Welcome to CSEN 2022

9th International Conference on Computer Science and Engineering (CSEN 2022)

September 17~18, 2022, Copenhagen, Denmark



Accepted Papers
RestroDroid - A Restaurant App using Bot Service

Jeny Jijo1, Supreet Ronad2, Sathvik Saya2, Sampreeth Naik2 and Priyadarshini V2, 1Assistant Professor, Dept of CSE, PES University, Electronic City Campus, Bengaluru, 2Dept of CSE, PES University, Electronic City Campus, Bengaluru

ABSTRACT

An automated system that can be used to make the working of a restaurant more efficient is described. In today’s age of rapid meals and need for social distancing due to COVID-19, ensuring hygiene and keeping safe has become a top-most priority. But in businesses which involve serving people in short range, it is difficult to do the same. The solution is to make the whole system contact-less using technology. The proposed system makes use of a mobile app and a line following robot to deliver food to customers in a restaurant. Ordering through the application gives the user fast visual confirmation of our choices and assures that the things in the order placed is exactly what the customer ordered. This technology is paired with a robot that can bring meals to a certain table. The whole system is made low-cost as compared to the existing systems.

KEYWORDS

Automated service, Bot, Application, Flutter, Arduino, IoT, Restaurant.


Enhancement of AES cryptography with Fast Key Generation Technique to improve cyber security

Mr.P Salman Raju1, Dr.P Venkateswara Rao1 and Prof. S S Murthy2, 1Department of CSE, AKNU, Rajahmundry, India, 2Director, IPE, Hyderabad, India

ABSTRACT

The most successful method of securing data is cryptographic encryption. Modern cryptographic techniques significantly contribute towards todays accelerated cybersecurity needs. With the advent of data, it has become more important to secure the data from external interference to improve Cyber security. Various algorithms have been deployed with a penultimate goal of securing data of which Advanced Encryption Standard (AES) is one. The paper provides a substantial modification over Advanced Encryption Standard, with the aim of Cyber security and easy implementation focusing on the Symmetric Key Cryptosystem and AES algorithm. The performance was compared using different parameters such as data block size, encryption/decryption speed, run-time, and compile time. The main reason for this experiment was to exploit the effectiveness of the newly modified AES over the actual AES using the real-life application. A new key implementation process, the right shift key technique, and the elimination of the Mix-Column process were introduced. This paper provides calculations based on the AES-128-bit version, but the proposed algorithm can work on other versions as well. The goal was to achieve fast execution time and less memory usage while keeping the security hard to breach. The result depicts that our method achieved 88% throughput and better net execution time. This is a substantial improvement in terms of efficiency from the traditional AES.

KEYWORDS

Cryptography, Cyber Security, Symmetric Key Cryptography, Block Cipher, AES, Modified AES, Performance Evaluation, Result and Discussion.


Motion Detection based User Activity Identification using Machine Learning

Darshan B1, Moniak Rao2, S Saraf3 And Dr. A S Poornima4, 1& 4Department Of Computer Science Engineering, Siddaganga Institute of Technology Tumkur, India, 2JSS Science and Technology University Mysore, 3Commscope Inc

ABSTRACT

Human detection plays an important role various real life application. Most of conventional techniques rely upon utilizinghand made capabilities that are problem-structured and finest for particular tasks. Moreover, theyre fairly vulnerable to dynamical activities which includes illumination changes, digicam jitter, and variations in item sizes. On the opposite hand, the proposed function gaining knowledge of techniques are less expensive and less complicated due to discriminative capabilities. Most of conventional processes rely on utilizing hand made features that are problem-based and premiere for unique tasks. Moreover, they may be at risk of dynamical occasions along with illumination changes, digital cam jitter, and versions in item sizes. On the opposite hand, the proposed function mastering processes are inexpensive and less complicated due to abstraction and discriminative capabilities.In the sector of indoor tracking, researchers have proven an interest in deep learning for classifying each day human activities, detecting falls, and tracking gait abnormalities. Driving these interests are rising programs associated with clever and steady homes, assisted living, and medical diagnosis. The fulfillment of deep learning in offering an correct real-time accounting of determined human motion articulations essentially relies upon at the neural community structure, enter facts representation, and right training. Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained tensor flow model produces an average accuracy of 95%.

