Use of Analogies in Science Education, a Systematic Mapping Study

Hernandez Pedro and Espitia Edinson

ABSTRACT

This systematic mapping study consisted of tracking the scientific literature that addresses the issue of analogies as a didactic strategy in science teaching. An analogy can be understood as comparing an existing knowledge with a new knowledge to achieve a better understanding of the new knowledge as a result of the comparison of similarities; or in other words, use students own concepts to introduce new concepts using comparisons between the two. The purpose of this study was to identify, analyze, synthesize and evaluate research works that touched on this topic, with this, to have knowledge about the models of uses of analogies, most used didactic strategies, research methodologies in this field and how to evaluate the learning effectiveness of working with analogies. The methodology that was used is the systematic mapping study; Five questions were posed that guided the information tracking process. Later, the electronic documents in English for the last twenty years were traced in five databases related to the educational field. Finally, it is concluded by responding to the purpose of the study where it is evident that, broadly speaking, the research methodologies in this field are quantitative as well as qualitative, to implement analogies, resources such as images, illustrations, textual indications and audiovisual aids are used, it is usually evaluated the effectiveness of using analogies with multiple choice tests, oral tests of creating analogies by students.

KEYWORDS

Analogies, science teaching, analog model.

Delivering Higher Education in Public Health Emergencies: Lessons from the COVID-19 Pandemic crisis in Sierra Leone

Thomas SONGU,Osman SANKOH, Andrew BAIMBA, Njala University, Sierra Leone

ABSTRACT

With the general closure of universities and schools in Sierra Leone due to the COVID-19 pandemic crisis, digital learning initiatives become credible alternatives to maintain students and pupils in educational, training, and research links. This paper provides evidence of distance learning modalities implemented in Sierra Leone during and beyond COVID-19 emergencies. This paper responds to three primary questions: This paper will respond to three primary questions: What digital learning modalities are shown to be most effective in Sierra Leone for providing continuity in learning amid temporary or permanent school closures? What digital learning modalities are promising but lack evidence? What are the challenges and considerations when planning for and implementing digital learning? This paper also provides snapshots of the different distance learning initiatives and programs that have been implemented in Sierra Leone, including both the promises they offer and the challenges they pose.

KEYWORDS

COVID-19 Pandemic, Digital Learning, e-learning, Higher Education, Sierra Leone.

Adoption of Online Learning amongst Undergraduate Students of Public Universities in Kenya amid Corona Virus Pandemic. A Case of University of Nairobi-Kenya

Ouru John Nyaegah, Lecturer & Coordinator School of Open and Distance Learning, University of Nairobi, Nairobi, Kenya

ABSTRACT

Education is very crucial in the development of the individual and society and it cuts across different stages of human growth and development with specific and achievable goals. The purpose of this survey was to establish the extent to which Corona Virus Pandemic influenced adoption of Online learning among undergraduate students at Nakuru and Kisii Learning Centers of the University of Nairobi. The study came up with five objectives which focused on learners’ attitudes towards adoption of virtual learning, how they applied online platforms to learn and how they coped up with online challenges. The study used 232 students while Spear-man Coefficient of Correlation Formula was applied to establish research instruments’ reliability. The study established that, despite the university ensuring online learning, students faced poor internet connectivity, and unfavorable study environments. Strategies were urgently needed in order to build a Resilient Education System to enhance learning in the university.

KEYWORDS

Pandemic, Cessation of movement, Lockdown, Online Platforms, Resilient Education System.

Automatic Computing Communication:A New Perspective Algorithm to Cognition for Automatic Computer Communication Systems

Bita Bayat, Department of Computer Engineering, Azad University, Safadasht, Tehran, Iran

