Reviewerly/exHarmony huggingface dataset:
An Analysis on The Students’ Language Learning Strategies in Learning English

&lt;p align="center"&gt;Abstract&lt;/p&gt;&lt;p&gt;This study revealed the students’ language learning strategies in learning English. This study conducted on descriptive design which used quantitative approach to analyze the data. Therefore, the data is collected through Strategy Inventory for Language Learning (SILL) Questionnaire. The populations in this research were 116 students, while 90 of them were used as sample. The researcher uses purposive sampling as the technique sampling to select the sample.&lt;/p&gt;&lt;p&gt;The findings of the language learning strategies used by the students with high level of achievement and the students with low level of achievement are the students with high level of achievement is better than students with low level of achievement in the use of language learning strategies because they have higher average score in the use of six strategies, namely memory, cognitive, compensation, metacognitive, affective and social strategies. The finding of the use of language learning strategies by female and male students are female students is better in the use of language learning strategies because they get higher average score.&lt;/p&gt;&lt;p&gt;The teachers should be equipped with a lot of strategies that they will be able to propose to students so that they can deal with difficult academic task. The teachers should design activities that the students use a variety of strategies and after the completion of the task they should held a discussion session with students about the strategies that they use, whether these strategies proved to be useful or not. In his way, the teachers have opportunity to see the success each students in the use of the strategies, the students will be able to see what their appropriate strategies use. Thus, they will be given the opportunity to make self evaluation, decide which is better for them or learn an alternative way of doing a particular task.&lt;/p&gt;&lt;p&gt;Keyword: Language, Second Language Acquisition, Language Learning Strategies.&lt;/p&gt;
BLOCK CHAIN BASED SECURED DATA SHARING SYSTEM FOR CLOUD ENVIRONMENT

Cloud Storage has become the most preferred storage system for data and file storage since it can be accessed from anywhere across the internet. Even though it has its own perks and advantages, there are some flaws in the storage systems in its security. Cloud security for cloud storage system has intense security mechanism but it does not mean that the security system can be broken. To make sure that does not happen a Multi-user system for access control of datasets stored in an untrusted cloud environment is used. Like any other untrusted environment Cloud storage needs the ability to secure shared information. This approach which proposed provides an access control over the data stored in the cloud without the provider participation. The main tool of access control mechanism is cipher text-policy attribute-based encryption scheme with dynamic attributes. In the proposed system a block chain based decentralized ledger is used. This system also provides immutable log of all meaningful security events, such as key generation, access policy assignment, change or revocation, access request. A set of cryptographic protocols is proposed which ensures privacy of cryptographic operations requiring secret or private keys. Only cipher texts of hash codes are transferred through the block chain ledger. This means that the every events which occurs gets stored in the ledger which gives us the access to know the events which has happened in the storage system, which improves the system, and also the system generates key to the ones who wants to access the data or files in the storage system. This enables a more secure cloud environment where the breaking of the security system is more complicated. Thus this system provides a more secure data sharing system for cloud environment.
Improvements to Measures of Nonlinearity Computation for Polynomial Nonlinearity

In our Fusion 2010 paper, we calculated curvature measures of nonlinearity (CMoN) of a polynomial curve in 2D using differential geometry, Bates and Watts and direct parameter-effects curvatures. The parameter-effects curvatures require the Jacobian and Hessian of the measurement function evaluated at the estimated parameter. Previously we obtained the maximum likelihood (ML) estimate of the parameter $x$ by numerical optimization. In this paper, we present analytic expressions for the ML estimate and associated variance. We show through Monte Carlo simulations that the variance of the estimator and the Cramer-Rao lower bound (CRLB) are nearly the same for different powers of x. We also find that the bias error is small and the mean square error (MSE) is close to the CRLB and variance of the ML estimate. Our numerical results show that the average normalized estimation error squared (ANEES) lies within the 99% confidence interval most of the time, indicating that the variance is consistent with the estimation error.
Hardware/Software Co-verification of DNN for IoT Edge Devices: Leveraging FPGAs

