Reviewerly/exHarmony huggingface dataset:
Oxygen Regime Control in Water Body Amelioration

An important aspect of water body amelioration is the control of the oxygen regime in water mass. Pollution of water bodies deteriorates their oxygen regime, and the natural inflow of oxygen through the free surface is not enough to compensate for oxygen consumption for pollutant oxidation. Water pollution by various substances causes damage resulting from a decrease in the ecological safety of urban water bodies. Data of World Health Organization (WHO) show that the contact of the population with polluted water bodies causes spreading of deceases, such as cholera, diarrhea, dysentery, hepatitis A, typhoid, and poliomyelitis, and creates considerable health risks. In this context, the artificial aeration of water mass with the use of aeration systems, which improve water quality, is gaining in importance. Most widespread among such aeration systems are diffused-air aerators, in which air supplied by a compressor passes through perforated diffuser plates. The size of the perforation is often chosen with no appropriate hydraulic substantiation. The size of the resulting air bubbles, no doubt, depends on the size of perforation holes; however, the available design relationships give contradictory results depending on the immersion depth of the diffuser plate and the working pressure, which determines air discharge velocity from diffuser plate perforations. This shows that the studies along this line are of scientific and practical importance. This article presents the analysis of the existing relationships for determining the size of air bubbles that form when air is pumped into water through nozzles of different diameters at different pumping rates; the analysis has shown the results of such calculations to differ considerably. Buckingham π-theorem was used to construct dimensionless groups, determining the relationship between the size of bubbles and the factors that govern the outflow of air into water. Dimensionless groups were used to obtain a formula for calculating the size of air bubbles at the aeration of water mass.
Equivocating Yao: Constant-Round Adaptively Secure Multiparty Computation in the Plain Model

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 11 October 2017Accepted: 04 May 2021Published online: 13 January 2022Keywordsadaptive security, multiparty computation, garbled circuitsAMS Subject Headings94A60Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Binary homodyne detection for observing quadrature squeezing in satellite links

Optical satellite links open up new prospects for realizing quantum physical experiments over unprecedented length scales. We analyze and affirm the feasibility of detecting quantum squeezing in an optical mode with homodyne detection of only one bit resolution, as is found in satellites already in orbit. We show experimentally that, in combination with a coherent displacement, a binary homodyne detector can still detect quantum squeezing efficiently even under high loss. The sample overhead in comparison to non-discretized homodyne detection is merely a factor of 3.3.
Delta Embedding Learning

Unsupervised word embeddings have become a popular approach of word representation in NLP tasks.However there are limitations to the semantics represented by unsupervised embeddings, and inadequate fine-tuning of embeddings can lead to suboptimal performance.We propose a novel learning technique called Delta Embedding Learning, which can be applied to general NLP tasks to improve performance by optimized tuning of the word embeddings.A structured regularization is applied to the embeddings to ensure they are tuned in an incremental way.As a result, the tuned word embeddings become better word representations by absorbing semantic information from supervision without "forgetting."We apply the method to various NLP tasks and see a consistent improvement in performance.Evaluation also confirms the tuned word embeddings have better semantic properties.
A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech -- a Deep Learning approach

In this project, we aim to build a Text-to-Speech system able to produce speech with a controllable emotional expressiveness. We propose a methodology for solving this problem in three main steps. The first is the collection of emotional speech data. We discuss the various formats of existing datasets and their usability in speech generation. The second step is the development of a system to automatically annotate data with emotion/expressiveness features. We compare several techniques using transfer learning to extract such a representation through other tasks and propose a method to visualize and interpret the correlation between vocal and emotional features. The third step is the development of a deep learning-based system taking text and emotion/expressiveness as input and producing speech as output. We study the impact of fine tuning from a neutral TTS towards an emotional TTS in terms of intelligibility and perception of the emotion.
Infrastructure and Energy Conservation in Big Data Computing: A Survey

Progress in life, physical sciences and technology depends on eﬃcient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of eﬃcient HPC infrastructures. This paper addresses the vital problem of energy-eﬃcient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.
How cognitive and environmental constraints influence the reliability of simulated animats in groups

