Reviewerly/exHarmony huggingface dataset:
Building a Community of STEM Educators in Nigeria Using the TeachAKid2Code Program

Research studies show insufficient number of STEM educators needed to meet the young population in Nigeria’s STEM skills needs. This paper outlines the design of a learning and teaching framework using online technologies for STEM volunteers to enable delivery of STEM outreach activities across 7 states in Nigeria. The study addresses the barrier of insufficient number of STEM educators and lack of access to meet the demand of training young people requiring STEM skills. Using a constructionist approach and a connectivism learning theory, the study adopts a combination of a MOOC model and direct instruction using online resources to teach and train volunteers to deliver and facilitate STEM outreach activities to young children in primary and secondary schools in Nigeria. 215 volunteers were trained and 69 STEM volunteers were successfully paired in 63 schools to deliver STEM outreach activities to 5252 young people over the course of 3 months.
Innovation of Elearning at Comenius University

Just like at many other universities in Slovakia, Comenius University has mostly been using the elearning server as a storage for electronic materials, as opposed to a learning tool. Within the development project, Education for Information Society (002UK-2-1/2018), we have an opportunity to change our approach. This article summarizes the current elearning situation at Comenius University, a survey conducted among our teachers and administrators, ideas gained from literature, plans to proceed and the reasoning behind them.These instructions give you basic guidelines for preparing camera-ready papers for conference proceedings.
Posterior Cramér-Rao Lower Bounds for Extended Target Tracking with Gaussian Process PMHT

In practical target tracking scenarios with high-resolution sensors, targets often appear as extended targets with irregular and arbitrary shapes. In this paper, the posterior Cram&#x00E9;r-Rao lower bounds (PCRLB) for extended target tracking with a Gaussian Process (GP) measurement model is derived to quantify the achievable accuracy of estimates of multiple extended target states within the Probabilistic Multi-Hypothesis Tracker (PMHT) framework in scenarios with clutter. Simulation results verify the effectiveness of the proposed PCRLB.
Optimal U-Net Architecture for Object Recognition Problems in Multispectral Satellite Imagery

Geospatial data follows Moore's law. On the back of improvements of optical Earth observation satellite hardware [1] (weight, propulsion systems, signal transmission and resolution) as well as reduced costs of rocket launch carrying these satellites, number of nanosatellites deployed to Lower Earth Orbit (LEO) in 2018 was larger than in the previous 10 years combined [2]. This allowed an exponential growth in satellite imagery data production that is available for military and commercial use. Machine learning tools enable us to process high resolution, multi-spectral satellite imagery data to recognize objects at scale [3] and generate insights with practical industry applications. It allowa us to calculate global oil reserves, track tanker ships or estimate retail revenue based on the car count, all exceptionally valuable financial information. In this paper, we investigate various computer vision techniques to develop an optimal machine learning technique for object recognition problems at this unique type of the dataset: multi-spectral satellite imagery.
Determining NPC Behavior in Maze Chase Game using Naïve Bayes Algorithm

In this paper, we propose an implementation of Naïve Bayes algorithm in a chase game called Maze Chase. Maze Chase is a chase game where a player must avoid several chasings Non-Player Character (NPC). In our proposed implementation, the NPC will run automatically using artificial intelligence. There are four NPC in Maze Chase, each with its own characteristics. Because of the characteristic differences, the four NPC needs to communicate with each other. For communication we use a multi-agent system. Multi-agent system is a part of artificial intelligence which were used by NPC to communicate with each other using several defined parameters. We used several parameters, such as the number of coins in a zone, the amount of golden coins in a zone, and the centroid values. These parameters were used as variables for an implementation of Naïve Bayes algorithms. Our proposed implementation of Naïve Bayes was used to count the probabilities of NPC behavior, which will move closer towards the player according to several zones in the game map. From the testing results, Naïve Bayes algorithm could be used to decide the NPC movement according to its target zone on the Maze Chase game, with error rate 0.5%.
Tracking of a Missile Using Interacting Multiple Model

After thrust force of missile is off, many debris are created. The debris interferes with tracking the missile. Therefore, it is difficult to track the missile with a single Kalman filter. In this paper, we used Interacting Multiple Model (IMM) to solve the problem. Combining multiple models means that it considers many possible motions of missile, and it makes estimation accurate. This method is simulated with true trajectory derived from Modified Point Mass Model (MPMM). It is shown that proposed method improves estimation error.
In praise of partially interpretable predictors