Deep neural networks (DNN) is one of the emerging types of machine learning that is being used to solve problems that are too complex to be solved by humans. However, with the growing complexity of the problems in practical applications today, there are more computations and a need for portability for these DNN. With the utilization of graphics processing units (GPUs) to perform the computations required by DNN now deep learning is becoming ubiquitous. However, the downside is that GPUs are not portable to deploy inference on non-specialized general purpose embedded system designs or internet of things (IoT) applications. An alternative is to use a Field Programmable Gate Arrays (FPGAs) which is an embedded system designed for data-intensive processing and can even be used in low-power battery operated devices. FPGAs can achieve a high level of performance at a lower cost of power. In this work, a complete methodology is shown to deploy inference phase of DNN. One of the most popular DNN architecture known as Convolutional Neural Network (CNN) has been implemented on a FPGA board known at PYNQ-Z1 using the PYNQ platform to execute the CNN. Our methodology takes the advantage of Python overlay provided for PYNQ-Z1 to easily debug and verify the CNN-IP generated by regular syntheses tools such as Xilinx’s Vivado.
Unified framework of Explainable AI to enhance classifier performance

Deep learning image classifiers are extensively used in document processing, activity monitoring, object recognition and separations etc. However, even the best classifiers are not free from errors. It would be very helpful if the errors that are pumped in to the system due to the classifier decisions are reduced. The framework comprises of heat map generation, attribute generation, text explanation generation and activation.
GENERAL ARTIFICIAL INTELLIGENCE MODEL FOR DEVELOPING ADAPTIVE NON PLAYABLE CHARACTERS IN COMPUTER GAMES

In 2005, through inauguration of the annual AAAI General Game Playing Competition the artificial intelligence (AI) community was introduced to AI in computer games. Since then, the efforts have been made to make AI systems understand the rules of arbitrary computer games without any human help. This paper will introduce a new model based on neural networks and genetic algorithm concepts which has the potential to replace behavior tree model currently used by game developers for making AI systems. This model introduces the possibility for AI systems to be trained in the game instead of creating complex nested if-else tree to determine the next move. This adaptive learning approach for AI systems could potentially lead to development of more powerful and engaging AI systems known as Non playable characters in gaming industry. The model demonstrated in this paper is developed using Python 3 language and the game environment in which it is tested are developed in Python 3 environment as well.
Fit for Play: Developing an Adaptive Exergame Platform to Motivate Inactive Children

Abstract: Sedentary behaviour in children, four years of age and older, has increased over the last decades. These children become physically less skilled, which demotivates them for regular sports activities. They become susceptible to health risks such as obesity and have a heightened chance to develop depression and a lower self-esteem. Sports professionals acknowledge that these children in time become unable to keep up with the sports education pace, leaving them prone to social exclusion as well. Exergames seem promising in their potential to increase the amount and quality of physical exercise in this group. Moreover, they offer strategies to motivate children to a more active and healthier lifestyle. However, some issues remain unclear regarding their applicability and individual fittingness. For one thing sports professionals have little to no experience using exergames in physical education, let alone understand which games could be appropriate to structurally activate said children. In addition, existing exergames regularly lack a suitable degree of adaptivity regarding what a child is physically capable of, which psychological needs should be addressed, and to what inactive children find appealing in terms of gameplay. The aim of our research project is to build a first prototype of an adaptive platform for exergames to motivate inactive children to structurally engage in physical exercise more, and better. The participative design method we used in our preliminary qualitative research led to a better understanding of the barriers to move and the psychological needs children have when it comes to physical exercise. We made a first global list of requirements for the adaptive platform and an overview of necessary design directions. Future pursuits in this project include a participative design research study amongst both children and sports professionals, and a thorough review of the literature and state of the art knowledge. We will use this knowledge to create a first prototype of an adaptive platform in collaboration with a serious game company and an organisation of sport professionals. After user testing we will use the evaluation findings as a baseline for future measurements regarding the adaptation of suggested exergames and to formalize and disseminate found design guidelines.
Good practies using innovative learning methods

The present article aims to present to a wider audience selected in-depth examples of practice that have been addressed and applied in contemporary education. Using experience as an exchange of ideas for good practice from other countries gives a clear idea of training and the learner development plan. The factors contributing to success in students' theory and practice are emphasized . All these factors and elements of good practice are interlinked to achieve sustainability and sustainability in all areas of education / leadership, vision and incentives are key at all levels.The main problems faced by students during their transition to the transition are finding the right balance between their theoretical and practical training. The effectiveness of the entire educational and educational system depends on it.The analysis of the results obtained from the mentioned examples of good practice in education shows that the use of one type of method is not sufficient for the correct perception of the curriculum by the students.
Fadhil J. Chalabi (1927–2019): A remembrance