ABSTRACT

The aim of this paper was to optimize the system and the method of identifying communication systems and evaluating the scope of system communication. Algorithmic technique was used to simulate the article. The name of the data set was a Mehr Bank data set in Iran with the number of connection routes of 80 cases and the prediction of 2 models (optimal and distorted). The algorithms used included a combined neural network and genetic algorithm, support vector machine (SVM). In the results of the research, we showed that in relation to the reduction of the cases of distorted route data and the increase of optimal routes, the accuracy of detecting the routes of connection to bank users in optimal routes is increasing. Using a combined neural network and genetic algorithm, the backup vector machine improves the accuracy of detecting connection paths to bank users. By recognizing the information, the system proposed in this paper can transfer less data when transferring data with the same amount. Using two types of algorithms to explain the level of accuracy and power of algorithms in identifying and monitoring the connection paths of inter-system communication. The algorithms used included a combined neural network and genetic algorithm, support vector machine (SVM). Examination of the ability of each of the hybrid algorithms The combined neural network and genetic algorithm and support vector machine (SVM) showed that in the major items of classification and identification of interconnection pathways and their identification, the neural network and genetic hybrid algorithm is more successful. And the percentage of identification and classification of this algorithm in order to identify computer communication systems was higher than the support vector machine (SVM).

KEYWORDS

Combined Neural Network and Genetics Algorithm, Support Vector Machine, Communication Systems, Connection Paths.

The Colored Disconnection Numbers of Cellular Networks and Grid Networks

Xuqing Bai and Xueliang Li and Yindi Weng, Center for Combinatorics and LPMC, Nankai University, Tianjin 300071, China

ABSTRACT

Let G be a nontrivial edge-colored connected graph. An edge-cut R of G is called a rainbow-cut if no two of its edges are colored the same. An edge-colored graph G is rainbow disconnected if for every two vertices u and v of G, there exists a u-v-rainbow-cut separating them. Such an edge coloring is called a rainbow disconnection coloring of G. For a connected graph G, the rainbow disconnection number of G, denoted by rd(G), is defined as the smallest number of colors that are needed in order to make G rainbow disconnected. Similarly, there are some new concepts of graph coloring, such as proper disconnection coloring, monochromatic disconnection coloring and rainbow vertex-disconnection coloring. In this paper, we obtain the exact values of the rainbow (vertex-)disconnection numbers, proper and monochromatic disconnection numbers of cellular and grid networks, respectively.

KEYWORDS

edge-(vertex-)coloring, connectivity, rainbow edge- (vertex)-cut, (strong) rainbow (vertex-)disconnection numbers, proper and monochromatic disconnection numbers.

Automatic Data Segmentation in Human Activity Recognition

Seyed Modaresi^1,2, Aomar Osmani¹, Mohammadreza Razzazi², Abdolghani Chibani³, ¹Sorbonne Paris Nord University, ²Amirkabir University of Technology and Institute for Research in Fundamental Sciences (IPM), ³University Paris-Est Creteil

ABSTRACT

Internet of Things (IoT) generates a long and heterogeneous series of data. It is particularly the case with human activity recognition. Segmentation is a common bias used to divide this long (may be infinite) data stream into a set of smaller meaning-full finite segments to have a more straightforward model. It is often defined by researchers using their prior knowledge and therefore adds uncontrollable biases in their models. In this paper, we define the segmentation as a particular case of a general data-decomposition problem. Therefore, we formalise this problem as an hyperparameter in order to control the added biases and to optimize the segmentation process for a given task to solve. The impact of the biases should be described and evaluated in the data decomposition step, the problem resolution (ML) step, and in the composition (the connection between ML results, segments and the global problem results) step. In addition, our formalism leads to select dynamically an appropriate segmentation method independently as an hyper-parameter from the considered application that reduces, by the way, the implicit added biases. Intensive experiments on several public datasets show the effectiveness of this original approach.

KEYWORDS

Activity Recognition, Segmentation, Data-Decomposition, Complex Event Recognition, IoT.

A System for Supporting Anomaly Detection in Government Data using Multiple Classifiers

Rafael A. Spíndola and Tiago M. U. Araújo, Computer Center, Federal University of Paraiba, João Pessoa, Brazil

ABSTRACT

With increasing amounts of data to be analyzed and interpreted, Anomaly Detection emerges as one of the areas of great impact in the context of Data Mining. Its applications extend to the most diverse fields of human activity, notably in medicine, administration, information science, economics and computing. In this work, we propose a support system for detecting aberrant events in stationary databases from Public Administration. The solution combines multiple supervised and unsupervised detection algorithms (OCSVM, LOF, CBLOF, HBOS, KNN, IForest and Robust Covariance) to classify events as anomalies. The results showed that, of the total events returned by the solution, 91.61% +/- 1.66% of them were correctly identified as outliers. Therefore, there are indications that the proposed solution has the potential to contribute to government audit support activities, as well as to management and decisionmaking processes, these arising from the interpretation of the phenomena present in the data.

