Search results for: text mining

Authors: Muhammad Fairul Azreen bin Mohd Zahid, Melissa Querk, Aimi Nabila bt Anizaim

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For this particular research, the focus is based on the practice as research which will produce a choreography as the outcome. The ideas organically develop as an “epiphany” from the meeting, brainstorming, or situation that revolves around surroundings. In this study, the researchers are approaching the national pillar of Malaysia known as ‘Rukun Negara’ to develop a choreographic idea. The concept theory of Speech Act by J.L Austin is used to compose the choreography alongside with national pillar ‘Rukun Negara’ as a guideline for a contemporary work titled, The Pledge 2.0, besides fostering the spirit of unity. These approaches will offer flexibility in creating a choreography piece. The pledge has crossed the boundaries by using texts and heavy issues in choreography developments. It will emphasize the concept of delivering the speech via verbal and nonverbal body language. Besides using the Theory of Speech Acts, the development process of creating this piece will lay the bare normative structure implicit in performance practice. Converging current issues into the final choreographic piece for this research is vital as this research will explore a few choreography methods from different perspectives. Hence, the audience will be able to see the world of dance that always revolves in line with the diachronic process in many ways. The method used in this research is qualitative, which will be used in finding the movement that fits the given facts.

Keywords: performing arts, speech act, performative, nationalism, choreography, politic in dance

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Authors: Melba D. Horton

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Algae are identified as an alternative source of biofuel because of their ubiquitous distribution in aquatic environments. Diatoms are unique forms of algae characterized by silicified cell walls which have gained prominence in various technological applications. Polk County is home to a multitude of ponds and lakes but has not been explored for the presence of diatoms. Considering the condition of the waters brought about by predominant phosphate mining activities in the area, this research was conducted to determine if endemic diatoms are present and explore their potential for low-cost mass production. Using custom-built photobioreactors, water samples from various lakes provided by the Polk County Parks and Recreation and from nearby ponds were used as the source of diatoms together with other algae obtained during collection. Results of the initial culture cycles were successful, but later an overgrowth of other algae crashed the diatom population. Experiments were conducted in the laboratory to tease out some factors possibly contributing to the die-off. Generally, the total biomass declines after two culture cycles and the causative factors need further investigation. The lipid yield is minimum; however, the high frustule production after die-off adds value to the overall benefit of the harvest.

Keywords: diatoms, algae, biofuel, lipid, photobioreactor, frustule

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Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

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When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

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Authors: Julia Gurol

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Why do we observe a shift towards convergence in EU-China security interests? While contradicting attitudes towards key principles of inter-state and region-to-state relations, including state sovereignty, territorial integrity, and intervention policies have ever since hindered EU-China inter-regional cooperation beyond the economic realm, collaboration in peace and security issues is now becoming a key pillar of European-Chinese relations. In addition, the Belt and Road Initiative as most ambitious Chinese foreign policy project explicitly touches upon several European foreign policy and security preferences. Based on these counterintuitive findings, this paper traces the process of convergence of Sino-European security interests. Drawing on qualitative text analysis of official Chinese and European policy papers and documents from the establishment of diplomatic relations in 1975 until today, it assesses the striking change over time. On this basis, the paper uses theories of neo-functionalism, inter-regionalism, and securitization and borrows from constructivist views in International Relations’ theory, to expound possible motives for the change in Chinese and respectively European preferences in the security realm. The results reveal interesting insights into the decisive factors and motives behind both countries’ foreign policies. The paper concludes with a discussion of further potential and difficulties of EU-China security cooperation.

Keywords: belt and road initiative, China, European Union, foreign policy, neo-functionalism, security

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Authors: Ahmed Al Khamisi

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The Disaster Preparedness Standard (DPS) is one of the elements that is evaluated by the Accreditation Canada International (ACI). ACI emphasizes to train and educate all staff, including service providers and senior leaders, on emergency and disaster preparedness upon the orientation and annually thereafter. Lack of awareness and deficit of knowledge among the healthcare providers about DPS have been noticed in a tertiary hospital where ACI standards were implemented. Therefore, this paper aims to introduce a video-based e-learning (VB-EL) module that explains the hospital’s disaster plan in a simple language which will be easily accessible to all healthcare providers through the hospital’s website. The healthcare disaster preparedness coordinator in the targeted hospital will be responsible to ensure that VB-EL is ready by 25 April 2019. This module will be developed based on the Kirkpatrick evaluation method. In fact, VB-EL combines different data forms such as images, motion, sounds, text in a complementary fashion which will suit diverse learning styles and individual learning pace of healthcare providers. Moreover, the module can be adjusted easily than other tools to control the information that healthcare providers receive. It will enable healthcare providers to stop, rewind, fast-forward, and replay content as many times as needed. Some anticipated limitations in the development of this module include challenges of preparing VB-EL content and resistance from healthcare providers.

