Reviewerly/exHarmony huggingface dataset:
Liv2eat: A Cross Platform Application to Digitalize the College Canteen Using Firebase

With the exponential advancements in technology each year, a plethora of applications have been developed ranging from the most basic to the most advanced use cases. The amelioration in the smartphone industry has proven to be a major factor in the rapid development of mobile applications. The time and cost required to build apps increases due to the presence of multiple platforms, resulting in different programming for each. Hence, to mitigate these challenges, the solution was found in hybrid applications. To join this race of the digital world, a cross-platform application was developed for college canteen, which administered the users to place orders efficiently through a mobile application. Concurrently, this expunged the canteen problem of crowd management by notifying the customers after order completion, and assisted the staff to become dexterous with the use of this technology, thereby reducing manual labour. This paper discusses the technologies used, comparative studies and results obtained after the development of the prototype of the proposed system.
R, Language for Data Analytics

It has been 23 years since the initial version of R was launched. In this duration R has been modified several times, and now it has evolved as a giant in the field of data analysis. Nowadays R is one of the best known tool for data mining, statistics and machine learning. Today R is enjoying a vast community that provides quick response and support. With over 10000 packages available to download as per our requirement, R appears as complete solution for all data science related task .In this paper we have discussed brief history of R project. We have also described the present status of R and distinguish features of R. We have tried to explain why R is the first choice for data analysis by comparing it with other languages available for data science. We have also discussed its limitations and solutions to deal with these. This paper will be beneficial for researchers to gain an insight of R, who are going to work on data analysis related project.
Comparison of representation switching number and achieved bit-rate in DASH algorithms

MPEG Dynamic Adaptive Streaming over Hypertext Transfer Protocol (MPEG DASH) is adaptive streaming standard developed to unify previously developed adaptive streaming technologies. Over the years various MPEG DASH based adaptive streaming algorithms have been developed, mainly hybrid algorithms that take into account the buffer occupancy, available bandwidth and other parameters like video segment size. Adaptive streaming algorithms are used to determine the next video representation in order to ensure the best possible quality of experience in varying network conditions. They must balance well between the initial video delay and the number and depth of representation switching, as well as avoid video stalling events. This paper compares three different algorithms: Rate Adaptation Algorithm (LIU), Segment-Aware Rate Adaptation (SARA) and Content-Aware Rate Adaptation (CARA) in five network test cases with three video sequences. Obtained results show that on average LIU has the most representation switching events regardless of the spatial (SI) and temporal information (TI) of video sequence and it has the lowest achieved bitrate values. Although CARA and SARA achieve similar results, due to the fact that SARA has four phases of deciding on the representation of the next segment, it has the least number of switching events and a larger value of switching depth. CARA achieves highest average values of achieved bitrate in cases when video sequences Elephants Dream and Big Buck Bunny with lower SI and TI are used.
Multiscale Meets Spatial Awareness: An Efficient Attention Guidance Network for Human Parsing

Human parsing, which aims at resolving human body and clothes into semantic part regions from an human image, is a fundamental task in human-centric analysis. Recently, the approaches for human parsing based on deep convolutional neural networks (DCNNs) have made significant progress. However, hierarchically exploiting multiscale and spatial contexts as convolutional features is still a hurdle to overcome. In order to boost the scale and spatial awareness of a DCNN, we propose two effective structures, named “Attention SPP and Attention RefineNet,” to form a Mutual Attention operation, to exploit multiscale and spatial semantics different from the existing approaches. Moreover, we propose a novel Attention Guidance Network (AG-Net), a simple yet effective architecture without using bells and whistles (such as human pose and edge information), to address human parsing tasks. Comprehensive evaluations on two public datasets well demonstrate that the AG-Net outperforms the state-of-the-art networks.
The Comparison of K-Nearest Neighbor (K-NN) Algorithm and Fuzzy Tsukamoto Logic in the Determination of SMA Students Majors in Banten

Students' majors at High School (SMA) in Banten is certainly different from the public ones which has already had its standardization and integrated computerized system in students' majors. Most of private schools of SMA have not had a system and standardization in the students' majors. Factors of majors 'determination have not been based on students' skill, interest, and aptitude. This present research aims at assessing two approaches in benefiting data of SMA majors. The first approach is by usingK-Nearest Neighbor (K-NN) algorithm, and the second one is by using Fuzzy Tsukamoto method. From the data assessment in SMA students' majors, both of approaches (i.e., K-Nearest Neighbor (K-NN) algorithm and Fuzzy Tsukamoto method) can be used for SMA students' majors with a fairly-high percentage of accuracy, of which K-NN98,4% and Tsukamoto 99,2%. This points out that both methods can be used for SMA students' majors in Banten. From those both methods, the comparison is conducted by using three factors, that is, final result, data processing, and the speed of process by looking at the condition in the field, then it is found that Fuzzy Tsukamoto method is better than K-NN method in SMA students' majors in Banten.
Bidirectional Blockchain