Abstract Evolving in groups can either enhance or reduce an individual’s task performance. Still, we know little about the factors underlying group performance, which may be reduced to three major dimensions: (a) the individual’s ability to perform a task, (b) the dependency on environmental conditions, and (c) the perception of, and the reaction to, other group members. In our research, we investigated how these dimensions interrelate in simulated evolution experiments using adaptive agents equipped with Markov brains (“animats”). We evolved the animats to perform a spatial-navigation task under various evolutionary setups. The last generation of each evolution simulation was tested across modified conditions to evaluate and compare the animats’ reliability when faced with change. Moreover, the complexity of the evolved Markov brains was assessed based on measures of information integration. We found that, under the right conditions, specialized animats were as reliable as animats already evolved for the modified tasks, that interaction between animats was dependent on the environment and on the design of the animats, and that the task difficulty influenced the correlation between the performance of the animat and its brain complexity. Generally, our results suggest that the interrelation between the aforementioned dimensions is complex and their contribution to the group’s task performance, reliability, and brain complexity varies, which points to further dependencies. Still, our study reveals that balancing the group size and individual cognitive abilities prevents over-specialization and can help to evolve better reliability under unknown environmental situations. Author Summary The ability to adapt to environmental changes is an essential attribute of organisms which have had evolutionary success. We designed a simulated evolution experiment to better understand the relevant features of such organisms and the conditions under which they evolve: First, we created diverse groups of cognitive systems by evolving simulated organisms (“animats”) acting in groups on a spatial-navigation task. Second, we post-evolutionary tested the final evolved animats in new environments–not encountered before– in order to test their reliability when faced with change. Our results imply that the ability to generalize to environments with changing task demands can have complex dependencies on the cognitive design and sensor configuration of the organism itself, as well as its social or environmental conditions.
Effectiveness of Mobile Learning: moPhyDict to Improve High School Students’ Physics Conceptual Understanding

In learning physics, the problem that arises is a lack of physics conceptual understanding in contextual learning. These problems are due to the absence of innovative and creative learning resources that can be used to learn by the students. One of the learning resources that can be used is a dictionary. moPhyDict (mobile physics dictionary) is a dictionary containing explanations of concepts and illustrations (images, videos, animations) in a language that is easily understood by students. This study specifically aims to test the effectiveness of Mobile Learning: moPhyDict to improve student’s physics conceptual understanding. The method employed in this study was a quasi-experiment design. The experimental design used a pretest-posttest control group design. The sample of the study was 60 students from senior High School, which divided into control group and experiment group. The instrument test in this study used conceptual multiple choice question. Based on the t-test on the control class and the experimental class, it can be inferred that there is a significance score of 0.031. These results are supported by data: (1) the average score of the ability to understand the concept of the control class who did not use moPhyDict increased from 37.4 to 55.46, and (2) the average score of the conceptual understanding of the experimental class who used moPhyDict increased from 40.87 to 74.33. It was concluded that moPhyDict is effective to improve physics conceptual understanding of High School Students.
Pemodelan Knowledge Dalam Proses Pemberian Beasiswa Bagi Mahasiswa Menggunakan Soft System Methodology (SSM) (Studi Kasus : Fakultas Keguruan dan Ilmu Pendidikan Universitas Pattimura Ambon)

The process of scholarship awarding especially for the university students through the sub-section of student affairs is influenced by the students’ background and achievements. The background of the students and the process of scholarship awarding in the sub-section of students affairs have the important role to support students’ achievements and academic finance. The method that used in designing knowledge method to this scholarship awarding is the kualitative method with the soft system methodology approach and the steps that added in accordance with the case study. Knowledge management of FKIP Unpatti Ambon is ecpected to work well if there is an interaction between each component and there is no imbalance of the three components, they are knowledge management plot, appropriate technology, and conducive workplace habits. Knowledge management that is modeled using the SSM approach can provide opportunities to FKIP Unpatti Ambon in catching and analysing the information in the faculties. Faculties can implement it strategically in the form of warehousing, and decision support system. The establishment of a process for accessing information to all outside societies through the internet, groupware, and group decision support system is very needed, so that the stakeholders in the fakulties are informed properly, informatively and inovatively. This makes the motivation of knowkedge accumulated from organizational experience.
Proficient Valuation and Estimation of Human Performance in Collective Learning Environments using Machine Learning