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Optimal Bayesian Classification

Since prior knowledge is required to obtain a good error estimate in smallsample settings, it is prudent to utilize that knowledge when designing a classifier. When large amounts of data are available, one can appeal to the Vapnik–Chervonenkis (VC) theory (and extensions), which bounds the difference between the error of the best classifier in a family of classifiers and the error of the designed classifier as a function of sample size, absent prior distributional knowledge, but this VC bound is generally useless for small samples. Rather than take a distribution-free approach, we can find a classifier with minimum expected error, given the data and an uncertainty class of feature-label distributions. This is accomplished in the framework of optimal operator design under uncertainty.
Path planning of manipulator based on acceleration and deceleration control of two-phase adaptive path

Abstract In this paper, the key technology in NC system, acceleration and deceleration control, is applied to path planning of manipulator. A two-phase adaptive path acceleration and deceleration control method is proposed, which reduces the requirement of S-shaped curve acceleration and deceleration control method on the shortest path length, and improves the smoothness of trapezoidal acceleration and deceleration control. On the whole, it improves the rapidity of accurately reaching the target position and the smoothness of manipulator’s movement.
Eyeball Movement Based Wheel Chair Using RaspberryPi

We have been seeing many people who are fighting against many diseases. One of the most painful disease is Paralysis. All of us think of supporting the people in need either financially or physically. But being an engineer we can design a new instrument or machine that helps needy. This project is one such attempt to contribute our part to the society. Paralysis is one of the biggest curse to mankind. The person can neither feel nor move himself. In extreme cases of paralysis the person needs the support of another guy even for moving his finger. Paralysis is of many types, the 4 main categories are , Monoplegia (one arm or leg), Hemiplegia(one arm & leg),Paraplegia(both legs),Quadriplegia(both legs & arms) In worst case paralysis(Quadriplegia)the person could move only his eyes. The head movement or voice based wheelchairs will not hold good in that situation. So an eyeball movement based wheelchair would help the best for those people. This would be more accurate when compared to other automated wheelchairs because in voice controlled wheelchair ,addition of noise due to outside environment leads to inaccuracy .For head movement based wheelchair human effort is required. So an iris controlled or eyeball detection wheelchair would be the best option. We implement this using Raspberrypi (with OpenCV Python),camera and driver motors
Nervo: Augmented Reality Mobile Application for the Science Education of Central and Peripheral Nervous Systems

Due to high comprehension and knowledge retention demands, most children find it difficult to learn Science topics. From the focal points to the complex structure of the human body, human anatomy is one of the hardest subjects to learn. Because of the complexity of the structure and function relationships within these systems, the Central and Peripheral Nervous Systems are the most difficult and strenuous systems to learn out of all human body systems. Nervo is a mobile application employing Augmented Reality and Machine Learning that is designed to become a supplementary learning resource for elementary students learning the nervous systems. Through its learning outline and integrated engaging features, it aims to immerse its users in an interactive and effective learning experience. The mobile application is developed in collaboration with Cebu Institute of Technology-University Elementary Department. According to the results, the application has successfully met the minimum user requirements and is now available on Google Playstore.
Le parole sono ponti: risorse digitali per l'integrazione in contesti multilingui

Learning the language of the hosting country is a key factor for the integrationof people coming from abroad but this objective could be achieved only if politicalinstitutions promote initiatives in this direction. Researchers in the fieldcan cooperate, providing their expertise. In this paper we present two researchactivities concerning the use and the production of multilingual resourcesdeveloped at the Institute for Computational Linguistics “Antonio Zampolli”.More specifically, we report on the creation of a multilingual glossary of difficultterms extracted from forms that people submit to the municipality ofGenova to access public services. We also describe the experimental use in aprimary school of ImagAct, a multilingual lexical resource focused on actionverbs consulted by pupils to create a vocabulary of food preparation in Italian,English, Chinese and Spanish.
Defining and identifying the optimal embedding dimension of networks