KEYWORDS

Motion Detection, Machine Learning, Tensor Flow, Deep learning, Multilayer perceptron.


Epilepsy Prediction using Machine Learning

Dharshika S, Sahreen Sajad and Sushmitha N, Department of Information Science and Engineering, R.V. College of Engineering Bengaluru, Karnataka, India

ABSTRACT

Epilepsy has severe impacts on patients including disrupting their social relationships and less mobility. Prediction of the disease can help the patient prevent the onset of seizures with the help of appropriate medication. Since the traditional methods of studying EEG are prone to misdiagnosis, Machine Learning can provide a more accurate diagnosis. In this paper, we aim to survey models to better describe methodologies for a high-precision model to predict epilepsy in patients.

KEYWORDS

Seizure, EEG (Electroencephalogram), Machine Learning, KNN (K-Nearest Neighbour, Logistic Regression, Decision tree.


Mining Online Drug Reviews Database for the Treatment of Rheumatoid Arthritis by using Deep Learning

Pinar Yildirim, Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey

ABSTRACT

In this paper, a research study to extract knowledge in the online patient reviews for rheumatoid arthritis is introduced. Rheumatoid arthritis is a long-term and disabling autoimmune disease. Today, a huge amount of people have rheumatoid arthritis in the world. Considering the importance of the medication of rheumatoid arthritis, we aimed to investigate patient reviews in WebMD database and get some useful information for this disease. Our results revealed that etanercept treatment has the highest number of reviews. Data analysis was applied to discover knowledge on this drug. Deep learning approach was used to predict the effectiveness of etanercept and classification results were compared with other traditional classifiers. According to the comparison of classifiers, deep neural network has better accuracy metrics than others. Therefore, the results highlight that deep learning can be encouraging for medical data analyses. We hope that our study can make contributions to intelligent data analysis in medical domain.

KEYWORDS

Classification, Deep Learning, Etanercept, Online Drug Reviews.


Cyberbullying Detection using Ensemble Method

Saranyanath K P1, Wei Shi2 and Jean-Pierre Corriveau1, 1School of Computer Science, Carleton University, Ottawa, Canada, 2School of Information Technology, Carleton University, Ottawa, Canada

ABSTRACT

Cyberbullying is a form of bullying that occurs across social media platforms using electronic messages. In this paper we propose three different approaches, and five models to identify cyberbullying on a generated social media dataset, derived from multiple online platforms. Our initial approach consists in enhancing a Support Vector Machines. Our second approach is based on DistilBERT, which is a lighter and faster Transformer model than BERT. Staking the first three models we obtain two more ensemble models. Contrasting the ensemble models with the three others, we observe that the ensemble models outperform the base model concerning all evaluation metrics except precision. While the highest accuracy, of 89.6%, was obtained using an ensemble model, we achieved the lowest accuracy, at 85.53% on the SVM model. The DistilBERT model exhibited the highest precision, at 91.17%. The model developed using the different granularity of features outperformed the simple TF-IDF.

KEYWORDS

Machine Learning, Natural Language Processing, Support Vector Machine, DistilBERT, Cyberbullying.


Performance Evaluation for the use of ELMO Word Embedding in Cyberbullying Detection

Tina Yazdizadeh and Wei Shi, School of Information Technology, Carleton University, Ottawa, Ontario, Canada

ABSTRACT

Communication using modern internet technologies has revolutionizedthe ways humans exchange information.Despite all the advantages made available by information and communication technology, its applicability is still limited due to problems caused by personal attacks or pseudo-attacks.Thesetoxic contents may be in the form of texts (e.g., online chats, emails), speech, or even images or movie clips on social media platforms.Because cyberbullyingof an individual via the use of such toxic digital contentmay have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect cyberbullying from the social media content using machine learning approaches.During a cyberbullying detection process, word embedding techniques are used to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words such that the words that are closer in the vector space are expected to be similar in meaning.The extracted embeddings are then used todecide if a digital input contains cyberbullying content.Supplying strong word representations to classification methods is an important issue.In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, usingthree basic machine learning models and four deep learning models.The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.