Novel organic single Machine learning has fascinated more and more attention in the past few decades. In this paper, we have proposed machine learning-based system architecture and algorithms to find patterns of learning, interaction, and relationship and effective valuation for a complex system involving massive data that could be obtained from a proposed collective learning environment (CLE). Collective learning may take place among larger team members to find solutions for real-time events or problems, and to discuss concepts or interactions during situational judgment tasks (SJT). Modelling a collective, networked system that involves multimodal data presents many challenges. This paper focuses on proposing a Machine Learning - (ML)-based system architecture to promote understanding of the behaviours, group dynamics, and interactions in the CLE. The designed framework integrates techniques from computational psychometrics (CP) and deep learning models that include the utilization of convolution neural networks (CNNs) for feature extraction, skill identification, and pattern recognition. The proposed framework also identifies the behavioral components at a micro level, and can help us model behaviors of a group involved in learning.
Let's Chat! Can Virtual Agents learn how to have a Conversation?

Intelligent virtual agents frequently engage the user in conversation. The underlying technology - often referred to as spoken dialogue systems - have experienced a revolution over the past decade, moving from being completely handcrafted to using data-driven machine learning methods.
Super-Node Based Detection of Redundant Ontology Relations

Redundant relations refer to explicit relations which can also be deducted implicitly. Although there exist several ontology redundancy elimination methods, they all do not take equivalent relations into consideration. Actually, real ontologies usually contain equivalent relations; their redundancies cannot be completely detected by existing algorithms. Aiming at solving this problem, this paper proposes a super-node based ontology redundancy elimination algorithm. The algorithm consists of super-node transformation and transitive redundancy elimination. During the super-node transformation process, nodes equivalent to each other are transferred into a super-node. Then by deleting the overlapped edges, redundancies relating to equivalent relations are eliminated. During the transitive redundancy elimination process, redundant relations are eliminated by comparing concept nodes' direct and indirect neighbors. Most notably, we proposed a theorem to validate real ontology's irredundancy. Our algorithm outperforms others on both real ontologies and synthetic dynamic ontologies.
Human-Robot Full-Sentence VQA Interaction System with Highway Memory Network

Intelligent robots such as social robots and home service robots that are being developed nowadays are required to have the ability to naturally communicate with users in natural language. Beyond simple data retrieval and simple dialogue level, we propose a new Human-Robot Interaction (HRI) system that enables a robot to understand and reason the environment around a user and present information about them in natural language, whenever the user asks a question to the robot. For its intelligent HRI, based on Dynamic Memory Networks (DMN), a neural network for Visual Question Answering (VQA), we propose a new full sentence VQA network model called Full-Sentence Highway Memory Network (FSHMN). For its robot platform, a three DOF robotic head was used which has a neck with three motors and a tablet PC head. To verify the feasibility of the proposed system, an experiment is performed in which a user and a robot interact with each other in a way of question answering in a customized kitchen environment. Through the experiment, we not only demonstrated the effectiveness of applying deep learning to HRI applications in real environments but also presented a new insight into HRI.
Survey on Web Application Vulnerability

Web applications have turned into a fundamental piece of our day to day life.Since web applications contain important sensitive data, programmers attempt to discover vulnerabilities and endeavor them to imitate the client, take data, or damage the application.Web Security is a difficult issue and it can't be ignored.Over the most recent couple of years, the world has seen a phenomenal time of technological development.Unfortunately, alongside the technological development, the attacks have additionally expanded.This paper represents in detail the most overall and unsafe web application vulnerable attacks.
Overview on the legal position of cryptocurrency (Bitcoin) in some countries in the world - Orientation to build a legal framework for cryptocurrency in Vietnam

The article focuses on analyzing and commenting on the nature of cryptocurrency (typically Bitcoin) and the legal position of this cryptocurrency in Vietnam through comparison with countries like France (Europe) and Thailand in order to make a perfect recommendation for Vietnamese law on this issue. Accordingly, the paper shows that in France, cryptocurrency is considered a kind of asset, although the French law currently does not recognize the ability to pay in encrypted money for all transactions, though in fact, it can be converted into real money. In Thailand, cryptocurrency is seen as a type of stock and can be traded on the stock exchange. Meanwhile in Vietnam, although there are many conflicting opinions, however, with the current regulations, cryptocurrency is sufficient to be considered a kind of property rights recorded in Vietnam Civil Code in 2015. And therefore, although not used as a means of payment as legal money, cryptocurrency should have been the object of exchange and transaction activities as a type of securities in Thailand. The management of cryptocurrency under the securities model could help Vietnam to control well the illegal cryptocurrency transactions such as money laundering, terrorist financing, etc. Besides, it also helps increase revenue for the State budget through taxation on transactions and income generated from such transactions.
Recurrent Existence Determination Through Policy Optimization