Abstract Often there is an uninterpretable model that is statistically as good as, if not better than, a successful interpretable model. Accordingly, if one restricts attention to interpretable models, then one may sacrifice predictive power or other desirable properties. A minimal condition for an interpretable, usually parametric, model to be better than another model is that the first should have smaller mean‐squared error or integrated mean‐squared error. We show through a series of examples that this is often not the case and give the asymptotic forms of a variety of interpretable, partially interpretable, and noninterpretable methods. We find techniques that combine aspects of both interpretability and noninterpretability in models seem to give the best results.
Based on the Artificial Intelligence Technology in Higher Vocational English Major Students After-School Tutoring System Design Research

In recent years, China has increased its investment in the information industry, and concepts such as Internet and cloud computing continue to attract widespread attention. The rapid development of computer technology as a result has brought convenience to our lives, especially in the education industry. The traditional English teaching in higher vocational colleges has always been inadequate. With the promulgation of relevant government policies, higher vocational colleges have gradually promoted the process of informationization of English teaching, and the application of artificial intelligence has brought a new way of English teaching in higher vocational colleges. In this context, based on the artificial intelligence technology and the curriculum goals as the foundation, combining to achieve higher vocational English teaching quality for the purpose, with the method of literature analysis combined with field survey to sorting and analysis of related research, and then try to design in higher vocational English major students after-school tutoring system, and study its specific conditions and environment, to promote higher vocational English major students’ English learning level, the optimization of high vocational English teaching way and the effect.
Efficient Majority-Logic Reed-Solomon Decoders for Single Symbol Correction

A new low-complexity method to decode single symbol correction Reed-Solomon codes is proposed in this paper. This decoding algorithm takes advantage of the equivalent parity-check matrix representation to apply majority logic techniques that avoid the needs of computing Galois Field inversions, divisions and logarithms, unlike previous efficient solutions. The derived architectures allow to increase the order of the Galois Field, keeping similar area and delay results for the same message length. Hence, it is possible to configure the burst error capacity without compromising the decoder performance. Finally, due to the three-step procedure of the decoder: syndrome, magnitude estimation and majority logic, the decoding latency is reduced to two clock cycles without compromising the critical path improving latency between two and five times. The proposed decoder can obtain an area reduction of at least 44% for codes with high Galois Field order, i.e., GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ). The high level of customization in terms of data word length and the high frequency and low area make these decoders suitable for a wide range of storage systems.
Monte Carlo wavelets: a randomized approach to frame discretization

In this paper we propose and study a family of continuous wavelets on general domains, and a corresponding stochastic discretization that we call Monte Carlo wavelets. First, using tools from the theory of reproducing kernel Hilbert spaces and associated integral operators, we define a family of continuous wavelets by spectral calculus. Then, we propose a stochastic discretization based on Monte Carlo estimates of integral operators. Using concentration of measure results, we establish the convergence of such a discretization and derive convergence rates under natural regularity assumptions.
Comparative Study of Moments Shape Descriptors and propose a new hybrid Descriptor technique

The shape is a significant visible feature of an object in images. Finding images by using shape features has gained much consideration. For precise computerized classification of the type of object in the image, different features extraction approaches may be used. To identify and determine the preferences among them, three different techniques are studied extensively and determined how it affected by the change which may happen to the different shapes with the proposal of a hybrid method that contains the robust descriptors of each technique which improves the results and provide strong features for shape description that achieves high accuracy reaching more than 90%.
Making Sense of Search: Using Graph Embedding and Visualization to Transform Query Understanding

We present a suite of interfaces for the visual exploration and evaluation of graph embeddings -- machine learning models that reveal implicit relationships not directly observed in the input graph. Our focus is on the embedding of navigation graphs induced from search engine query logs, and how visualization of similar queries across different embeddings, combined with the interactive tuning of results through multi-attribute ranking and post-filtering (e.g., using raw query frequency or derived entity type), can provide a universal foundation for query recommendation. We describe the process of technology transfer from our applied research team to the Microsoft Bing product team, examining the critical role that visualization played in their decisions to ship the technology on bing.com.
Features Selection for Classification of SMILES Codes Based on Their Function

