Reviewerly/exHarmony huggingface dataset:
Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages

We train neural machine translation (NMT) models from English to six target languages, using NMT encoder representations to predict ancestor constituent labels of source language words. We find that NMT encoders learn similar source syntax regardless of NMT target language, relying on explicit morphosyntactic cues to extract syntactic features from source sentences. Furthermore, the NMT encoders outperform RNNs trained directly on several of the constituent label prediction tasks, suggesting that NMT encoder representations can be used effectively for natural language tasks involving syntax. However, both the NMT encoders and the directly-trained RNNs learn substantially different syntactic information from a probabilistic context-free grammar (PCFG) parser. Despite lower overall accuracy scores, the PCFG often performs well on sentences for which the RNN-based models perform poorly, suggesting that RNN architectures are constrained in the types of syntax they can learn.
Design and Development of Hand Gesture Voice Conversion System Using for Dump and Deaf People

Sign language is difficult for dump people to communicate with normal people. Because most of the normal people they are don’t know the sign language. Communications among deaf, dump and a usual persons continuously must a large defied task. In this paper the main Moto is convert the sign language into human hearing voice. That is very useful for dump, deaf and normal people. Flex sensor and accelerometer to sense the movement made by finger. Another one is convert the voice into text through android app for deaf people. This project is used for specially challenged people hold equal par in the society.
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning

An essential problem in automated machine learning (AutoML) is that of model selection. A unique challenge in the sequential setting is the fact that the optimal model itself may vary over time, depending on the distribution of features and labels available up to each point in time. In this paper, we propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. This is accomplished by treating the performance at each time step as its own black-box function. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions using deep kernel learning (DKL). To the best of our knowledge, we are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose. Using multiple real-world datasets, we verify that our proposed method outperforms both standard BO and multi-objective BO algorithms on a variety of sequence prediction tasks.
Multi-level Fitness Critics for Cooperative Coevolution

In many multiagent domains, and particularly in tightly coupled domains, teasing an agent's contribution to the system performance based on a single episodic return is difficult. This well-known difficulty hits state-to-action mapping approaches such as neural networks trained by evolutionary algorithms particularly hard. This paper introduces fitness critics, which leverage the expected fitness to evaluate an agent's performance. This approach turns a sparse performance metric (policy evaluation) into a dense performance metric (state-action evaluation) by relating the episodic feedback to the state-action pairs experienced during the execution of that policy. In the tightly-coupled multi-rover domain (where multiple rovers have to perform a particular task simultaneously), only teams using fitness critics were able to demonstrate effective learning on tasks with tight coupling while other coevolved teams were unable to learn at all.
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate

In this paper, we consider distributed algorithms for solving the empirical risk minimization problem under the master/worker communication model. We develop a distributed asynchronous quasi-Newton algorithm that can achieve superlinear convergence. To our knowledge, this is the first distributed asynchronous algorithm with superlinear convergence guarantees. Our algorithm is communication-efficient in the sense that at every iteration the master node and workers communicate vectors of size $O(p)$, where $p$ is the dimension of the decision variable. The proposed method is based on a distributed asynchronous averaging scheme of decision vectors and gradients in a way to effectively capture the local Hessian information of the objective function. Our convergence theory supports asynchronous computations subject to both bounded delays and unbounded delays with a bounded time-average. Unlike in the majority of asynchronous optimization literature, we do not require choosing smaller stepsize when delays are huge. We provide numerical experiments that match our theoretical results and showcase significant improvement comparing to state-of-the-art distributed algorithms.
Evaluation of Teaching Effect of Internet of Things Education Platform based on Long-term and Short-term Memory Network

Aiming at the problems of poor teaching effect and high energy consumption existing in traditional internet of things education platform, this paper puts forward a teaching effect evaluation method of internet of things education platform based on long-term and short-term memory network. Firstly, the status quo of the education platform of the internet of things is analysed, including the development and structure of the education platform of the internet of things. Then, the LSTM model is constructed to realise the evaluation of the teaching effect of the education platform of the internet of things. Finally, through the study of the model, the teaching effect of the internet of things education platform is evaluated. The experimental results show that the running energy consumption accounts for 12.84% of the total energy consumption of the system when the method is used to evaluate the teaching effect, which proves the effectiveness of the method.
Improved Algorithms for Learning Equilibria in Simulation-Based Games

