Adaptive Diamond Refinement Search(ADRS) algorithm for the motion estimation which has better performance than the Adaptive Diamond Orthogonal Search(ADOS) and Diamond Refinement Search(DRS) algorithms. The combination of multiwavelet, Multi Stage Vector Quantization(MVSQ) and Adaptive Diamond Refinement Search(ADRS) algorithm gives the high compression ratios. Preliminary results indicate that the proposed method has good performance in terms of average number of search points, PSNR values and compression rates.
How to Prepare Speakers for Virtual Events

Special Events GaloreVolume 20, Issue 11 p. 4-4 Event Programming How to Prepare Speakers for Virtual Events First published: 12 October 2020 https://doi.org/10.1002/speg.31462Read 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 onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume20, Issue11November 2020Pages 4-4 RelatedInformation
Learning an Intrinsic-Variable Preserving Manifold for Dynamic Visual Tracking

In this section, the variable preserving manifold learning (IVPML) method is first introduced.
Development and Integration of an Odia Stemmer in Dspace for Odia Search Engine

Stemmer is used for reducing inflectional or derived word to its stem. This technique involves removing the suffix or prefix affixed in a word. It can be used for information retrieval system to refine the overall execution of the retrieval process. This process is not equivalent to morphological analysis. This process only finds the stem of a word. This technique decreases the number of terms in information retrieval system. There are various techniques exists for stemming. Here a new hybrid stemmer has developed named as “Mula” for Odia Language. It is a combination of brute force and enhanced suffix strippingapproach for Odia language. The new born stemmer is both computationally inexpensive and domain independent. We have integrated this stemmer in existing Dspace for Odia text retrieval System. The results are commendable and suggest that the new stemmer can be used effectively in Odia Search Engine. The proposed stemmer also handles over-stemming and under-stemming effectively.
Contributors

This chapter aims to clarify transformation from requirement to design which is the first step of design phase in mMIS-DLC. Therefore, architecture design is explained in detail. It includes architecture components and the way to create architecture design. Furthermore, hardware and software specification is explained in order to identify the required list of hardware and software in the proposed mMIS. Therefore, the main outcomes and deliverables of requirement to design step are architecture design and specification of hardware and software. Finally, the concepts are applied for iHeart as an example to assist the readers to understand the content of this chapter better.
AUTOMATIC SEGMENTATION OF INTRAVASCULAR ULTRASOUND IMAGES BASED ON TEMPORAL TEXTURE ANALYSIS

In our study we developed a novel automatic algorithm for the analysis and delineation of lumen and external elastic membrane (EEM) boundaries using both temporal and spatial variation of IVUS data. The pre-processing steps involve the construction of Laplacian gradient image from neighboring images, and the use of discrete wavelet frame decompositions for texture computation. A smooth Lumen and EEM contour is predicted by applying radial basis functions on contour initialization. This algorithm is evaluated on large data set of multi-patient 2293 IVUS images and pitted against the manually segmented contours by medical experts. It is observed that this algorithm reliably performs contour prediction with clinically appreciated limits of average prediction error equaling 0.1254 mm and 0.0762 mm for Lumen and EEM respectively. Furthermore a custom Lumen detection algorithm for stented images is proposed and tested with average prediction error of 0.048 mm
Biologically Motivated Machine Intelligence

Biological systems routinely solve problems involving pattern recognition and feature extraction. Such problems do not appear to admit similarly routine algorithmic solutions; the power of biological systems in this regard apparently arises from nonalgorithmic dynamics. It is our intention to explore, and to develop principles of, functional characteristics of natural (biological) intelligence with a view to their realization through an appropriate automation model.
Global Citizenship Education

The aim of this article is to discuss the importance and relevance of global citizenship education. What kind of knowledge, values and skills are needed for becoming global citizen and the foremost aim is to understand the implementation of global citizenship education in the classroom. Information and communication technology has made the world a global village. Interconnectedness has brought the people of different countries very close to each other. Therefore, all persons should learn to become citizens of the world. Global citizenship education requires the knowledge and skill in the following areas: social justice, diversity, globalization and interdependence, sustainable development, peace and conflict resolution. Critical thinking, debating, minimizing inequalities and injustice, respect for each, cooperation and conflict resolution. Following values and attitude need to be developed in order to understand the intricacies of global citizenship education like identity and self esteem, empathy, belief that everyone can make a difference. Therefore, there is need of teaching global citizenship at various developmental levels. We must teach controversial issues so as to develop a positive attitude to resolve the conflict in a peaceful and non-violent way.
NEAR SUBTRACTION SEMIGROUPS