KEYWORDS

Cyberbullying, Natural Language Processing, Word Embeddings, ELMo, Machine Learning


Affective Computing analysis in Emotional Psychology using AMIGOS with Gaussian Expectation-Maximization model

Bakkialakshmi, Hindustan Institute of Technology and Science, India

ABSTRACT

Investigation of human subjects is the goal. The value and authenticity of human emotions are greater. The development of affect theory leads one to believe that it is necessary to be aware of ones sentiments and emotions to forecast ones behaviour. The proposed system considers the AMIGOS dataset, in which 40 test videos are exposed to the 20 participants. The dataset collects the ECG, EEG, and GSR values of the participants during exposure to video. The proposed line of inquiry is focused on developing a reliable model that incorporates neurophysiological data into actual feelings. Any change in emotional affect will directly elicit a response in the bodys physiological systems. The system that is described makes use of the AMIGOS dataset to develop a one-of-a-kind prediction method termed the Gaussian expectation-maximization technique. In terms of statistical parameters such as population mean and standard deviation, the suggested method is evaluated in comparison to a technique that is considered to be state-of-the-art. When doing an in-depth analysis of emotions, it is necessary to retrieve the comparative analyses of several people, each of whom possesses their own set of covariate points. Anger, hatred, disgust, happiness, and sadness are some of the emotional states that can be identified using the technique that has been provided. The proposed system determines an individuals emotional state after a minimum of 6 iterations of iterative learning, using the Gaussian Expectation maximization statistical model, in which to the level of zero error, the iterations tend to continue. Perhaps each of these improves predictions while simultaneously increasing the amount of value extracted.

KEYWORDS

Affective computing, Emotion Detection, Machine learning, Amigos, Emotional Psychology.


An Intelligent Community-Driven Mobile Application to Automate the Classification of plants using Artificial Intelligence and Computer Vision

Yifei Tong1 and Yu Sun2, 1Trinity Grammar School, 119 Prospect Rd, Summer Hill NSW2130, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA92620

ABSTRACT

How can the ef iciency of volunteers be improved in performing bushcare in the limited amount of time able tobespent caring for each location every month [1]? Bushcare is a volunteer activity with a high dif iculty curve for volunteers just starting out as the crucial skill of distinguishing the native plants from the harmful invasive species only comes with experience and memorization[2]. The lack of ability to distinguish targeted plants will greatly reduce the ef iciency of the volunteers as they workthrough the limited amount of time they have at each location each month while also discouraging newly joinedvolunteers from continuing this activity. To assist newly joined volunteers, the majority of each would likely be from a younger demographic with a digital app that could help the user distinguish the species of plant, making it easier for them to start familiarizingthemselves with both the native and invasive species in their area [3]. The user could simply have to take a pictureof the plant they wish to identify and the software would use its image recognition algorithm trained with a databaseof dif erent species of plants to identify the type of plant and whether it needs to be removed. At the same time, moreexperienced volunteers could continue to use this app, identifying errors in the app’s identification to make it morereliable.

KEYWORDS

Flutter, Machine learning, Firebase, Image recognition.


A Data-Driven Analytical System to Optimize Swimming Training and Competition Performance using Machine Learning and Big Data Analysis

Tony Zheng1 and Yu Sun2, 1Troy High School, 2200 Dorothy Ln, Fullerton, CA 92831, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

Many swimmers are constantly incorporating new and dif erent training regimes that would let them improve quickly [2]. However, it is dif icult for a swimmer to see their progress instantly. This paper develops a tool for swimmers, specifically swimmers, to predict their future results. We applied machine learning and conducted a qualitative evaluation of the approach [3]. The results show that it is possible to determine their future performance with decent accuracy. This application considers the swimmers performance history, age, weight, and height to predict the most accurate results.

KEYWORDS

Machine Learning, Mobile APP, database.


WassBERT: High Performance BERT-based Persian Sentiment Analyzer and Comparison to Other State-of-the-art Approaches

Masoumeh Mohammadi and Shadi Tavakoli, DepartMent of DataScience & Machine Learning, Telewebion, Tehran, Iran

ABSTRACT

Applications require the ability to perceive others opinions as one of the most outstanding parts of knowledge. Finding the positive or negative feelings in sentences is called sentiment analysis (SA). Businesses use it to understand customer sentiment in comments on websites or social media. An optimized loss function and novel data augmentation methods are proposed for this study, based on Bidirectional Encoder Representations from Transformers (BERT). First, a crawled dataset from Persian movie comments on various sites has been prepared. Then, balancing and augmentation techniques are accomplished on the dataset. Next, some deep models and the proposed BERT are applied to the dataset. We focus on customizing the loss function, which achieves an overall accuracy of 94.06 for multi-label (positive, negative, neutral) sentences. And the comparative experiments are conducted on the dataset, where the results reveal the performance of the proposed model is significantly superior compared with other models.