Ensuring the authenticity and integrity of data transferred over the Internet is still a daunting task. Although various mechanisms have been proposed to strengthen the key management infrastructure, they still rely on trusted third parties (TTPs) or require additional entities to monitor the system. This paper introduces Bidirectional Blockchain - a collection of protocols that ensure the authenticity of data sent from a collection of one or more servers to distributed clients (e.g., Internet of Things (IoT) devices) without relying on TTPs. Bidirectional Blockchain is based on the benefits of multi-party computation and blockchain to ensure the secrecy of keys used to sign the distributed data and the correctness and integrity of the received data, respectively. We implemented Bidirectional Blockchain on a Raspberry Pi, and our evaluation results confirm that Bidirectional Blockchain is lightweight and hence can be used to secure a variety of data transactions, including software updates, even on resource-constrained devices.
Are Cryptocurrencies Priced in the Cross-section? A Portfolio Approach

Most papers, that study determinants of cryptocurrency prices, find no significant relation with existing market factors. We examine a portfolio approach to explore cross-sectional pricing within the cryptomarket. At its inception, Bitcoin meant to be an alternative to fiat currencies. Yet high returns in this market may have also attracted usual investors as well, because of these new investment opportunities and diversification venues. Since Bitcoin, the number of cryptocurrencies has reached more than 12,000 worldwide at the end of 2021, for a market capitalization of 2.35 Trillion US dollars according to coingecko.com. Hence, investors have more choices when they decide to enter into the cryptomarket. So, they have an incentive to understand the cryptomarket interaction with their current investments. Using 110 cryptocurrencies based on their market capitalization, from September 2014 and June 2021, we study ten (10) factors which belong to three asset classes, i.e., equity, currency, and commodity. The result shows that two factors, S&P 500 (market risk premium) and VIX (volatility of stock market) are both significant in our cross-sectional analysis, implying the influence of the stock market on cryptomarket. More importantly, in the cross-section, cryptocurrencies can (tend to) be used as an alternative to gold/silver in storing value. These results support the market participantsâ€™ view that cryptocurrencies have both currency and investment functions.
Scene-segmented Video Information Annotation System V2.0

We have built the scene-segmented video information annotation system and upgraded it to version 2.0. The system imports the video by user selection and splits into the scene units. Each scene clips are annotated by the integration of visual features derived by state-of-the-art deep learning techniques. The proposed system uses the multiview deep convolutional neural network for video segmentation and a supervised movie caption model for video annotation. Each functionality has been installed in two different sub-systems and connected through the web interface. The web interface allows connecting to external content providers in order to expand the capability of the system.
Cloud Drive Apps - Closing the Gap Between AI Research to Practice

In the past few years, Cloud Drive Apps have aroused increasing interest from end-users and enterprise customers. During this period, numerous artificial intelligence based features were introduced, such as functions enabling users to intelligently organize, search, share, edit and recreate content with their images and videos. In this talk, I will introduce our latest work related to highly-efficient image understanding, which aims to enable various novel methods (such as neural architecture search [1,2] and advanced training techniques [3,4]) to be practiced in Cloud Drive App use cases. I will discuss use-cases such as image search through free-text query, focusing on difficult real-world problems and suggested solutions. I will also demonstrate the usefulness of the proposed techniques when applied to public competitions.
Improved accuracy in counting residual white blood cells in red cell concentrates using new blood bank mode software of <scp>SYSMEX XN‐1000</scp> hematology analyzer

TransfusionVolume 60, Issue 10 p. 2456-2457 LETTERS TO THE EDITOR Improved accuracy in counting residual white blood cells in red cell concentrates using new blood bank mode software of SYSMEX XN-1000 hematology analyzer Johan W. Lagerberg, Corresponding Author j.lagerberg@sanquin.nl orcid.org/0000-0002-8878-8079 Department of Product and Process Development, Sanquin Blood Bank, Amsterdam, The NetherlandsSearch for more papers by this authorDirk de Korte, orcid.org/0000-0001-9772-7377 Department of Product and Process Development, Sanquin Blood Bank, Amsterdam, The NetherlandsSearch for more papers by this author Johan W. Lagerberg, Corresponding Author j.lagerberg@sanquin.nl orcid.org/0000-0002-8878-8079 Department of Product and Process Development, Sanquin Blood Bank, Amsterdam, The NetherlandsSearch for more papers by this authorDirk de Korte, orcid.org/0000-0001-9772-7377 Department of Product and Process Development, Sanquin Blood Bank, Amsterdam, The NetherlandsSearch for more papers by this author First published: 14 October 2020 https://doi.org/10.1111/trf.15985Read 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. Volume60, Issue10October 2020Pages 2456-2457 RelatedInformation
Penggunaan Buku Ajar Nihongo Kirakira I pada Pembelajaran Bahasa Jepang di SMA Kesatrian I Semarang