KEYWORDS

Anomaly detection, Outlier detection, (un)supervised learning, data mining.

Manufacturers, AI Models and Machine Learning, Value Chains, and 5th Generation Wireless Networks

Robert B. Cohen, Economic Strategy Institute, Washington, D.C, USA

ABSTRACT

When AI models and machine learning are fully interconnected in factories using 5th Generation wireless communications, firms achieve significant gains over what they obtained from their initial, digital efforts. Firms enhance their value chains by building smart factories and connecting nearly all manufacturing processes to machine learning and AI models that analyze data rapidly. Next, they take advantage of network effects to derive even larger benefits inside their production operations and in their supply chains. In both phases, the adoption of 5th Generation wireless in plants ramps up firms’ abilities to interconnect their digital systems. Once the interconnected systems exist, firms exploit network effectsto create“knowledge synthesis” or knowledge platforms to consolidate insights gained from optimizing many machines and processes. Using “knowledge synthesis”, firms can transfer knowledge from one group of equipment to another that is not optimized. Theresult is that firms are far more flexible and scalable.

KEYWORDS

Digital Firms, Value Chains, Smart Factories, Manufacturing Processes, Knowledge Synthesis.

New Horizons and New Potential for the Public Space: Architecture, Interaction, Sociality, Artificial Intelligence, Robotic, Communication, Technology, Neuroscience, Coding

Prof. Dr. Elnaz Ghazi, Professore at Mater Neuroestetica, Department of Systems Medicine, Tor Vergata University of Rome , Rome, Italy

ABSTRACT

In modern times, research has broadened its interests to interactions with other disciplines; it now considers the specular natures of art and science and of psychology and aesthetic to be no longer contrasting, but complementary. It connects them to the architectural project, manifesting them as attention to spatial orientation, to the perception of privacy, to social interaction, and to other experiential aspects of human behaviour. The interdisciplinary field investigated here concerns the relationship between brain and environment, and considers its impact on human perception in the building of new spaces. This relationship has always been present in architectural research, but today it needs a more in-depth investigation and an assessment of its potential. This substantial work investigates the experimental field of the interaction between Interactive architecture, Artificial Intelligence, Robotic, neurosciences and social sciences. It draws on facial expression tests and studies on the brain waves that occur when individuals use certain spaces, in an attempt to establish a new planning method that takes into account perceptions, feelings and emotions to fine-tune the architectural and social features of public spaces. This research developed from a specific question: is it possible to create a new architectural outlook where public spaces could be tools for social sharing and growth, and where people could interact empathically – while maintaining their individuality – to build a socially satisfying common future? Based on this first question, the research focused on the studies conducted in Artificial Intelligence and Robotic that incorporated in Interactive Architectural space and contemporary neurosciences, speculating whether findings concerning the operation of Brain-Computer Interface (BCI) could be applied to the planning of public spaces to improve the quality of the interaction between individuals (or between individuals and spaces themselves). The potential of the public space can be viewed based on the concept of body as a medium, whose extension becomes the basis for a practical intervention on the interactivity between body and space, and consequently on social interaction. It is in this sense that technology plays a part in the relationship between human beings and environment. The present study offers an architectural solution to the renewed need for social communication by incorporating an intelligent component into public spaces (that public space present in this research as Meta-Body 0r Robot incorporate in Artificial Living Organism System). This component, integrated in the project and Facial expression, would be able to connect users of the same space to each other through the potential of their brain waves, using HCI (Human- Computer Interface), BCI (Brain-Computer Interface) technologies and EEG (electroencephalography). For this reason, the study is focused on interactive architecture, as we believe in the need to expand its applications to public spaces and to the service of the whole community. Although this research field has clearly enormous potential for the individual, subjective space, this study focuses on the public space because, under current conditions, the mere presence of technology might substantially increase social interactions in the space itself. In this concept, architectural features will be provided with powerful sensors that can interpret, decipher and analyses the multiple signals involuntarily emitted by human beings, particularly brain waves, which will be captured through BCI and wearable technologies such as EEG with strong coding and programming.

