Reviewerly/exHarmony huggingface dataset:
Generation of Chaotic Signal for Scrambling Matrix Content

Very well evolved, information technology made so easy the transfer of all types of data over public channels. For this reason, ensuring data security is certainly a necessary requirement. Scrambling data is one solution to hide information from non authorized users. Presenting matrix content, image scrambling can be made by only adding a mask to the real content. A user, having the appropriate mask, can recognize the image content by only subtracting it. Chaotic function is recently used for image encryption. In this paper, an algorithm of image scrambling based on three logistic chaotic functions is proposed. Defined by its initial condition and parameter, each chaotic function will generate a random signal. The set of initial conditions and parameters is the encryption key. The performance of this technique is ensured for two great reasons. First, using masks on the image makes unintelligible its content. Second, using three successive encryption processes makes so difficult attacks. This point reflects, in one hand, a sufficient key length to resist to brute force attack. In the other hand, it reflects the random aspect of the pixel distribution in the scrambled image. That means, the randomness in one mask minimizes the correlations really existent between neighboring pixels. That makes our proposed approach resistant to known attacks and suitable for applications requiring secure data transfer such as medical image exchanged between doctors
Universal trapping law induced by an atomic cloud in single-photon cooperative dynamics

The single-photon cooperative dynamics of an assembly of two-level quantum emitters coupled by a bosonic bath is investigated. The bosonic bath is general and can be anything as long as the exchange of excitations between quantum emitters and bath is present. In these systems it is found that the population on the excited emitter keeps a simple and universal trapping law due to the existence of the system's dark states. Different from the trapping regime caused by photon-emitter dressed states, this type of trapping is only associated with the number of quantum emitters. According to the trapping law, cooperative spontaneous emission at the single-photon level in this kind of system is universally inhibited when the emitter number is large enough.
Land-use classification via ensemble dropout information discriminative extreme learning machine based on deep convolution feature

Classifying land-use scenes with high quality and accuracy is an important research direction in current hyperspectral remote sensing images, which is conducive to scientific management and utilization of land. An effective classifier and feature extractor can improve classification stability and accuracy. Therefore, based on deep learning technique, a dropout-based ensemble learning method is proposed in this paper, which combines convolutional neural network (CNN) and information discriminating extreme learning machine (IELM). Pre-trained CNN is used to learn effective and robust features, and deep convolution features are fed to the IELM classifier. Then the adoption of dropout technique and ensemble method can improve generalization capabilities and stability. The effectiveness of the proposed algorithm is tested by hyperspectral remote sensing image classification experiments. The experimental results show that the proposed E-CNN-dropIELM has achieved satisfactory results compared to state-of-the-art methods in terms of classification accuracy and stability.
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning

We propose a novel hybrid stochastic pol-icy gradient estimator by combining an un-biased policy gradient estimator, the REIN-FORCE estimator, with another biased one,an adapted SARAH estimator for policy op-timization. The hybrid policy gradient esti-mator is shown to be biased, but has vari-ance reduced property. Using this estimator,we develop a new Proximal Hybrid StochasticPolicy Gradient Algorithm (ProxHSPGA) tosolve a composite policy optimization prob-lem that allows us to handle constraints orregularizers on the policy parameters. Wefirst propose a single-looped algorithm thenintroduce a more practical restarting vari-ant.We prove that both algorithms canachieve the best-known trajectory complex-ityO(e−3)to attain a first-order stationarypoint for the composite problem which is bet-ter than existing REINFORCE/GPOMDPO(e−4)and SVRPGO(e−10/3)in the non-composite setting. We evaluate the perfor-mance of our algorithm on several well-knownexamples in reinforcement learning. Numer-ical results show that our algorithm outper-forms two existing methods on these exam-ples. Moreover, the composite settings in-deed have some advantages compared to thenon-composite ones on certain problems.
The CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer

This paper describes the CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0 on typologically diverse morphological inflection.The (unrestricted) submission uses the cross-lingual approach of our last year's winning submission (Anastasopoulos and Neubig, 2019), but adapted to use specific transfer languages for each test language.Our system, with fixed non-tuned hyperparameters, achieved a macro-averaged accuracy of 80.65 ranking 20 th among 31 systems, but it was still tied for best system in 25 of the 90 total languages.
Attendance System using Face Recognition utilizing OpenCV Image Processing Library