Network embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension -- small enough to be efficient and large enough to be effective -- is challenging but necessary to generate embeddings applicable to a multitude of tasks. Unlike most existing strategies that rely on performance maximization in downstream tasks, here we propose a principled method for the identification of an optimal dimension such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. Estimated values of the optimal dimension in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.
Implementasi Data Mining dalam Pemilihan Bahasa Pemograman dalam Penyelesaian Tugas Akhir Mahasiswa

One of the things that can be used as a measure of one's success in taking education at Dumai STMIK is the final project. Final Project is a task that must be done by students to complete their undergraduate education. In completing the Final Project students must be able to make an application program related to the problem under study. But the problem is that there are students at STMIK Dumai choosing a programming language that is not right with the Final Assignment Scheme, resulting in the length of the process of making the final project. With the implementation of the selection of programming languages in the completion of students' Final Assignments by applying data mining using the rough set method, it is expected to provide an appropriate solution in completing the final assignments of students to be on time
Detecting Anomalies in Time-Varying Media Crime News Using Tensor Decomposition

Nowadays, the mass media surround us in many forms. Newspapers, radio and TV reports about many topics, including the crime committed in a region. Indirectly, the media provide statistics about crime incidents, and policymakers could focus their attention on the unusual number of crime news (c.f., regular events) for evaluating and proposing new public policies. In the present work, the Tensor decomposition is used to detect an unusual amount of crime news. To achieve this goal, two rejection criterion techniques were compared. Also, several image binarization techniques were used to validate our proposal. Our result can be used to detect an unusual amount of crime news as a proxy of unusual crime activity.
Content Based Image Retrieval Using Boosted Local Extrema Co-occurrence Pattern

This paper presents the Boosted Local Extrema Co-occurrence Pattern (BLECoP) which focuses more on the local structures by enhancing the local extrema pattern. The proposed method can effectively combine with structural texture, GLCM and HSV color space. BLECoP tested over the standard Corel databases, which is extensively used in the Content-Based Image Retrieval(CBIR) domain. Experimental results show that BLECoP outperforms other state-of-the-art descriptors that are recently proposed in CBIR research using color and texture features. The performance of the proposed descriptor’s image retrieval results is achieved best accuracy rates, that are remarkable on heterogeneous(non-flat) images. Further BLECoP can be effectively used to retrieve more accurate images in a very large scale image databases.
A cost model of communication in distributed computations in tactical environment

One needs a good communication cost model to understand how best to optimize the off-loaded computation in a tactical environment. In practical scenarios, complications also arise from the in-band and out-of-band channel congestion interference. This can happen due to the intentional and/or unintentional adversary equipment and will affect both the nature of algorithms and the sequence of steps in their execution. As a first step towards solution of the above problems, we present a model for a multi-node tactical network with resource constraints with and without presence of some adversary nodes.
Hostage Situation And Its Resolution In Ethiopia

Purpose of the study: One of the modus operandi of criminals such as terrorist, emotional, mentally ill person to achieve their need is held person hostage, especially government officials, investors, and tourists. The police also used to force to secure peace and security, but now the police use the hostage negotiation team to save the life of hostage-taker and hostage. These hostage-takers take innocent hostage persons as means of negotiation, which may terrorize everybody, such as an investor, tourist, and vulnerable group, which have a negative impact on the development and peace of the country. Additionally, the right to life is the mother of other human rights. The purpose of this paper is to evaluate the practice of hostage situation and its resolution&#x0D; Methodology: Data were collected through document review and interviews of police and victim, who were selected purposively since they have direct relation. This is desktop research and descriptive design. The data were collected through document review and media review. The collected data were analyzed thematically.&#x0D; Finding: The result of the analysis data shows that there is a hostage situation in Ethiopia, and the response of the police is the use of force than the negotiation team.&#x0D; Application: The police should establish a hostage negotiation department with professional negotiators. The governments also should enact negotiation policy and strategy in Ethiopia.&#x0D; Novelty/Originality: There is a hostage situation, and its resolution lacks clear guidelines in Ethiopia, and nobody studied it. Therefore, this study may use as reference material for students'; the government may use it as input for policy and lawmakers.
Optimal Bayesian Error Estimation

Given that a distributional model is needed to achieve useful performance bounds for classifier error estimation when using the training data, a natural course of action is to define a prior distribution over the uncertainty class of feature-label distributions and then find an optimal minimum-mean-squareerror (MMSE) error estimator relative to the uncertainty class (Dalton and Dougherty, 2011b).