KEYWORDS

Coding, Biology, Artificial Intelligence, Interaction, Sociality, Communication, Technology, Neuroscience, Robotic.

A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models

Josh Kalin^1,2, David Noever², and Matt Ciolino², ¹Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA, ²PeopleTec, Inc, Huntsville, AL, USA

ABSTRACT

Each machine learning model deployed into production has a risk of adversarial attack. Quantifying the contributing factors and uncertainties using empirical measures could assist the industry with assessing the risk of downloading and deploying common machine learning model types. The Drake Equation is famously used for parameterizing uncertainties and estimating the number of radio-capable extra-terrestrial civilizations. This work proposes modifying the traditional Drake Equation’s formalism to estimate the number of potentially successful adversarial attacks on a deployed model. While previous work has outlined methods for discovering vulnerabilities in public model architectures, the proposed equation seeks to provide a semi-quantitative benchmark for evaluating the potential risk factors of adversarial attacks.

KEYWORDS

Neural Networks, Machine Learning, Image Classification, Adversarial Attacks.

A Development Framework for a Conversational Agent to Explore Machine Learning Concepts

Ayse Kok Arslan, Oxford Alumni of Northern California, Santa Clara, USA

ABSTRACT

This study aims to introduce a discussion platform and curriculum designed to help people understand how machines learn. Research shows how to train an agent through dialogue and understand how information is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy based on existing research and integrates a wide range of different subject documents into a set of key AI literacy skills to develop a user-centered AI. This functionality and structural considerations are organized into a conceptual framework based on the literature. Contributions to this paper can be used to initiate discussion and guide future research on AI learning within the computer science community.

KEYWORDS

Machine Learning, Visual Editing, Construction, Neural Nets, Artificial Intelligence.

Seasonal Heavy Rain Forecasting Method

Jaekwang KIM, School of Convergence / Convergence program for social innovation, Sungkyunkwan University, Seoul 03063, South Korea

ABSTRACT

In this study, we study the technique for predicting heavy / non-rain rainfall after 6 hours from the present using the values of the weather attributes. Through this study, we investigated whether each attribute value is influenced by a specific pattern of weather maps representing heavy and non-heavy rains or seasonally when making heavy / non-heavy forecasts. For the experiment, a 20-year cumulative weather map was learned with Support Vector Machine (SVM) and tested using a set of correct answers for heavy rain and heavy rain. As a result of the experiment, it was found that the heavy rain prediction of SVM showed an accuracy rate of up to 70%, and that it was seasonal variation rather than a specific pattern that influenced the prediction.

KEYWORDS

Prediction Method, Forecasting, Machine learning, Feature extraction.

Pseudo examples by noisy decoder and release of UrduMNIST dataset

Wisal Khan¹, Teerath Kumar², Waqas Ahmad¹, Bin Luo¹, Ali Kashif Basheer³, Ejaz Ahmed⁴, Waseem Shahzad⁴, ¹School of Computer and Technology, Anhui University, Hefei 230039, Peoples Republic of China, ²Kyung Hee University, South Korea, ³Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK & School of Electrical Engineering and Computer Science, National University of Science and Technology (NUST), Islamabad, Pakistan, ⁴National University of Computer and Emerging Sciences, Islamabad Pakistan

ABSTRACT

Pseudo examples generation has shown an impressive performance on image classification tasks. Pseudo examples generation is useful when we have data in a limited quantity used for semi-supervised learning or few-shot learning. Previous work used autoencoder to improve the classification performance for semi-supervised learning. A Single autoencoder can generate confusing pseudo examples that degrade the performance. To address this issue, we propose a unique way of pseudo examples generation using only generator (decoder) for each class separately, that is effective for both semi-supervised learning and few-shot learning. In our approach, the decoder is trained for each class sample using random noise, and N samples are generated using the trained decoder. Our generator based approach outperforms previous semi-supervised learning and few-shot learning approaches. Secondly, we are the first to release the UrduMNIST dataset consists of 100000 images, including 80000 training and 20000 test images collected through three different methods for diversity purposes. We also check our methods effectiveness on the UrduMNIST dataset by using semi-supervised learning with absolute average improvement of 3.042 accuracy and few-shot learning with absolute average improvement of 1.5 accuracy. Both learning’s are used with a different number of examples.