KEYWORDS

Bidirectional encoder representations from transformers (BERT), Bidirectional long short-term memory (Bi-LSTM), Comment classification, Convolutional neural network (CNN), Deep learning, Opinion mining(OM), Natural language processing (NLP), Persian language sentiment classification, Persian Sentiment analysis, Text mining.


DAGAM: Data Augmentation with Generation and Modification

Byeong-Cheol Jo1,*, Tak-Sung Heo1,*, Yeongjoon Park1, Yongmin Yoo1, Won Ik Cho2 and Kyungsun Kim1, 1AI R&D Group, NHN, Seoul, Republic of Korea, 2Department of Electrical and Computer Engineering and INMC, Seoul National University, Seoul, Republic of Korea

ABSTRACT

Text classification has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, under-fitting often occurs due to the size of the model being very large compared to the amount of available training data. In light of this, we introduce three data augmentation schemes that help reduce underfitting problems of large-scale language models. Primarily we use a generation model for data augmentation, which is defined as Data Augmentation with Generation (DAG). Next, we augment data using text modification techniques such as corruption and word order change (Data Augmentation with Modification, DAM). Finally, we propose Data Augmentation with Generation And Modification (DAGAM), which combines DAG and DAM techniques. We conduct data augmentation for six benchmark datasets of text classification task, and verify the usefulness of DAG, DAM, and DAGAM through BERT-based fine-tuning and evaluation.

KEYWORDS

Data Augmentation, Text Generation, Text Modification, Summarization, Character Order Change.


Topic Segmentation of Research Article Collections

Erion Çano and Benjamin Roth, Digital Philology, Research Group Data Mining and Machine Learning, University of Vienna

ABSTRACT

Collections of research article data harvested from the web have become common recently since they are important resources for experimenting on tasks such as named entity recognition, text summarization, or keyword generation. In fact, certain types of experiments require collections that are both large and topically structured, with records assigned to separate research disciplines. Unfortunately, the current collections of publicly available research articles are either small or heterogeneous and unstructured. In this work, we perform topic segmentation of a paper data collection that we crawled and produce a multitopic dataset of roughly seven million paper data records. We construct a taxonomy of topics extracted from the data records and then annotate each document with its corresponding topic from that taxonomy. As a result, it is possible to use this newly proposed dataset in two modalities: as a heterogeneous collection of documents from various disciplines or as a set of homogeneous collections, each from a single research topic.

KEYWORDS

Research Articles, Topic Segmentation, Multitopic Dataset, Keyword Generation, Research Resources.


GRASS: A Syntactic Text Simplification System based on Semantic Representations

Rita Hijazi 1, 2, Bernard Espinasse1 and Núria Gala2, 1Laboratoire Informatique et Systèmes, Aix-Marseille University, Marseille, France, 2Laboratoire Parole et Langage, Aix-Marseille University, Aix-en-Provence, France

ABSTRACT

Automatic Text Simplification (ATS) is the process of reducing the linguistic complexity of a text to improve its understandability and readability, while still maintaining its original information, content and meaning. Several text transformation operations can be performed such as splitting a sentence into several shorter sentences, substitution of complex elements, and reorganization. It has been shown that the implementation of these operations essentially at a syntactic level causes several problems that could be solved by using semantic representations. In this paper, we present GRASS (GRAph-based Semantic representation for syntactic Simplification), a rule-based automatic syntactic simplification system that uses semantic representations. The system allows the syntactic simplification of complex constructions, such as subordination clauses, appositive clauses, coordination clauses, and passive forms. It is based on transformations of graph-based meaning representation of the text expressed in DMRS (Dependency Minimal Recursion Semantics) notation using rewriting rules. The experimental results obtained on a reference corpus and according to specific metrics outperform the results obtained by other state of the art systems on the same reference corpus.