B.M. Schen([2])은 함수의 합성 “ ˚ ”과 차집합 연산 “ － ”에 대하여 닫혀있는 함수들의 집합 O에서의 대수적 구조인 subtraction semigroup (O; ˚, －)를 정의하였다. 이 구조에서 (O; ˚)는 semigroup, (O; －)는 [1]에서 정의한 subtraction algebra를 이룬다. B.M. Schen은 [2]에서 모든 subtraction semigroup은 invertible function들의 difference semigroup과 동형이라는 사실을 밝혔다. 본 논문에서는 이 subtraction semigroup의 한 일반화로써 near subtraction semigroup을 정의하고 이의 한 특수한 형태인 strong near subtraction semigroup의 개념을 정의하여 이들의 일반적인 성질과 ideal의 특성을 조사하고 이들의 응용도를 조사하고자 한다.
SISTEM PENDUKUNG KEPUTUSAN MANAJEMEN KOMPLAIN MENGGUNAKAN METODE SIMPLE MULTI-ATTRIBUTE RATING TECHNIQUE EXPLOITING RANK

During this time the complaint management at PT. X is still less than optimal, this is due to the technical area and the development of Internet network services Pay TV PT. X very broad, covering all areas of the city of Surabaya and Sidoarjo. In addition, the submission of a complaint by the customer served through telephone communication with customer service section (CS). Weaknesses in the delivery of this complaint, sometimes difficult connected telephone communication on the part of CS for busy communications, where if in some time not connected communication with CS branch in Surabaya, the direct telephone communication automatically connected to the PT. X central Jakarta. It is often a misconception for Surabaya branch some managerial Jakarta Centre to handle complaints about bad customer. There is also to convey customer complaints directly with the coming into office of PT. X Surabaya. Nature of complaints submitted by customers varied, and from the technical side of the settlement sometimes complains that no once can directly, this is because usually there are technical problems that require analysis and long turnaround time. In addition, weaknesses in the management of the current complaint are the inability to handle the complaint in accordance urgency priority level. By building a Decision Support System Using Web Based Complaint Management SMARTER method, so as to accommodate the customer complaints effectively, and to provide decision support priorities in settling the complaint in accordance with the level of difficulty and areas of disturbance disorders, as well as the creation of a report that is capable of handling complaints clicking history of early receipt of customer complaint to completion. This is one of them functioned to support the development of the policy-making process-development of product technology features PT. X Surabaya.
Application of Peer Reviewed Journal Articles for Enhancing Technological Literacy

The Environment" course was taught in spring 2012 using the peer reviewed journal articles for enhancing technological literacy of the students.At the beginning of the course the students were given a pre-intervention assignment.The students were asked to explore the beneficial and harmful results of using the technology on the environment from the view point of an industry.The course introduced the students to the application of basic scientific principles (in Physics, Chemistry, and Biology) to the environment.More specifically, the concepts of sustainability, ecology and evolution, population, climate, biodiversity and various industries such as agriculture, forests, and energy were covered in the course.The course was organized into several modules.For example, in the climate and air resources module, the students were introduced to atmospheric circulation, ocean circulation, climate and weather, air pollution and greenhouse effect and climate change.Students were not usually aware that systems of positive and negative feedbacks and their combinations affect the global warming.There were also many misconcepts among the students about what it means for the earth to get warmed.The average grade of the pre-intervention group was 67% and that of the post-intervention group was 79%, an 18% improvement over the pre-intervention.The groups were significantly different with a calculated t value of 3.3.The t-test confirmed statistical improvement at significant confidence level with an alpha value of 0.05.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI PERSEDIAAN UNTUK MENINGKATKAN PENGENDALIAN INTERNAL (STUDI KASUS PADA PT LOMAX)

Inventory of raw materials is one of important asset for companies especially for manufacturing company. Therefore, companies require a good information system and internal control for their own inventory. Without an adequate information sys-tem and internal control, the effectiveness and efficiency of the operation will be hard to achieved. Researcherstried to do research of raw material inventory information system at PT Lomax Surabaya. PT Lomax Surabaya is a manufacturing company that pro-duce machines. The research method conducted is a case study using qualitative data. Data analysis techniquesis started from the evaluation of running raw material inventory information systems, evaluation of user requirements for information systems, eval-uation of internal controls, and the computerized design of raw material inventory information system. Result of this research is the design of raw material inventory information systems that can improve not only internal control but also effectiveness and efficiency of operation of the company. A new system will be able to reduce the risk of recording errors, to improve quality of raw material inventory information and to issue an accurate and timely report.
Application Of Emerging Knowledge Discovery Methods In Engineering Education

industrial information systems, data analysis and knowledge discovery, and engineering education research
Fuzzy-neuro system for decision-making in management