Nihongo Kirakira I is a textbook published by The Japan Foundation and compiled by following the Content Competencies and Basic Competencies of the 2013 curriculum for secondary education. This book has fulfilled the requirements of the Ministry of Education and Culture to answer the needs of basic level Japanese language learners, equivalent to the level 5 JLPT (Japanese Language Proficiency Test). Preliminary studies show that there are positive responses regarding to this book from students in Kesatrian 1 High School Semarang. This study aims to investigate the further responses about Japanese language learning by using Nihongo Kirakira I. This research was conducted by using quantitative descriptive methods, involving 72 respondents in Kesatrian High School 1. The data were obtained through open and closed questionnaires, then analyzed by using percentage descriptive techniques. The result shows that 42 respondents (58%) gave "good" response and 30 respondents (42%) gave "not good" response. Poor responses are caused by respondents’ difficulty in pronouncing Japanese vocabulary and in reading hiragana and katakana letters. In general, respondents thought that learning Japanese using Nihongo Kirakira I is adequate.
Design Guidance for Interactive Visualization of Movies and Videos in Time and Space

Considered an art form, commercial and user-generated movies and videos are being released and shared in huge quantities over the Internet and TV on demand. The complexity and richness of their contents make these media a very interesting source of information, requiring new and powerful ways of visualizing and accessing them in order to make the most of their potential.A set of guidelines as design principles are described, applied, and discussed upon our previous work on interactive visualizations of movies. We aim in creating the underlying framework to inform and guide the design that should enhance the understanding, navigation, and access to movies and videos in time and space, from video collections to contents.
Fast Path Planning for On-Water Automatic Rescue Intelligent Robot Based on Constant Thrust Artificial Fluid Method

In this paper, fast path planning of on-water automatic rescue intelligent robot is studied based on the constant thrust artificial fluid method. First, a three-dimensional environment model is established, and then the kinematics equation of the robot is given. The constant thrust artificial fluid method based on the isochronous interpolation method is proposed, and a novel algorithm of constant thrust fitting is researched through the impulse compensation. The effect of obstacles on original fluid field is quantified by the perturbation matrix, and the streamlines can be regarded as the planned path. Simulation results demonstrate the effectiveness of this method by comparing with A-star algorithm and ant colony algorithm. It is proved that the planned path can avoid all obstacles smoothly and swiftly and reach the destination eventually.
The detection method of positioning accuracy in CORS system exists

Abstract The positioning accuracy detection of CORS system is an essential link before it is put into market. However, the location accuracy detection method does not take into account the correlation between the comprehensive environment around the detection point and the external coincidence accuracy, so the method is not rigorous and scientific. This paper proposes a method based on nonparametric statistics, which can verify the correlation between the comprehensive environment around the detection point and the external coincidence accuracy from a mathematical point of view, and then it is incorporated into the traditional positioning accuracy detection method, and is verified by a CORS system. The results are satisfactory, which proves that this method is very effective in compensating the integrity of CORS positioning accuracy detection method.
The Implementation of Fuzzy Mamdani Algorithm for Determining Departments in SMK

Based on the Regulation of the Minister of National Education (Permendiknas/Peraturan Menteri Pendidikan Nasional) Number 22 of 2006, the organization of programs in SMK is divided into two groups, X grade is a general program and XI grade and XII grade take majors. Designation at SMK aims to provide opportunities for students to develop competency knowledge, skills, and attitudes according to their interests, talents, and academic abilities. The mismatch in determining the direction for students can result in difficulties in learning. So this research discusses the application of the Fuzzy Mamdani Algorithm for the determination of the Multimedia and Network Computer Engineering program for X grade students of SMK Al Islahiyah Singosari based on 3 Input variables and 2 Output variables. Input variables consist of the report card value (NRaport), the TKJ interview score (Network Computer Engineering) and the MM interview score (Multimedia) which consists of 3 fuzzy sets, namely (low, normal, high). While the output variables are TKJ and MM majors. The results were obtained from the comparison of the output variable values of TKJ and MM. If the TKJ output is greater than the MM output, the student enters the TKJ department, and vice versa, whereas if the TKJ and MM outputs have the same value, the school will determine the majors completely. This research produces a system that can help students who register at the SMK Terpadu Al-Ishlahiyah in deciding majors and concentrations.
Enhanced Activity Recognition of the IoT Smart Home users through Cluster Analysis