Face recognition Technology is being appealing field in recent years. Taking attendance is a real-world task, which needs a creative solution to reduce time, efforts and resources. Face recognition Attendance is a technique to detect and recognize the students' or employees' face for marking their attendance by using unique face features extracted from the images captured. In proposed face recognition project, a raspberry PI based system will be able to detect and recognize human faces in a quick and accurate way via images or videos that are being captured through a Camera. It detects the faces within the image and compares it with the listed faces in the database. On recognition of a registered face on the captured image assortments, the attendance of that student is marked present otherwise absent. The system is developed on Open Source image processing library hence; it is not hardware nor software dependent. Many algorithms are used to ameliorate the performance of the system but the concept to be implemented here is Eigen matrix concept (Eigen Faces). It is used to convert the images into the matrix, based on the features of the images, to easily recognize the faces of the students, so that the attendance database can be easily updated.
Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models

What do powerful models of word meaning created from distributional data (e.g.Word2vec (Mikolov et al., 2013) BERT (Devlin et al., 2019) and ELMO (Peters et al., 2018)) represent?What causes words to be similar in the semantic space?What type of information is lacking?This thesis proposal presents a framework for investigating the information encoded in distributional semantic models.Several analysis methods have been suggested, but they have been shown to be limited and are not well understood.This approach pairs observations made on actual corpora with insights obtained from data manipulation experiments.The expected outcome is a better understanding of (1) the semantic information we can infer purely based on linguistic co-occurrence patterns and (2) the potential of distributional semantic models to pick up linguistic evidence.
The Effect Of Project Based Learning Model Combined With Snake And Ladder Media To Fifth Graders’ Learning Activity And Outcomes In Social Studies

The present study aims to determine the effect of the project based learning model with snake and ladder media to fifth graders’ learning activity and outcome in social studies. The material covered was the Proclamation of Indonesian Independence. The research method used was a quasi-experimental with non-equivalent (pretest and posttest) control group design. The research respondents consisted of 36 students. Data were collected through observation and tests. The research instrument used was observation sheet containing students’ learning activities and a test of learning outcomes that had been validated by experts. Independent samples t-test was used for hypothesis testing. The students’ activity and learning outcomes improvement were determined by the results of the N-gain test. The results showed an increase in activity and learning outcomes in both classes. The experimental class performed higher activity and learning outcomes than the control class. This shows that project based learning model with snake ladder media had a positive effect primary 5 students’ activities and learning outcomes in social studies learning.
Interactive Neural Network: Leveraging Part-of-Speech Window for Aspect Term Extraction (Student Abstract)

Aspect term extraction is a fundamental task for aspect-level sentiment analysis. Previous methods tend to extract noun aspect terms due to the large quantities of them, and perform badly on extracting aspect terms containing words with other POS tags, according to experimental results. In addition, few works focus on the POS tags of adjacent words which are critical to aspect term extraction. We propose a novel model which combines POS and word features in an interactive way, and makes full use of the POS tags of adjacent words by POS window. We conduct experiments on two datasets, and prove the effectiveness of our model.
Determinants of Corporate Social Responsibility Practice of Manufacturing Firms in Dire Dawa Administration

The objective of this research is to find out determinants of corporate social responsibilities (CSR) of mineral water bottling companies in Dire Dawa Administration. Eight water bottlers in the city and four stakeholder organizations were included in the study. Primary data was collected from 308 sample of water bottling companies and 4 managers of the stakeholder organizations. Logit regression model was used to analyze the data with the help of SPSS version 20. The finding of the research showed that community pressure (CP), labour pressure (LP), customer demand (CD) and social license to operate (SL) are significant determinants of CSR, while sustainability practice (SU) is not significant determinant of CSR practice. The study recommended companies should practice CSR that accommodate the interested and influential stakeholder namely community, employee and customers. Keywords: corporate social responsibility (CSR), Dire Dawa Administration, mineral water bottling company, determinants. DOI: 10.7176/DCS/10-6-04 Publication date: June 30 th 2020
Classification of virtual string links up to cobordism

Cobordism of virtual string links on n strands is a combinatorial generalization of link cobordism. There exists a bijection between virtual string links on n strands up to cobordisms and elements of the direct product of n(n − 1) copies of the integers. This paper also shows that virtual string links up to unwelded equivalence are classified by those groups. Finally, the related theory of welded string link cobordism is defined herein and shown to be trivial for string links with one component.
On the Construction of Smart Classroom in Shanghai JiaoTong University