KEYWORDS

Syntactic Text Simplification, Graph-Based Meaning Representation, DMRS, Graph-Rewriting.


Bantu Spell Checker and Corrector using Modified Edit Distance Algorithm (MEDA)

Boago Okgetheng, Gabofetswe Malema, Ariq Ahmer, Boemo Lenyibi, Ontiretse Ishmael, Department of Computer Science, University of Botswana, Gaborone, Botswana

ABSTRACT

Automatic spelling correction for a language is critical since the current world is almost entirely dependent on digital devices that employ electronic keyboards. Correct spelling adds to textual document accessibility and readability. Automatic spelling correction is essential for many NLP applications like web search engines, text summarization, sentiment analysis etc. A few efforts on automatic spelling correction in Bantu languages have been completed; however, the numbers are insufficient. We proposed a spell checker for typed words based on the Modified minimum edit distance Algorithm (MEDA), and the Syllable Error Detection Algorithm (SEDA). In this study, we adjusted the minimal edit distance Algorithm by including a frequency score for letters and ordered operations. The SEDA identifies the component of the word and the position of the letter which has an error. For this research, the Setswana language was utilized for testing, and other languages related to Setswana will use this spell checker. Setswana is a Bantu language spoken mostly in Botswana, South Africa, and Namibia and its automatic spelling correction are still in its early stages. Setswana is Botswana’s national language and is mostly utilized in schools and government offices. The accuracy was measured in 2500 Setswana words for assessment. The SEDA discovered incorrect Setswana words with 99% accuracy. When evaluating MEDA, the edit distance algorithm was utilized as the baseline, and it generated an accuracy of 52%. In comparison, the edit distance algorithm with ordered operations provided 64% accuracy, and MEDA produced 92% accuracy. The model failed in the closely related terms.

KEYWORDS

Bantu Spell Checker, Edit Distance algorithm, morphologically rich, Syllable Error Detection Algorithm.


Chess is a Natural Language Game

Michael DeLeo and Erhan Guven, Whiting School of Engineering, Johns Hopkins University, Baltimore, USA

ABSTRACT

Representing a board game and its positions by text-based notation enables the possibility of NLP applications. Language models, can help gain insight into a variety of interesting problems such as unsupervised learning rules of a game, detecting player behavior patterns, player attribution, and ultimately learning the game to beat state of the art. In this study, we applied BERT models, first to the simple Nim game to analyze its performance in the presence of noise in a setup of a few-shot learning architecture. We analyzed the model performance via three virtual players, namely Nim Guru, Random player, and Q-learner. In the second part, we applied the game learning language model to the chess game, and a large set of grandmaster games with exhaustive encyclopaedia openings. Finally, we have shown that model practically learns the rules of the chess game and can survive games against Stockfish at a category-A rating level.

KEYWORDS

Natural Language Processing, Chess, BERT, Sequence Learning.


Multilingual Document Concept Topic Modeming

Ibrahim Hussein Musa1, Guilin Qi1* and Kang Xu2, 1School of Computer Science and Engineering, Southeast University, Nanjing 211189, Jiangsu, China, 2Computer Science Nanjing University of Posts Telecommunications, Nanjing, Jiangsu, China

ABSTRACT

Recently, topic modeling has been generally used to determine the abstract topics in text corpora. In an Unsupervised way, probabilistic topic models have enjoyed much success in extracting and analyzing Topics from document collections. Prevailing topic models are mainly founded on the idea that each Document is depicted as a probability distribution over the topic, and each topic is a probability distribution over words. However, this assumption is not optimal; a common deficiency of existing topic models are that they would not work well for extracting cross-lingual topics simply because words in different languages generally do not co-occur with each other. This paper proposes an integrated novel framework for Extracting topics from document collections. By incorporating a concept layer between the topic and word Layers, then transferring the knowledge to a cross-lingual model in order to improve the topic Classification for the target language with the ultimate objective of simplifying the process of extracting Shared topics in text data among different languages. Specifically, we propose a novel multilingual Document concept topic modeling (MDCTM). We derive the inference algorithm based on the Gibbs Sampling process. The empirical evaluation was carried out on two states of the art datasets by using Porter Stemmer for English documents and restoring words and forms of words for Chinese documents. Using jieba for word segmentation show that the (MDCTM) model can effectively extract concept topic Models from multilingual text data. Moreover, a noticeable advantage of our proposed model is that it can Be combined with state-of-the-art approaches to achieve better performances. We hope that this will eventually enable machines to better understand human concept, which can help reduce the ambiguities in Multi-language scenarios. Furthermore, we also hope that our system sets up a new baseline for future Concept level methods applied to a much wider class of corpora.