We present an enhanced activity recommender system with deep level causation analysis for the primary and secondary routines identified in the smart home system. The causation analysis helps in building various health care recommender services to handle preventive measures, to recommend quick medicines etc. Our system takes various IoT device events including health and monitoring devices of the smart home as input, performs preprocessing using the big data handling techniques to transform it to be applicable to our system, applies our custom pattern-mining algorithm to derive the activity patterns of the users, performs the causation analysis of each of the primary and secondary patterns of the user, derives the causation scores of the patterns using various health monitoring IoT devices, monitors those patterns with high causation score and then finally provides the necessary recommendations to the smart home users. In a regular smart home system, activity patterns with high confidence score are identified as user routines and are monitored, however in environments like elderly care systems, a secondary pattern with high causation score plays a key role as it is backed up with various vitals of the user and helps in taking up preventive measures in real time. We validate our system using user trial methods and various validation techniques.
Enhanced Video Classification System Using a Block-Based Motion Vector

The main objective of this work was to design and implement a support vector machine-based classification system to classify video data into predefined classes. Video data has to be structured and indexed for any video classification methodology. Video structure analysis involves shot boundary detection and keyframe extraction. Shot boundary detection is performed using a two-pass block-based adaptive threshold method. The seek spread strategy is used for keyframe extraction. In most of the video classification methods, selection of features is important. The selected features contribute to the efficiency of the classification system. It is very hard to find out which combination of features is most effective. Feature selection makes relevance to the proposed system. Herein, a support vector machine-based classifier was considered for the classification of video clips. The performance of the proposed system considered six categories of video clips: cartoons, commercials, cricket, football, tennis, and news. When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. The research concentrated on feature extraction where combination of selected features was given to a classifier to get the best classification performance.
Authoring Negotiation Content and Programming Simulated Patients

Competent negotiation with simulated patients can save expenses in medical education and improve outcomes for all parties involved. The use of simulated agents is beneficial for a study of human behaviour and cognition due to the possibility to create and manage a wide range of specific social situations. Building plausible cognitive models underlying the agent’s intelligent behaviour from scratch is challenging and costly. Interaction designers and cognitive engineers require sufficient background knowledge to decide which domain information, resources and activities are important. Domain experts require sufficient understanding of human interaction and social cognition. All may lack advanced software development skills and an access to sufficient amount of authentic data. This paper presents a methodology to author cognitive agents and interactions with them. Authors can easily encode agents’ knowledge and equip them with different sets of preferences and decision making strategies. This offers abundant opportunities for various social simulations: to create and control situations in which doctor’s decision making and negotiation skills can be applied and assessed; employ and relate specific action patterns to various strategies and sociopragmatic variables of interactional power, social distance and degree of imposition; predict outcomes and explain why the choices made lead to what specific outcomes. The proposed approach also enables efficient collection of significant amount of annotated dialogue data and can be applied to model various medical and not medical negotiation scenarios.
An EM Algorithm for Capsule Regression

We investigate a latent variable model for multinomial classification inspired by recent capsule architectures for visual object recognition (Sabour, Frosst, &amp; Hinton, 2017 ). Capsule architectures use vectors of hidden unit activities to encode the pose of visual objects in an image, and they use the lengths of these vectors to encode the probabilities that objects are present. Probabilities from different capsules can also be propagated through deep multilayer networks to model the part-whole relationships of more complex objects. Notwithstanding the promise of these networks, there still remains much to understand about capsules as primitive computing elements in their own right. In this letter, we study the problem of capsule regression—a higher-dimensional analog of logistic, probit, and softmax regression in which class probabilities are derived from vectors of competing magnitude. To start, we propose a simple capsule architecture for multinomial classification: the architecture has one capsule per class, and each capsule uses a weight matrix to compute the vector of hidden unit activities for patterns it seeks to recognize. Next, we show how to model these hidden unit activities as latent variables, and we use a squashing nonlinearity to convert their magnitudes as vectors into normalized probabilities for multinomial classification. When different capsules compete to recognize the same pattern, the squashing nonlinearity induces nongaussian terms in the posterior distribution over their latent variables. Nevertheless, we show that exact inference remains tractable and use an expectation-maximization procedure to derive least-squares updates for each capsule's weight matrix. We also present experimental results to demonstrate how these ideas work in practice.