We tackle a fundamental problem in empirical game-theoretic analysis (EGTA), that of learning equilibria of simulation-based games. Such games cannot be described in analytical form; instead, a black-box simulator can be queried to obtain noisy samples of utilities. Our first theorem establishes that uniform approximations of simulation-based games are equilibrium preserving. We then design algorithms that uniformly approximate simulation-based games with finite-sample guarantees. Our first algorithm, global sampling (GS), extends previous work that constructs confidence intervals assuming bounded utilities with confidence intervals that are sensitive to variance. The second, progressive sample with pruning (PSP), samples progressively, ceasing the sampling process (i.e., pruning strategies) as soon as it determines that the corresponding utilities have been sufficiently well estimated for equilibrium computation. We experiment with our algorithms using both GAMUT, a state-of-the-art game generator, and Gambit, a state-of-the-art game solver. For a broad swath of games, we show that GS using our variance-sensitive bounds outperforms previous work, and that PSP can significantly GS. Here outperform means achieving the same guarantees with far fewer samples.
DevOps Automation Advances I.T. Sectors with the Strategy of Release Management

This Development of software release is the systematic process, planning, designing, and monitoring software through different phases and settings; testing and delivery of software releases included. Traditional models, such as incremental/ iterative and ad-hoc approaches, do not fulfill today's demanding customers or I.T. companies. New methods, such as agile software creation and the continuous implementation of DevOps, occur. The research problem for this study is to understand how development and operations automation leads to improving the strategies in release management and easing operations for the I.T. sector (Slack et al., 2018). DevOps and Agile are equally beneficial to faster implementation of working functions in development. Continuous Delivery and DevOps primarily seek to provide quicker and more frequent, more stable applications to meet consumer and company needs (Verona et al., 2016). This paper provides an overview of the development of software release management, from conventional methods to agile and product development via DevOps. This study demonstrates how automation in software release management advances the strategies in the I.T. sector and ease the operations both in time and in effect to meet information system’s needs. A review of the literature will be conducted to build a robust evidence-based study on the research problem. Based on what many scholars have found out in their various studies, there is a strong correlation between improved automation and the ease of operations in release management to meet the I.T. needs. This DevOps study aims to explore the benefits of automation advancement for I.T. stakeholders and operating teams to make it easier for emerging technologies to move from initiation to completion of its operations. On a small scale, the advancement of automation is improved by alternating between infrastructure and design. More organized methods, such as CD, are generally needed in large organizations. The most advanced breakthroughs to enhance the reliability of software development in large companies are usually the application of these concepts on a scale, and it should be the focus of any significant transformation (Kim et al., 2016). The software development life cycle and its release management are seen as a painstaking process, because the available resources and time in one software release may not generally meet all requirements. However, the method enables I.T., stakeholders, based on each release restriction, to reach sections of their operational needs for product releases. There are several obstacles to the planning and review process, making it one of the most common problems in software demand engineering.
PENERAPAN DECISION SUPPORT SYSTEM DYNAMIC MENGGUNAKAN SIMPLE ADDICTIVE WEIGHTING DALAM PENENTUAN PEGAWAI TERBAIK

Lots of applications or programs that are very useful to simplify human work. This includes applications that are made within a company. A company needs an intelligent system or an agent that controls the company's system. In a company has employees who work. This research will discuss the Dynamic Decision Support System in determining the best employees using one of the web-based Multi-Criteria Decision Making methods, which is Simple Additive Weighting (SAW). By using 2 types of data namely pattern data and test data. The data inputted were 15 data consisting of 10 test data and 5 pattern data. Then a confusion matrix can be obtained in the form of an accuracy value of 25%, a precision of 100%, a recall of 14%, and an F Measure of 24.5%.
M-Learning in Higher Education: Exploring the Gender Based Faculty Performance of Business Schools in Pakistan