KEYWORDS

Semi Supervised Learning, Few Shot Learning, Encoder-Decoder, UrduMNIST Dataset.

Intelligent Computational Model for the Classification of Covid-19 with Chest Radiography Compared to other Respiratory Diseases

Paula Santos^{1, 2}, ¹Department of Psychology, University of São Paulo, Ribeirão Preto, Brazil, ²Department of Head and Neck Surgery, Ophthalmology and Otorhinolaryngology, University of São Paulo, Ribeirão Preto, Brazil

ABSTRACT

Lung X-ray images, if processed using statistical and computational methods, can distinguish pneumonia from COVID-19. The present work shows that it is possible to extract lung X-ray characteristics to improve the methods of examining and diagnosing patients with suspected COVID-19, distinguishing them from malaria, dengue, H1N1, tuberculosis, and Streptococcus pneumonia. More precisely, an intelligent computational model was developed to process lung X-ray images and classify whether the image is of a patient with COVID-19. The images were processed and extracted their characteristics. These characteristics were the input data for an unsupervised statistical learning method, PCA, and clustering, which identified specific attributes of X-ray images with Covid-19. The introduction of statistical models allowed a fast algorithm, which used the X-means clustering method associated with the Bayesian Information Criterion (CIB). The developed algorithm efficiently distinguished each pulmonary pathology from X-ray images. The method exhibited excellent sensitivity. The average recognition accuracy of COVID-19 was 0.93 ± 0.051.

KEYWORDS

Probabilistic Models, Machine Learning and Computer Vision.

An Object Detection Navigator to Assist the Visually Impaired using Artificial Intelligence and Computer Vision

Ethan Wu¹, Jonathan Sahagun², Yu Sun³, ¹Bellflower, CA 90706, ²California State University, Los Angeles, Los Angeles, CA, 90032, ³California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

The advent and worldwide adoption of smartphones has enriched the lives of many people. However, one particular group--the visually impaired--still need specific apps to help them with their daily lives. Thus, I’m developing this Smart app to specifically help the visually-impaired. Specifically, I hope to integrate the functions of Google Maps into the Smart App. While Google Maps functions well as a GPS for the average person without any impairment, I’m adding additional features to the Smart app so that it would guide the eye-sight impaired. For example, I will use the camera of the Smartphone to guide the user such that it would take the user to the desired destination. Thus, using the inherent functions (camera) of a phone, the Smart app can gently and safely guide any sight-impaired person to a predetermined destination by walking. One can think of Smart app as an improvement upon Google Maps -- for the visually impaired.

KEYWORDS

Object detection, Google Maps, iOS, Android.

Classroom to Online Instruction: A Remedy to Insecurity in Nigerian Universities

Akhigbe, O. J. Ph.D¹ and Ogunlade, B.O. Ph.D², ¹Office Technology And Management, School Of Information And Communication Technology, Auchi Polytechnic, Auchi, ²Department Of Educational Technology, Bamidele Olumilua University Of Education, Science And Technology. Ikere Ekiti.

ABSTRACT

As schools across nations move from onsite to online instruction in an attempt to reduce impact of insecurity and evolving coronavirus pandemic. Educational institutions in Nigeria are struggling to convert their face-to-face to virtual classes. The demand for online instruction is now on the increase, academic institution that cannot offer this option may risk losing potential students to other institutions that adopt innovative approaches to education especially during these period of uncertainty. This turn-around has forced institution to scramble for how to change lecture delivery methods to online or virtual method. Many instructors and teachers are now wondering on how to provide a learning experience to students. Understanding strategies that will prepare academic institutions to meet the challenges associated with this transformation is essential. Developing quality online program that will suit the situation of Nigerians and also keep students off roads and campuses that have become hide out for kidnapers, herdsmen, gunmen and terrorist is now imperative in this era of insecurity. This paper therefore explores the multi-faceted nature of insecurity in schools, its dangers to students and the entire society as well as change from onsite to online instruction as a solution to insecurity and Covid-19 pandemic in Nigerian education.