Binary determination of the presence of objects is one of the problems where humans perform extraordinarily better than computer vision systems, in terms of both speed and preciseness. One of the possible reasons is that humans can skip most of the clutter and attend only on salient regions. Recurrent attention models (RAM) are the first computational models to imitate the way humans process images via the REINFORCE algorithm. Despite that RAM is originally designed for image recognition, we extend it and present recurrent existence determination, an attention-based mechanism to solve the existence determination. Our algorithm employs a novel $k$-maximum aggregation layer and a new reward mechanism to address the issue of delayed rewards, which would have caused the instability of the training process. The experimental analysis demonstrates significant efficiency and accuracy improvement over existing approaches, on both synthetic and real-world datasets.
Fast and Streaming Analytics in ATM Cash Management

Cash management across a network of ATMs can be greatly improved, exploiting a multitude of information sources, which however generate huge datasets, not possible to be analysed via traditional methods. Business Intelligence in the Banking, Financial Services and Insurance (BFSI) sector can be even more challenging when exploitation of real-time information streams is desired. This paper presents the uCash ATM cash management system, running on top of CloudDBAppliance, supporting fast analytics over historical data, as well as streaming analytics functionalities over real-time data, served by its Operational Database. The paper discussed integration points with the platform and provides integration hints, thus constituting a real use case example of the CloudDBAppliance platform, allowing for its further exploitation in various application domains.
INVESTIGATING THE CULTURAL CONTENTS IN EXPERIENCING ENGLISH TEXTBOOK FOR EIGHT GRADE STUDENTS OF JUNIOR HIGH SCHOOL

Abstract Name: Yunita Dewi Wahyuni Student No.: 1409200080040 Study Program: Magister Degree in English Education Department: English Education Title : Investigating the Cultural Contents in Experiencing English Textbook for Eight Grade Students of Junior High School Keywords: Cultural Contents, Target Language, English textbook This research investigates the cultural contents implied in the English textbook entitled Experiencing English. A qualitative method was used in this research to analyze the English textbook. The instrument used for collecting data was only from documentation in the form of textbook. Since there are two research questions in this thesis, then the data were analyzed by using two frameworks, namely the framework of Cortazzi & Jin (1999) to investigate the types of culture and the framework of Byram et al. (1994) to investigate the aspects of culture. The result of the first research question shows that the target culture is prominently used in Experiencing English. Further, the second analysis analyzed the nine aspects that have to be included in the EFL textbook. The result shows that stereotypes and national identity is more frequently used in Experiencing English. This aspect represents the target culture stereotypes and national identity which are commonly focused on in the British and American culture. Finally, the conclusion leads to the cultural content that is frequently used in the Experiencing English textbook is the target language culture. Notwithstanding, the source/local culture values as drafted in the 2013 Curriculum is still implied in the textbook as intended to enable the students to reflect their own culture in English.
Studi Uji Kesesuaian Selisih Lapangan Kolimasi pada Pesawat Mammografi Analog dan Digital

Study of collimation field difference suitability test on analog and digital mammography at leading hospital in Makassar from 2016-2018. This test was carried out by using four coins of the same size placed on the edge of the collimator lamp field, one different size coin was placed on the table edge of the chest wall section, and one coin was placed on the surface under the compression pedal to coincide with the edge of the chest wall. The results of the p value obtained for each measurement point, both for the difference of the light collimation field toward the X-ray beam and the difference of X-ray beam collimation field toward image receptor have the same value of 0.000 (p &lt; 0.05). This value states that there are significant differences in the collimation field difference from 2016-2018. The results of collimation field difference suitability test on analog and digital mammography are still within the tolerance limits recommended by BAPETEN, for the difference of the light collimation field toward X-ray beam ≤ 1% SID and the difference of X-ray beam collimation field toward image receptor ≤ 2% SID. This shows that mammography is feasible and able to operate safely for patients and non-patients.
Information need: Introduction to the special issue

Information Need is one of the most significant and controversial concepts within Information Science. It is a concept that has one of the most substantial literatures within our field and appears widely across disciplines (Wilson, 2018) and extensively within both the research and practitioner literatures.