KEYWORDS

Topic models, concepts topic models, bilingual topic models, multilingual.


English to Indian Sign Language: Rule-based Translation System along with Multi-word Expressions and Synonym Substitution

Abhigyan Ghosh and Radhika Mamidi, Language Technologies Research Center, IIIT Hyderabad, India

ABSTRACT

The hearing challenged communities all over the world face difficulties to communicate with others. Machine translation has been one of the prominent technologies to facilitate communication with the deaf and hard of hearing community worldwide. We have explored and formulated the fundamental rules of Indian Sign Language (ISL) and implemented them as a translation mechanism of English Text to Indian Sign Language glosses. According to the formulated rules and sub-rules, the source text structure is identified and transferred to the target ISL gloss. This target language is such that it can be easily converted to videos using the Indian Sign Language dictionary. This research work also mentions the intermediate phases of the transfer process and innovations in the process such as Multi-Word Expression detection and synonym substitution to handle the limited vocabulary size of Indian Sign Language while producing semantically accurate translations.

KEYWORDS

Indian Sign Language (ISL), Machine Translation, Sign Language(SL), Low Resource Languages, hearing loss people, human-computer interaction, text to sign, communication, speech to sign


Corpus Study of in Order to for English Writing Teaching to Secondary School Students in Indonesia

Arisandy, Department of English, Kent State University

ABSTRACT

In order to is a conjunction that is commonly used for being able to be replaced by the infinitive marker to”. However, in Indonesia, it is still taught in middle schools to deliver its social function, text structure and linguistic features. Since English language teachers question the importance of teaching in order to, this study focused on finding the frequency of in order to in contexts covered by COCA. It was found that in order has been used 91,548 times in all modalities in the last three decades and 24,010 times for academic purposes. Academically, in order to is used more in geography or social science with 4122 times (20%) of all fields. However, the highest trend of use was in business, with a score of 310.97. Lastly, it was found that the word avoid collocated with in order to followed by the word protect.

KEYWORDS

in order to, COCA, Frequency, Collocated.


Comparison of Various Forms of Serious Games: Exploring the Potential use of Serious Game Walkthrough in Education Outside the Classroom

Xiaohan Feng1 and Makoto Murakami2, 1Graduate School of Information Sciences and Arts, Toyo University, Kawagoe, Saitama, Japan, 2Dept. of Information Sciences and Arts, Toyo University, Kawagoe, Saitama, Japan

ABSTRACT

The advantages of using serious games for education have already been proven in many studies, especially narrative VR games, which allow players to remember more information. On the other hand, game walkthrough can compensate for the disadvantages of gaming, such as pervasiveness and convenience. This study investigates whether game walkthrough of serious games can have the same learning effect as serious games. Use game creation (samples) and questionnaires, this study will compare the information that viewers remember from game walkthrough and actual game play, analyze their strengths and weaknesses, and examine the impact of the VR format on the results. The results proved that while game walkthrough allows subjects to follow the experiences of actual game players with a certain degree of empathy, they have limitations when it comes to compare with actual gameplay, especially when it comes to topics that require subjects to think for themselves. Meanwhile game walkthrough of VR game is not a medium suitable for making the receiver memorize information. For prevalence and convenience, however, serious games walkthrough is a viable educational option outside the classroom.

KEYWORDS

Serious game, multimedia, educational game, virtual reality, narratology, Education Outside the Classroom(EOTC).