The classification of the active compound based on their function is important to be done because most of them are unknown their function. The structure of the active compound can be represented by SMILES code that unique, compact and complete. Preprocessing SMILES code is a crucial task before SMILES codes are classified. In this research, preprocessing is extracting SMILES code into several features. The features must represent patterns or information from SMILES codes because the proper features of the SMILES codes will increase the accuracy of classification results. This paper uses features from SMILES codes directed by an expert and based on the previous research. Features will be normalized and are classified by an efficient and good classification method, Extreme Learning Machine (ELM). The experiment results show that first, adding features will increase the average of the accuracy of the system until 10.9% on dataset 1-3-4 (nerve-bacterial-cancer). Second, ELM is superior to SVM and KMNB in terms of both accuracy and processing time.
Exploring Graphic Sciences

If we analyze the physiognomy of knowledge in the field of graphic representation, we have to face a sort of diaspora that throughout history has dispersed its contents in various disciplinary fields and that has prevented its coagulation within a unitary and universally recognizable disciplinary apparatus, which instead can only be found within particular national and cultural contexts. The disciplines that explore graphic representation are not always identifiable in the panorama of international research because they assume various denominations in the different cultural traditions. Starting from these premises, in this article we want to recompose the elements present in the international panorama of research in the field of studies focused on the production of images that can be expressed through the expression Graphic Sciences. According to this hypothesis, the Graphic Sciences would not be configured as a single discipline with monolithic methods and univocally and preventively determined objectives, but rather as a set of fields of study with a nonunitary repertoire of interests.
Improving Japanese-English Bilingual Mapping of Word Embeddings based on Language Specificity

Recently, cross-lingual word embeddings have attracted a lot of attention, because they can capture semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform word embeddings space from one language to another. In this paper, we propose an advanced method for improving bilingual word embeddings by adding a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of Japanese language. On a benchmark data set of JapaneseEnglish bilingual lexicon induction, the proposed method achieved competitive performance compared to the method using a single mapping, with better results being found on original Japanese words.
An Agent-Based Model of Urban Pigeon Swarm Optimisation

Swarm intelligence algorithms are designed to find approximate solutions to optimisation problems emerging from the individual contributions of members of a population, and hence they can be naturally interpreted within the framework of agent-based models. This paper takes a recently introduced numeric optimisation swarm method inspired in the foraging behaviour or urban pigeons and reports a reworking of the algorithm as an agent-based model. The practicality of the model is tested on a set of benchmarks suitably adapted to a discrete search landscape, and the impact of the model parameters is investigated by exhaustive empirical simulations. To further validate the model, we compared it to an existing agent-based model of the standard Particle Swarm Optimisation, a widely-known swarm intelligence method. Results indicate that effectiveness and efficiency of both models are comparable, and moreover, that the agent-based framework was useful to discover new insights about improving the behaviour of the pigeon-inspired method.
Effectiveness of POS Tagging in Graph Based Sentiment Analysis Model

In the recent times, deriving high quality information from the ever increasing amount of text data is highly desirable in many application domains. Analysis and summarization of review data is one such domain which demands an effective sentiment analysis technique. This paper proposes an efficient sentiment analysis model while establishing the importance of POS tagging in sentiment analysis. The model makes use of a graph based keyword extraction and domain specific polarity assignment technique. The proposed model outperforms few already established techniques for three review datasets.
DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data

SPARQL is a W3C standard for querying the data stored as Resource Description Framework (RDF). The SPARQL queries are represented using triple-patterns, and the querying process searches for these patterns in given RDF. Most of the existing SPARQL evaluators provide centralized, DBMS inspired solutions consuming high resources and offering limited flexibility. To deal with the increasing size of RDF data, it is important to develop scalable and efficient solutions for distributed SPARQL query evaluation. In this paper, we present DISE - an open-source implementation of distributed in-memory SPARQL engine that can scale out to a cluster of machines. DISE represents the RDF graph as a three-way distributed tensor for querying large-scale RDF datasets. This distributed tensor representation offers opportunities for novel distributed applications. DISE translates the SPARQL queries into Spark-tensor operations by exploiting the information about the query complexity and creating a dynamic execution plan. We have tested the scalability and efficiency of DISE on different datasets. The results for this new representation based querying have been found scalable, efficient and comparable to a related approach.
Investigation of Effect of Noise Parameter on Brain MR Image Restoration