KEYWORDS

Paradigm Shift, Onsite, Online, Instruction, Insecurity and Covid-19 Pandemic.

Vehicle for Plastic Garbage Gathering

Janusz Bobulski1 and Lukasz Karbowiak, Department of Computer Science, Czestochowa University of Technology, Poland

ABSTRACT

Nowadays, waste and threats related to it are a serious environmental issue. The interest in waste management is growing in terms of technologies that would help minimize their quantity and in terms of their neutralization and economic use as well. The basic segregation used now is not enough as only some types of plastic can be recycled. There is a problem with separating the different types of plastic, so modern techniques for sorting it are needed. One of such techniques is, for example, to use deep learning and convolutional neural network. The main issue with this work is the design of the robot for automatically segregating plastic waste into seven categories based on the camera image. This vehicle applies computer vision and artificial intelligence methods to waste recognition. The car could be helpful in terrain and factories. We present a 15-layer convolution neural network that can recognize 7 groups of plastic.

KEYWORDS

Deep learning CNN, waste management, image processing, environment protection, artificial intelligence.

Clustering Commercial and Residential Electricity Consumption using K-Means Algorithm

Esnehara P. Bagundang and Cyrus B. Rael, College of Computer Studies, Sultan Kudarat State University, Sultan Kudarat, Philippines

ABSTRACT

This study implemented K-means Algorithm to cluster the data set of electricity consumption of clients. The data set was obtained from the Meter Reader Billing Statement System of Sultan Kudarat Electric Cooperative, Inc. (SUKELCO). It aimed to cluster the electricity consumption of commercial and residential clients for the period of four months (January-April 2021). The result of this study shows an interesting fact that majority of both commercial and residential clients belongs to the group with low electricity consumption and there is an increase demand of electricity each month.

KEYWORDS

Clustering, K-Means Algorithm, Electric Consumption.

Predicting the 2020 US Presidential Election with Twitter

Michael Caballero, Divison of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA, USA

ABSTRACT

One major sub-domain in the subject of polling public opinion with social media data is electoral prediction. Electoral prediction utilizing social media data potentially would significantly affect campaign strategies, complementing traditional polling methods and providing cheaper polling in real-time. First, this paper explores past successful methods from research for analysis and prediction of the 2020 US Presidential Election using Twitter data. Then, this research proposes a new method for electoral prediction which combines sentiment, from NLP on the text of tweets, and structural data with aggregate polling, a time series analysis, and a special focus on Twitter users critical to the election. Though this method performed worse than its baseline of polling predictions, it is inconclusive whether this is an accurate method for predicting elections due to scarcity of data. More research and more data are needed to accurately measure this method’s overall effectiveness.

KEYWORDS

Big Data, Internet of Things, Machine Learning, Data Mining, NLP.

Cross-Modal Perception in Kirundi

Emmanuella Ahishakiye, Department of English, Institute for Applied Pedagogy- University of Burundi, Faculty of Languages, Arts and Translation- University of Liège

ABSTRACT

Languages do not always use specific perception words to refer to specific senses. A word from one sense can metaphorically express another physical perception meaning. For Kirundi, findings from a corpus-based analysis revealed a cross-modal polysemy and a bidirectional hierarchy between higher and lower senses. The attested multisensory expression of auditory verb kwûmva ‘hear’ allows us to reduce sense modalities to two –vision and audition. Moreover, the use of synesthetic metaphor (e.g. kwûmva akamōto ‘lit:hear a smell’/ ururirimbo ruryoshe ‘lit:a tasty song’/ururirimbo ruhimbaye ‘lit:a pleasant song) does not only show that lower senses can be used to express higher senses, but also that Kirundi considers feelings and emotions as part of the perception system, i.e., there is a sensation-emotion continuum. Kirundi Speakers can express both internal and external perceptions through auditory experience verb (e.g.; kwûmva inzara ‘lit: hear hunger’, kwûmva umunêzēro ‘lit: hear happiness’, kwûmva ingoma ‘lit: hear beats of drums’).

KEYWORDS

Sense Modality, Kirundi, cross-modal perception, lexical semantics, synaesthetic metaphor.