Fast Rank Optimization Scheme by the Estimation of Vehicular Speed and Phase Difference in Mu-MIMO

Shin-Hwan Kim1, Kyung-Yup Kim2, Sang-Wook Kim3 and Jae-Hyung Koo4, 1Access Network Technology Team, Korea Telecom, Seoul, Korea, 2Access Network Technology Team, Korea Telecom, Seoul, Korea, 3Access Network Technology Department, Korea Telecom, Seoul, Korea, 4Network Research Technology Unit, Korea Telecom, Seoul, Korea

ABSTRACT

Resent MU-MIMO(Multi User-Multi Input Multi Output) scheme is one of the important and advanced technologies. In particular, it is a suitable technique to increase the capacity from the point of view of solving cell load, which is one of the big issues in the contents of 5G commercial field optimization. While this MU-MIMO technology has an important advantage of cell capacity expansion, there is a disadvantage like an interference problem due to each multi-user beams. It is important to use the advanced beamforming technology for MU-MIMO to overcome these disadvantages. Therefore, by applying the interference cancelling technology among inter UE(User Equipment) beams to improve each UE’s performance, it will contribute to improving the cell throughput. This paper introduces the various techniques of eliminating interference in MU-MIMO system. Also, it is important that UE reports rank indicator reflected the interference of multi-user beams. This paper analyses the problem of the conventional method of the rank decision in MU-MIMO system, estimates the vehicular speed quickly with the proposed rank optimization technique, and shows the DL(Downlink) UE’s performance is improved by applying a proposed rank value suitable for vehicular speed. This technique will be effectively applied to increase the overall cell capacity by improving the DL UE’s throughput in the MU-MIMO system.

KEYWORDS

MU-MIMO, 5G, multi-user, interference, UE, DL, rank indicator, cell capacity.


Deep Recurrent Neural Network based Chimp Optimization Algorithm for Human Activity Recognition

Shana L and Dr C Seldev Christopher, Department of Computer Science and Engineering, St. Xavier’s catholic college of engineering, Chunkankadai, Nagercoil, India

ABSTRACT

Human activity is individual biometric behaviour that can be detected based on the distance which has different submissions in social security, forensic detection and crime prevention. Hence, in this paper, the Deep Recurrent Neural Network-based Chimp Optimization Algorithm (DRNN-COA) is developed to identify human actions from the images. This proposed methodology is designed with three phases such as keyframe extraction, feature extraction and classification. In the keyframe extraction stage, the Structural Similarity Measure (SSIM) is utilized. In the feature extraction stage, Scale-Invariant Feature Transform (SIFT), Coverage factor and Space-Time Interest (STI) are used. The essential features are extracted with the consideration of the feature extraction method. Then the classification process is done by utilizing the proposed DRNN-COA Algorithm. Based on the proposed method, human activity classification is achieved which is utilized to identify the actions from the images. The proposed method is validated by using the KTH databases. The proposed method is implemented in the MATLAB platform and their corresponding performances/outputs are evaluated. Moreover, the statistical measures of the proposed method are also determined and compared with the existing method as Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM) respectively.

KEYWORDS

Human Activity Recognition, Similarity Index, Keyframe Extraction, Feature Extraction & Statistical Measurements.


From the Art of Software Testing to Test-as-a-Service

Janete Amaral, Alberto S. Lima, José Neuman de Souza, Lincoln S. Rocha, MDCC, Universidade Federal do Ceará, Fortaleza/CE – Brasil

ABSTRACT

Researchers consider that the first edition of the book “The Art of Software Testing”, by Myers (1979), initiated research in Software Testing. Since then, software testing has gone through evolutions that have driven standards and tools. This evolution has accompanied the complexity and variety of software deployment platforms. The migration to the cloud allowed benefits such as scalability, agility, and better return on investment. Cloud computing require a greater involvement of software testing to ensure that services work as expected. In addition to testing cloud applications, cloud computing has paved the way for testing in the Test-as-a-Service model. This work aims to characterize Test-as-a-Service, in the context of cloud computing. Based on the knowledge explained here, we sought to linearize the evolution of software testing, characterizing fundamental points, and allowing us to compose a synthesis of the body of knowledge in software testing, expanded by the paradigm of cloud computing.

KEYWORDS

Cloud computing, Software Testing, Test-as-a-Service.