The effect of noise parameter, standard deviation, on Brain MR Image restoration was investigated in order to select the filtering mechanism to get exact restoration. It is a well known fact that the noise in Brain MR image is Rician distributed, but if the standard deviation is between 4.51 to 4.28, the noise is neither Rician nor Gaussian; however when standard deviation is below 4.28 the noise behaves as Gaussian distributed. For accurate restoration, knowledge of this threshold value of σ, where noise characteristics changes, is essential so that corresponding filter can be chosen to remove the noise present in the image. It was found that the characteristics of noise distribution are differentiated on the basis of filtering competency of linear and non-linear filters. Gaussian filter is efficient at low standard deviation, σ <; 4.28 whereas standard deviation σ ≥4.28 Hybrid filter works well; this is verified using Structural Similarity Index measurement (SSIM),Mean Square Error (MSE) and Peak Signal to Noise Ration (PSNR). Hybrid filter is nothing but a series combination of Gaussian and median filter. The performance of Hybrid filter was also compared with Homomorphic filter, which works best for Rician distributed images, and thus verified that Hybrid filter works best for all three types of distribution over given range of standard deviation.. The proposed algorithm was implemented on synthetic images from BrainWeb data base using MATLAB 2015a.
Developing Electronic Learning Media Using 3d Pageflip On The Material Of Classification Of Living Thingsfor the 7th Grade Students Of Junior High School

Abstract The aim of this research is to develop the electronic learning media using 3D Pageflip on subject matter of Classification of living things for the Seventh Grade of Junior High School. Based on observation in several junior high schools in Jambi district, it is found that the subject matter about Classification of living things is difficult to understand and boring due to many organism in world that must be studied and every organism have the specific characters and it usually uses Latin. One of the solution of the problem is to develop the electronic learning media using 3D Pageflip. The 3D Pageflip is the software that is completed with several learning material like: power point, students worksheet, video, animation, pictures, photos. Research and development was designed by using ADDIE’s model following five steps namely: Analyze, Design, Develop, Implementation and Evaluation. The qualitative data was obtained from validator suggestions, and the quantitative data was collected through the trial test of the seventh grade students of Junior high school. This media was validated by the experts. The result of the design obtained the score of 70 (93%) with very good category. The result of content obtained the score of 41 (82%) with very good category. The suggestions from validators were used to revise the learning media. The trial test was also carried out to the biology teacher of Junior High School 7 Muaro Jambi and obtained the score of 60%, that is categorized to very good. The students conveyed that the media is appropriate to be used for the seventh grade students, in which obtained the score of 3.92. In conclusion, the electronic learning media in the subject of Classification of living things is very good and appropriate to be used for the seventh grade of Junior High School students.
One v/s All SVM Implementation for Keystroke based Authentication System

Authentication systems have laid the foundation for validating and securing user's identity. Due to increasing vulnerabilities, the traditional methods like passwords, PINs, tokens etc. cannot keep up with the challenges. The behavioural biometrics like Keystroke dynamics is used to authenticate a legitimate user through typing patterns. In this paper, we propose a machine learning approach to develop an authentication system that provides more robust user identity information using keystroke dynamics biometrics. One-v/s-all classification is applied to 51 users of CMU dataset. For optimization, the SVM classifier is used along with Grid Search for parameter tuning. Grid Search satisfies the goal of choosing the best kernel parameter pair. Experimental evaluation in a heterogeneous environment yields a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 10.24%. The graphical representation of the result is expressed through ROC (Receiver Operating Characteristic) curve which shows the increased reliability of the system for authentication purpose.
A Genetic-Frog Leaping Algorithm for Large Dataset Document Clustering

This study describes the development of a text document clustering optimization model using a novel Genetic Algorithm-Shuffled Frog Leaping Algorithm (GA-SFLA), which clusters text documents based on selected features in an efficient way. In the approach proposed in this study, a Genetic Algorithm (GA) handles the feature selection task, while a Shuffled Frog-Leaping Algorithm (SFLA) handles the clustering task. The effectiveness of the proposed approach was evaluated by testing it on the popular "20Newsgroup" text document dataset. After multiple experiments, it was found that using GA-SFLA on the 20Newsgroup dataset considerably helped to enhance the text document clustering task, compared to classical K-means clustering. In addition, the feature selection stage greatly contributed to improving the results of clustering. Nevertheless, this improvement comes at the expense of longer computational time.