Aspects of Meaning by Collocation and the Degree of Collocability in Hausa Fixed Expressions

Ali Usman Umar*, Department of Nigerian Languages, Federal University of Lafia and TijjaniShehuAlmajir, PhD., Department of Linguistics and Foreign Languages, Bayero University, Kano

ABSTRACT

This paper provides an account for collocational behaviour of Hausa certain fixed expressions withrespect to their aspects of collocative meaning and degree of collocability. Collocations are ubiquitous in everyday language communication, as a result of which is so substantial that language users need to take their pervasive practice into consideration as they shouldknow when and why to be processing such collocational information. It is obvious that meaning, context and culture are closely intertwinedforegrounding with which the formation for the background knowledge ofhuge collocational information are institutionalized. The research is based on empirical method for data justification and the same data was analysed using J. R. Firth’s (1957a, 1968) contextualism as its theoretical framework. In the final analysis, the paperrationalized that the collocative meaning of the collocating words could be determined in either in abstraction or literal meaning. And also the reason why words collocate is because of their meaning relations, context of use, mutual expectancy, and cultural norms. Another thing which rendered the collocational behaviour in the language more accessible, interesting, and worthwhile is the degree of collocability.

KEYWORDS

Collocations, collocability, fixed expressions, Hausa, Meaning.

Concept of Powerlessness and Cultural Estrangement in the Personal Narratives of the Crimea Inhabitants

Svetlana Kucherenko¹ and Tatiana Pavliuk², ¹Department of Psychology V.I. Vernadsky Crimean Federal University, Yalta, Crimea, ²Department of Humanities V.I. Vernadsky Crimean Federal University, Armiansk, Crimea

ABSTRACT

This article studies the theme of social-psychological alienation in personal narratives of inhabitants of the Crimea with emotionally-negative type predomination. Metaphorical narrative of “Adventures of Windock, Cloudette and Plumelet” by T. Pavliuk is analyzed; the metaphors of the futility of efforts (rains in the desert) and helplessness (passivity) are identified through the narrative analysis. Main ecopsychological dispositions in personal narratives of the Crimean people are given according to P.V. Lushin concept: home, family; enemy; indifference of others and the indignation at this. Ways of narratives’ deconstruction are put forward based on the category of multiple possibilities: “can be” instead of “should be”. The presence of the enemy’s ecopsychological disposition in the narratives is unproductive to overcome the existential difficulties associated with the change of the socio-political situation in the Crimea after March 18, 2014. Transition to a new self-conception and the place in society can occur through overcoming indifference to their own fate and to the closest people.

KEYWORDS

narrative analysis, personal narrative, metaphorical narrative, concept of powerlessness, cultural estrangement, social-psychological alienation, narrative’s deconstruction.

An overview of the Knowledge Organization Systems (KOS) Categorization in Digital Content Management

Maziar Amirhosseini, Faculty Member & Assistant Professor, Academic Relations and International Affairs, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

ABSTRACT

The principal purpose of the present article is to analyze the technical mechanisms and the categorizationprocedure of thesaurus, ontology, and their types in storage and retrieval of digital and non-digital content. The types of knowledge organization systems (KOSs) which are addressed in this article are Roget’s thesaurus, thesauri, Micro, Macro and Meta thesaurus, ontologies, and lower, middle, or upper level ontologies. The study attempts todemonstrate categorization procedure through determining the positionof KOSs in the context of data, information, and knowledge (DIK) by explaining their engineering mechanismssuch as data, information and knowledge engineering in content storage and retrieval, especially digital contents. The research method relies on documentary and historical methodology.As ontologies have taken the highest position between KOSs in making digital resources available in web-based environment, it is suggested that Iran and other developing countries use the capacities and capabilities of ontologies, especially in the development of national ontologies, in order to construct their knowledge-based infrastructure and system to achieve high performance in digital content engineering and management.

KEYWORDS

Knowledge Organization Systems (KOSs), Thesauri, Ontologies, Engineering Mechanism, Categorization Procedure.