This paper introduces a systematic approach for intelligent decision support system design based on a class of neural fuzzy networks built upon a general neuron model. The neural fuzzy networks can formally represent and process both the qualitative (linguistic) and quantitative information, which usually describe a complex, multi- dimensional systems or decision making processes. Presented the results of tests and a practical implementation of applications of fuzzy-neuro system for decision- making in strategic management and determination of product development strategy. Computer integrated manufacturing (CIM) provides manufacturing industry with the means to produce a variety of products efficiently. Effective planning, scheduling and control in the CIM environment depend largely on proper design of the decision sup- port system (DSS). A manufacturing system is driven by input stimuli from the mar- ket in the form of direct product demand, market conditions and feedback on production with a variety of information perspectives. The activities of this system may be broadly classified as management (including strategic planning), design, pro- duction planning and production operation. Intelligent DSS have evolved as tools that attempt to support decision-making processes in problem contexts characterized by novelty and large search spaces. Problems with these characteristics are known as unstructured problems. The managerial problem domain is composed of dynamic, temporal relationships between variables. Solving many problems concerned with modern manufacturing management is connected with processing of incomplete, in- exact information. First of all, these problems are mainly aspects of strategic man- agement: market analysis, choice of product strategy and of manufacturing system development, choice of strategy for manufacturing arrangement, and others. To solve such tasks it is necessary to apply unstructured procedures for decision making, which use experimental data, skills and human intuition. To model and process fuzzy, lin- guistic or so called qualitative information fuzzy sets theory and mathematical appa- ratus of fuzzy logic are used (1, 2, 3, 4). In such systems of decision making support, the process of obtaining explanations and inference engine operation during process- ing of fuzzy knowledge is highly complicated. This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire and process various types of knowledge (quantitative, qualitative, linguistic, fuzzy) and to solve selected unstructured problems and tasks in decision support systems. The proposed environment inte- grates techniques and methods of knowledge and decision process modeling such as artificial neural networks and fuzzy logic-based reasoning methods. In this paper possibilities are presented of an approach which combines methods based on fuzzy logic and artificial neural networks, which results in crea-
FAKTOR-FAKTOR YANG MEMPENGARUHI KINERJA SISTEM  INFORMASI AKUNTANSI PADA DEALER PT ASTRA  INTERNATIONAL Tbk. DI SURABAYA

At this time, Accounting Information Systems is needed by the company to face a tighter business competition. Presentation of an relevance, accurate and timeliness information is one of the key success to the company. A good Accounting Information Systems per-formance becomes very important to produce financial information and accounting management information that required in decision-making activity. The aim of this study was to find factors that can affect the performance of Accounting Information Systems. The object in this research is the employees of finance and operational department also other related departments which using Accounting Infor-mation Systems in Dealer PT Astra International Surabaya. Sample collection method used in this study is the convenience sampling method. Data in this study were analyzed using multiple linear regression technique analysis. The results of this study showed that personal technical Systems Information skills, top management support, the formalization of Systems Information Development and user education and training programs have significant positive effect on the performance of Accounting Information System. User participation in the system development process not positive influencing the performance of Accounting Information Systems.
Research in Cloud Computing

This paper is an introduction to the terms, characteristics, and services associated with network-based computing, commonly referred to as cloud computing. Cloud Computing is a technology that uses the network and central remote servers to maintain data and applications. Cloud computing allows consumers and businesses to use applications without installation and access their personal files at any computer with network access. This technology allows for much more efficient computing by centralizing data storage, processing and bandwidth. The primary business service models being deployed (such as software, platform, and infrastructure as a service) and common deployment models employed by service providers and users to use and maintain the cloud services (such as the private, public, community, and hybrid clouds) are discussed.
Finnish Baroque of existential semiotics: Eero Tarasti’s musical synthesis of the voluptuous dance of signs

I wrote this book review in deep appreciation of the lifetime work of Eero Tarasti-- a real artist in science and scientist in art. The tour-de-force through important ideas that the book included was mindboggling and continues to be very stimulating. Eero Tarasti is one of the very best scholars who has grown up on the Finnish granite grounds, while thriving on music and existential ideas of which the present anthology is a good example. He is a fossil in the human sciences who keeps reminding us all about our limits while using the piano instead of powerpoint to make crucial points for our understandings.
Numerical optimization

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new ``Colossal Clean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.
SwinIR: Image Restoration Using Swin Transformer

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced by up to 67%.
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Convolutional Neural Network (CNN) is one of the most significant networks in the deep learning field.Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention both of industry and academia in the past few years.The existing reviews mainly focus on the applications of CNN in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered.In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible.Besides, not only two-dimensional convolution but also one-dimensional and multi-dimensional ones are involved.First, this review starts with a brief introduction to the history of CNN.Second, we provide an overview of CNN.Third, classic and advanced CNN models are introduced, especially those key points making them reach state-of-the-art results.Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for function selection.Fifth, the applications of onedimensional, two-dimensional, and multi-dimensional convolution are covered.Finally, some open issues and promising directions for CNN are discussed to serve as guidelines for future work.