The integration of information technology and teaching puts forward new demands on the teaching environment of colleges and universities, which further drives the reform.How to construct the intelligent teaching environment in colleges and universities in a scientific and reasonable way so as to provide high quality resources, places and facilities for promoting the cultivation of innovative and compound talents?Based on the existing problems of teaching facilities and environment in colleges and universities as well as the needs of transformation, this paper expounds the connotation, construction objectives and contents of intelligent teaching environment, analyzes and explores the construction and application of intelligent teaching environment from eight aspects, and analyzes some controversial issues in the construction of intelligent teaching environment.Wisdom for the construction of the teaching environment to create good teaching and learning space, provide multiple technology facilities and means of teaching, strengthen the construction of teaching resources, to provide online and offline to seamlessly combine the teaching process of services, and promote the education teaching reform, in the process of the development of education informationization deepening constantly advancing with The Times in the practical work experience and thinking is analyzed, in order to play a role of reference for teaching reform.
Web application authentication using ZKP and novel 6D chaotic system

&lt;span&gt;Text password has long been a dominant approach to user authentication used by a huge quantity of Internet services. Web applications are now widely used for the implementation of a range of significant services. The securing of such applications has thus become a significant process. Currently the frequent use of passwords and the need for them make them more vulnerable to theft or guesswork. In the proposed research, the researcher designed an algorithm that has the ability to perform registration or to access web applications safely. The researcher designed an algorithm in the proposed research, which has the ability to securely perform registration or access web applications. The proposed idea based on the notion of Zero-knowledge proof. A complex generation of random number initiated by proposed novel 6D-Hyper chaotic system. The bottom line is that both parties (web application, user), have a secret number. These two numbers used to do the process of registration without requiring a password. Results from the research showed the importance of the proposed method by which the keys were managed and distributed in a safe and effective way.&lt;/span&gt;
Empirical Analysis of Financial Development Institutions and Economic Growth in Tanzania: Based on ARDL Model

Purpose -The financial institutions have a significant contribution to foster economic growth of a country.This study has the intention to investigate the causal relationship between financial development institutions and economic growth in Tanzania.Design/methodology/approach -The study spans from 1989 to 2018.The article uses four proxy of financial development institutions against Gross Domestic Product (GDP) growth (Annual %).Financial proxy of variables of development institutions comprises Broad Money (% of GDP), Domestic credit for private sector (% of GDP), Domestic credit provided by financial sector (% of GDP), and Domestic credit to the private sector by banks (% of GDP).This study uses Autoregressive Distributed Lag (ARDL), Vector Error Correction Model (VECM), and Granger Causality test.Findings -The paper tries to find the cointegration effects and causal relationships between the proxy variables and economic growth in Tanzania.It indicates that the economic growth of Tanzania dominates financial development institutions.Therefore, economic growth in Tanzania leads to the growth of financial sector.The development of financial sector also gives way to rise in overall economic activity.It is a unidirectional relationship through which the proxy variables depend on economic growth in the country.Discussion -Tanzania government should invest more in the financial institutions in order to improve the economic growth in Tanzania..
Effects of Self-Knowledge: Once Bitten Twice Shy

Procedurally generating rich, naturally behaving AI-controlled video game characters is an important open problem. In this paper we focus on a particular aspect of non-playable character (NPC) behavior, long favored by science-fiction writers. Specifically, we study the effects of self-knowledge on NPC behavior. To do so we adopt the well-known framework of agent-centered real-time heuristic search applied to the standard pathfinding task on video-game maps. Such search agents normally use a heuristic function to guide them around a map to the goal state. Heuristic functions are inaccurate underestimates of the remaining distance to goal. What if the agent somehow knew how long it (the agent) would actually take to reach the goal from each state? How would using such self-knowledge in place of a heuristic function affect the agent's behavior? We show that similarly to real life, knowing of one's irrational behavior in a situation can deter the agent from getting into that situation again even if it is, in fact, a part of an optimal solution. We demonstrate the "fear" with a simple example and empirically show that the issue is common in video-game pathfinding. We then analyze the issue theoretically and suggest that "fear" induced by self-knowledge is not a bug but a feature and may potentially be used to develop more naturally behaving NPCs.
Bardic: Generating Multimedia Narratives for Game Logs

In this paper we present the system called Bardic, which was developed over three years as the core technology in the Narrative for Sensemaking project, an effort to automatically generate narrative from low-level event data as an aid for sense making. At its core, Bardic is a narrative report generator that uses a logic-based language torepresent a story based on event/activity logs. Bardic generates different types of narrative discourse designed to convey different aspects of the underlying data. The system consists of aUnity application that allows users to explore generated stories and select parts of the stories to analyze in detail. In addition to its narrative generation capabilities, the application provides visualization and querycapabilities. In this paper we provide screen shots of the application in use, show examples of narrative output produced by Bardic, detail how Bardic generates its output, and discuss the current system's capabilities and limitations.
Time-Aware Uniformization of Winning Strategies