Automatic Easification of Action Rule Legislative Sentences

Sherry Maynard, The University of the West Indies, Cave Hill Campus, Cave Hill, St. Michael, Barbados

ABSTRACT

This paper automates the easification techique, clarifying cognitive structuring, as an aid to non-legal experts (specialist readers) who are required to read legislative text as part of their jobs. It outlines the design of an algorithm, referred to as Strateasy, which stratifies and easifies action rule legislative sentences (LS) and presents multiple perspective of the legislative idea. The algorithm design includes the development of semantic annotation rules to detect the modality, actor, action, case and condition concepts in action rule legislative sentences. The performance of the algorithm is tested based on its accuracy of semantic annotations, the semantic retention of its outputted easified LS as compared to the original LS and the perceived impact of the easified LS on the intrinsic and extraneous cognitive loads of the specialist readers who participated in the research.

KEYWORDS

Legislative Text, Semantic Annotation, Easification, Cognitive Load, Specialist Readers.

A New Hybrid Pso-jaya Algorithm for Function Optimization

Lucijano Berus¹ and Mirko Ficko², ¹University of Maribor, Faculty of Mechanical Engineering, Production Engineering Institute, Maribor, Slovenia, ²University of Maribor, Faculty of Mechanical Engineering, Production Engineering Institute, Maribor, Slovenia

ABSTRACT

A simple yet powerful population based algorithm (PSO-JAYA) is proposed as a combination of Particle Swarm Optimization (PSO) and JAYA optimization procedures. The idea is to synthesize the exploration abilities of PSO and JAYA into hybrid PSO-JAYA, which incorporates both algorithms strengths. Benchmark test functions are used to compare PSO-JAYA with other known optimization procedures, such as PSO, JAYA, GSA, and PSOGSA. Novel PSO-JAYA have reached the best performance on all 5 tested benchmark functions.

KEYWORDS

Particle swarm optimization, Function optimization, Jaya optimization, Constrained benchmark problems.

Reducing Latency in AWS Lambda

Dini Mardiana Binti Mohd Radzuan and Emrehan Cagatay Department of Computer Engineering, Esslingen University of Applied Sciences, Esslingen, Germany

ABSTRACT

Serverless computing or also called function-as-a-service (FaaS) has gained a higher importance in providing services to clients and end users. With serverless computing, the focus is more on the code to be written and less on the infrastructure to be implemented [1]. This makes it easier for developers to start developing their code and removes the need to maintain the services that they are providing. One of the serverless computing technologies available is AWS Lambda and is used for the CloudLab project in Esslingen University of Applied Sciences. The usage of AWS Lambda has shown a downside, namely latency in the server response. This work analyzes the underlying factors of the latency and discusses techniques that could be implemented in order to reduce latency in AWS Lambda.

KEYWORDS

Function-as-a-service, Serverless, Latency, AWS Lambda.

Care – A Framework for a Multimedia Assistance System for Singletons “Does It Help?”

Thomas Schirgi, Institute of Technical Informatics, Technical University, Graz, Austria

ABSTRACT

In contrast to the increasing degree of automation in the production industry, commissioning and maintenance activities will essentially be limited to manual activities. Production involves repetitive activities that are manageable and clearly defined as a process. Unlike this, commissioning and maintenance have to deal with uncontrollable, undefined, and non - standardized processes. Due to the large amount of information and data that needs to be handled, control and visualization represent a significant challenge. That goes along with an easily understandable visualization of information for users. Due to various types, sizes, voltage levels, etc., power transformers can be seen as singletons since no transformer equals other ones. Therefore, this paper aims to create a framework for a user-friendly assistance system for singletons. To create a system that is accepted by the user, workshops and interviews were held. Based on these criteria for a user-friendly interface and a framework for an assistance system for singletons were created. It was found that the paradigm has to consist of five key components to provide tailored assistance to users. These key components are Expertise, Infrastructure, Application & Platforms, Security & Privacy, and Business Process & Business Model. Based on those criteria, various use cases and a prototypical application, and flexible backend services were developed. This usability test confirmed the criteria and the framework guidelines; It could be seen that consistent methods of interaction and colors are essential. Finally, it can be said that the assistance system is moving in the right direction. It is of utmost importance that such assistance systems adapt to users needs to be accepted.

KEYWORDS

Multimedia, Assistance, CaRE, Software.