This study aims to observe the influence of inclusion of mobile learning in Business Schools on students’ performance and gender based faculty members’ performance in business schools in Karachi, Pakistan. The functions offered in mobile phones include the access to information of real-time, medium of communication, and host of affordances. Nevertheless, along with the opportunities and incentives the mobile devices carry opportunities for students to involve in academic dishonesty, deviation and distraction from the assigned tasks. By employing quantitative research method, this study tends to conduct an experiment to explore if faculty members in business schools perform differently based on their different genders with the inclusion of mobile learning. The result of the study reveals no difference between the performance of male and female faculty members with the inclusion of mobile learning by implementing the Google Class into their regular classes. Therefore, this study recommends the academic leaders, curriculum designers, and educationists in the field of business education to pay attention on the inclusion of mobile learning, as it is equally beneficial to students and faculty members regardless of their different genders.
Experience of applying and integrating of the online education system on a base of Moodle platform into the university's educational environment

The growth of the number of alternative sources of quality knowledge and ways of obtaining them by information and media Internet technologies creates a rivalry between traditional educational institutions in the educational space and prompts the usage of modern teaching methods.Implementation of Internet information technologies in higher education institutions faces a number of problems due to the lack of sustainable methods and the diversity of curricula, in particular, the need for teachers to acquire relevant competencies and the need to change teaching paradigms.In addition, online education systems, despite their benefits, such as the dynamism of educational material updates, accessibility and transparency, have certain disadvantages that require comprehensive study and adaptation to the regulatory requirements of the education system.Even though there are separate control documents in Ukraine, that define the terminology and basic principles of distance learning, e-learning textbooks and courses, etc., the latest on-line technologies need the accumulation of practical experience and comprehensive study of methods for the implementation, use and development of relevant software in accordance with educational standards .The article analyzes and summarizes the experience of construction of the online education system on the Moodle platform by the example of Kremenchuk Mykhaylo Ostrogradsky National University.Recommendations on optimization of the implementation process are given in the article.Also this article identifies main problems of integrating the online system into the educational space of the educational institution and ways of their solution.Authors have proposed a methodology of deployment of the online education system, have investigated the possibility of taking into account priorities of users and peculiarities of educational disciplines during its modular sequential configuration.Authors have analyzed the dynamics of educational materials addition and have developed software for statistical analysis of quantitative characteristics of electronic training courses in order to optimize the costs of implementation.The article confirms that the use of online education system helps to improve the quality of educational services and reduces costs of software through the use of online technologies.
Carbon dioxide as the main hazard in the design of personal ventilation systems

Mental work, used everywhere at present, is quite diverse and is characterized by various degrees of responsibility, monotony, attention, and the level of emotional stress. To provide comfortable living conditions for a person in a permanent workplace, it is also necessary to organize air conditioning systems. The most promising for this activity are personal ventilation systems, which provide a high-quality air environment in the area of human breathing while reducing capital and operating costs in comparison with traditional (mixing, displacement ventilation). However, when designing personal ventilation systems, one should not focus on the “average reference man”, but take into account the individual characteristics of the employee (age, gender, etc.) performing a specific type of mental activity. In order to clarify the actual value of carbon dioxide (with a certain degree of error) emitted by a person in a certain mental work, we perform a series of experiments. The authors conducted a significant amount of full-scale experimental investigations, the result of which is the confirmation of nature and dynamics of carbon dioxide`s changes in the room in the absence (inactivity) of ventilation systems (linear dependence), as well as the refinement of the amount of carbon dioxide emitted in a particular type of mental activity. It should be noted that this work is the beginning of large-scale scientific research designed to collect and systematize data on emissions of harmful substances from people engaged in various types of mental activity.
Face Recognition System Based on Gabor Wavelets Transform, Principal Component Analysis and Support Vector Machine