Optimizing Bicycle Delivery Service using Geo-Route Web Routing: A Case Study

Ricardo Souza Barreto Barcelos1 and Carlos Leonardo Ramos Póvoa2, 1Science and Technology Center, UENF, Campos dos Goitacazes, Brazil, 2Science and Technology Center, UENF, Campos dos Goitacazes, Brazil

ABSTRACT

The delivery of packages and products using bicycles is a worldwide trend in business logistics. The challenge for large cities is to use bicycles to bring sustainability to logistics, without losing the efficiency and productivity of the delivery service. The non-burning of fossil fuels and the agility of displacement make bicycles an excellent transportation option in urban centers. This work consists of a case study carried out in a bicycle delivery company. The company assembles delivery routes empirically. Using the Geo-Route route planner the routes were optimized, which in comparison with the routes used by the company produced a reduction in distance, as well as a reduction in the number of bicycles used in the delivery. With the use of the proposed routes, an increase in productivity and a more efficient division between the teams was obtained. Package delivery by bicycles favors studies that explore more and more alternative transportation options. The Geo-Route system provided a cost reduction making its use in the company satisfactory.

KEYWORDS

Bicycle touring, Logistics, Transportation.


FindMyPet: An Intelligent System for Indoor Pet Trackingand Analysis using Artificial Intelligence and Big Data

Qinqin Guo1, Yu Sun2, 1Portola High School, 1001 Cadence, Irvine, CA 92618, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

Pet tracking has been an important service in the pet supply industry, as it is constantly needed by countless pet owners [1]. As of 2021, about 90 million families in the U.S. alone have a pet, that is about 70% of all American households. However, for most owners of smaller pets such as cats, hamsters, and more, not being able to find the pet within the house has been a problem bothering them. This paper proposes a tool to use Raspberry Pi for gathering signal strength data of the blue tooth devices and using Artificial Intelligence to interpret the gathered data in order to get the precise location of the indoor moving object [2]. The system is applied to arrive with the location of pets within the house to an accurate level where the room that the pet is located in is correctly predicted. A qualitative evaluation of the approach has been conducted. The results show that the intelligent system is effective at correctly locating indoor pets that are constantly moving.

KEYWORDS

Raspberry Pi, Firebase, machine learning, Artificial Intelligence(AI).


Cause-Effect Graphs as a Black-box Testing Method: A Survey of Available Approaches and Algorithms

Ehlimana Krupalija, Emir Cogo, Šeila Bećirović, Irfan Prazina and Ingmar Bešić, Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina

ABSTRACT

Cause-effect graphs are often used as a method for deriving test cases for black-box testing. This paper presents a comparison of different available algorithms for converting cause-effect graph specifications to test case suites and problems which may arise when using different approaches. Different types of representation and graphical notation for describing nodes, logical relations and constraints used when creating cause-effect graph specification are also discussed. An overview of available tools for creating cause-effect graph specifications and deriving test case tables is given. The systematic approach in this paper is meant to offer aid to domain experts and end users in choosing the most appropriate algorithm for deriving test case tables in accordance to specific system priorities and gaining a better level of understanding the notation used for specifying cause-effect graphs so that the most common mistakes can be avoided.

KEYWORDS

Cause-effect graphs, Test case suites, Black-box testing, Software Testing, Software Quality.


Comparing Spectroscopy Measurements in the Prediction of in vitro Dissolution Profile using Artificial Neural Networks

Mohamed Azouz Mrad, Kristóf Csorba, Dorián László Galata, Zsombor Kristóf Nagy and Brigitta Nagy, Budapest University of Technology and Economics, Budapest, Hungary

ABSTRACT

Dissolution testing is part of the target product quality that is essential in approving new products in the pharmaceutical industry. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near-infrared (NIR) spectroscopies are two fast and complementary methods that provide information on the tablets physical and chemical properties and can help predict their dissolution profiles. This work aims to compare the information collected by these spectroscopy methods to support the decision of which measurements should be used so that the accuracy requirement of the industry is met. Artificial neural network models were created, in which the spectroscopy data and the measured compression curves were used as an input individually and in different combinations in order to estimate the dissolution profiles. Results showed that using only the NIR transmission method along with the compression force data or the Raman and NIR reflection methods, the dissolution profile was estimated within the acceptance limits of the f2 similarity factor. Adding further spectroscopy measurements increased the prediction accuracy.

KEYWORDS

Artificial Neural Networks, Dissolution prediction, Comparing spectroscopy measurement, Raman spectroscopy, NIR spectroscopy & Principal Component Analysis.