Two-player win/lose games of infinite duration are involved in several disciplines including computer science and logic. If such a game has deterministic winning strategies, one may ask how simple such strategies can get. The answer may help with actual implementation, or to win despite imperfect information, or to conceal sensitive information especially if the game is repeated. Given a concurrent two-player win/lose game of infinite duration, this article considers equivalence relations over histories of played actions. A classical restriction used here is that equivalent histories have equal length, hence time awareness. A sufficient condition is given such that if a player has winning strategies, she has one that prescribes the same action at equivalent histories, hence uniformization. The proof is fairly constructive and preserves finiteness of strategy memory, and counterexamples show relative tightness of the result. Several corollaries follow for games with states and colors.
THE MODEL OF STUDENTS ENGLISH CLUB CONTRIBUTION TO WRITING MOTIVATION AND PUNCTUATION AWARENESS IMPROVEMENT

This work aims to analyze the existence of the English Club Project (ECP) for Vocational Midwifery Students (VMS) by developing an essay in terms of the output program relating to their professionalism. The program facilitates 30 students to build up motivation and awareness of punctuation in additional learning of writing skills. As it is developed from qualitative research, this article is based on the data collected through gathering diversified sources of evidence, organizing a chain of evidence, and having key-informants reviewing draft case study reports. The results present an additional learning model constructed by the representation of the ECP program. ECP is an informally productive community by providing activities and opportunities in increasing the motivation for writing and punctuation awareness. Students begin to understand that doing the writing is uncomplicated anymore after joining the extracurricular programs managed with structured and directed activities. Also, the facilitator provides valuable supports and integrates teaching methods with information and technology devices that made the learning activities quickly done—as for recommendations, strengthening the support to this model is necessary for the sustainability of this additional learning program.
Comparison of Compression Algorithms in text data for Data Mining

Text data compression is a process of reducing text size by encrypting it.In our daily lives, we sometimes come to the point where the physical limitations of a text or data sent to work with it need to be addressed more quickly.Ways to create text compression should be adaptive and challenging.Compressing text data without losing the original context is important because it significantly reduces storage and communication costs.In this study, the focus is on different techniques of encoding text files and comparing them in data processing.Some efficient memory encryption programs are analyzed and executed in this work.These include: Shannon Fano coding, Hoffman coding, Hoffman comparative coding, LZW coding and Run-length coding.These analyzes show how these coding techniques work, how much compression for these coding techniques.Writing, the amount of memory required for each technique, a comparison between these techniques is possible to find out which technique is better in which conditions.Experiments have shown that Hoffman's comparative coding shows a higher compression ratio.In addition, the improved RLE encoding suggests a higher performance than the typical example in compressing data in text.
A New Four-Dimensional Chaotic System and Its Application in Speech Encryption

Traditional encryption algorithms are not suitable for modern mass speech situations, while some low-dimensional chaotic encryption algorithms are simple and easy to implement, but their key space often small, leading to poor security, so there is still a lot of room for improvement. Aiming at these problems, this paper proposes a new type of four-dimensional chaotic system and applies it to speech encryption. Simulation results show that the encryption scheme in this paper has higher key space and security, which can achieve the speech encryption goal.
Environmental Parameters Analysis and Power Prediction for Photovoltaic Power Generation Based on Ensembles of Decision Trees

Due to the influence of solar irradiation, temperature and other environmental factors, the output power of photovoltaic power generation has great randomness and randomness discontinuity. In this paper, a method for analyzing environment data related photovoltaic power generation based on ensembles of decision trees algorithm is studied. Firstly, the characteristics of environmental factors of photovoltaic power generation are analyzed by K-means clustering. And then the corresponding cluster label is assigned. Furthermore, the Radom Forests is combined to build a model. Finally, the method is validated by given data above from a real project. The results show that the proposed method can provide reference for the forecasting of photovoltaic power.
Attribute Noise Robust Binary Classification (Student Abstract)

We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown probability. In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that 0-1 loss (l0−1) need not be robust but a popular surrogate, squared loss (lsq) is. In Asy-In attribute noise model, we prove that l0−1 is robust for any distribution over 2 dimensional feature space. However, due to computational intractability of l0−1, we resort to lsq and observe that it need not be Asy-In noise robust. Our empirical results support Sy-De robustness of squared loss for low to moderate noise rates.