<p class='IJASEITAbtract'>Face Recognition is a well-known image analysis application in the branches of pattern recognition and computer vision. It utilizes the uniqueness of human facial characteristics for personnel identification and verification. For a long time, the recognition of facial expressions by using computer-based applications has been an active area of study to recognize face scheme through a face image database. It is used in a variety of essential fields of modern life such as security systems, criminal identification, video retrieval, passport and credit cards. In general, face recognition process can be summarized in three distinct steps: preprocessing, feature extraction, and classification. At first, histogram equalization and median filter are applied as preprocessing methods. Secondly, Gabor wavelets transform extracts the features of desirable facial characterized by, orientation selectivity, spatial locality, and spatial frequency to keep up the variations caused by the varying of facial expression and illumination. In addition to that, Principal Component Analysis methodology (PCA) is used in dimensionality reduction. At last, Support vector machine (SVM) is applied in classifying the feature of the image according to the classis of every mage. In order to test the approach used in this research, experiments were running on Yale database of 165 images from 15 individuals in MATLAB environment. The results obtained from the experiments confirmed the accuracy and robustness of the proposed system.
Introduction to IAM Architecture (v2)

In this section of the BoK, you will explore several conceptual architectures and how they enable IAM solutions across your enterprise. IAM touches all aspects of an organization’s IT environment whether it’s the HR system, email system, phone system or corporate applications, they all need to interface to the IAM environment. Whether it is by supporting the enforcement of user provisioning rules or validating the access of non-corporate users, IAM will always play a role in making IT operations efficient and secure. An architectural approach will heighten the probability that a consistent and comprehensive IAM solution will be achieved.
E-Voting using Blockchain Technology

Democratic voting is a crucial and serious event in any country, the current voting scheme in any country is through ballot paper or by use of EVM.These processes have many drawbacks such as transparency, low voter turn-out, tampering of votes, distrust in the election body, forging of unique Id (voter id card), delay in giving out results and the most important is security issues.Security of digital voting is always the biggest concern when considering to implement a digital voting system.With such monumental decisions at stake, there can be no doubt about the system's ability to secure data and defend against potential attacks.One way the security issues can be potentially solved is through the use of blockchain technology.Blockchain technology offers infinite number of applications.Blockchain is a distributed ledger technology that allows digital assets to be transacted in a peer-to-peer decentralized network.A distributed ledger technology is an exciting advancement in this regard.Block is a collection of all the transactions.Blockchain possess salient features such as immutability, Decentralization, Security, Transparency and anonymity.Blockchain with smart contracts emerges as a promising candidate for building a safer, secure and transparent E-voting systems.In this paper we have implemented and tested a sample e-voting application as a smart contract for the Ethereum network using the blockchain technology through wallets and the Solidity language.Limited amount of token(gas) is given in the wallet which is exhausted when the user votes thus preventing duplicity of votes.This paper also highlights the pros and cons of using blockchain technology and also demonstrates a practical system by showcasing a webapp for voting and its limitations.
Small Implementation Differences Can Have Large Effects on Evolvability

A key challenge in Evolutionary Computation is fitness landscape design. While the objective of the optimization process is often predefined, the actual fitness function, computational substrate, mutation scheme, and selection function must often be chosen by the experimenter. These choices are recognized to possibly have a large impact on experimental results. Here we investigate implementation differences of the encoding method and elaborate on its potential impact.
Contract Networking for Crowdsourced Connectivity

Universal connectivity is still unavailable or expensive for half of the global population, despite being critical for social participation. The deployment of crowdsourced networking infrastructures creates an opportunity for local development, where anyone can deploy a new device. In such infrastructures connectivity offer can expand incrementally and be sustainable through investment and fees resulting from the demand and consumption of content and services, including Internet access, that compensate the cost of the underlying network. While routing coordinates network data flows, economic flows can be coordinated by smart contracts built over a local distributed ledger. We define crowdsourced networks, the concept, architecture, and implementation using a local Ethereum PoA blockchain with Solidity smart contracts that compensate the data traffic contribution and consumption recorded by a traffic monitoring system, on a wireless mesh network. The prototype software has been validated in a controlled mesh network environment. Functional tests show its ability to account and route economic flows with small resource consumption, and therefore confirms these networks can develop organically by the addition of consumer and provider participants to reach the typical scale of most wireless mesh access networks and deliver networking services that aim to be socially and economically sustainable.