Keywords: Accreditation Canada International, Disaster Preparedness Standard, Kirkpatrick evaluation method, video-based e-learning

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Authors: Zahra Hassani

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Background and Purpose: Poststroke depression (PSD) is one of the complications of a stroke that reduces the patient's chance of recovery, becomes irritable, and changes personality. Cognitive rehabilitation is one of the non-pharmacological methods that improve deficits such as attention, memory, and symptoms of depression. Therefore, the purpose of the present study is to evaluate the Efficacy of Cognitive Rehabilitation Therapy on Poststroke Depression among Survivors of stroke. Method: In this study, a systematic review of the databases Google Scholar, PubMed, Science Direct, Elsevier between the years 2015 and 2019 with the keywords cognitive rehabilitation therapy, post-stroke, depression Search is done. In this process, studies that examined the Efficacy of Cognitive Rehabilitation Therapy on Poststroke Depression among Survivors of stroke were included in the study. Results: Inclusion criteria were full-text availability, interventional study, and non-review articles. There was a significant difference between the articles in terms of the indices studied, sample number, method of implementation, and so on. A review of studies have shown that cognitive rehabilitation therapy has a significant role in reducing the symptoms of post-stroke depression. The use of these interventions is also effective in improving problem-solving skills, improving memory, and improving attention and concentration. Conclusion: This study emphasizes on the development of efficient and flexible adaptive skills through cognitive processes and its effect on reducing depression in patients after stroke.

Keywords: cognitive therapy, depression, stroke, rehabilitation

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Authors: Esther Matthews

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This paper looks at ways of translating new metaphors: those conceived and created by authors, which are often called ‘original’ metaphors in the world of Translation Studies. An original metaphor is the most extreme form of figurative language, often dramatic and shocking in effect. It displays unexpected juxtapositions of language, suggesting there could be as many different translations as there are translators. However, some theorists say original metaphors should be translated ‘literally’ or ‘word for word’ as far as possible, suggesting a similarity between translators’ solutions. How do literary translators approach this challenge? This study focuses on Spanish-English translations of a novel full of original metaphors: Nada by Carmen Laforet (1921 – 2004). Original metaphors from the text were compared to the four published English translations by Inez Muñoz, Charles Franklin Payne, Glafyra Ennis, and Edith Grossman. These four translators employed a variety of translation methods, but they translated ‘literally’ in well over half of the original metaphors studied. In a two-part translation exercise and questionnaire, professional literary translators were asked to translate a number of these metaphors. Many different methods were employed, but again, over half of the original metaphors were translated literally. Although this investigation was limited to one author and language pair, it gives a clear indication that, although literary translators’ solutions vary, on the whole, they prefer to translate original metaphors as literally as possible within the confines of English grammar and syntax. It also reveals literary translators’ desire to reproduce the distinctive character of an author’s work as accurately as possible for the target reader.

Keywords: translation, original metaphor, literature, translator training

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Mostafa Gamal, Hoda K. Mohamed, Islam El-Maddah, Ali Hamdi

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In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs.

Keywords: graph databases, NLP, recommendation systems, similarity metrics

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Authors: Toby Gibbs, Suwaree Yordchim

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This research analyzes factors affecting the success of Litecoin Value within Thailand and develops a guideline for self-reliance for effective business implementation. Samples in this study included 119 people through surveys. The results revealed four main factors affecting the success as follows: 1) Future Career training should be pursued in applied Litecoin development. 2) Didn't grasp the concept of a digital currency or see the benefit of a digital currency. 3) There is a great need to educate the next generation of learners on the benefits of Litecoin within the community. 4) A great majority didn't know what Litecoin was. The guideline for self-reliance planning consisted of 4 aspects: 1) Development planning: by arranging meet up groups to conduct further education on Litecoin and share solutions on adoption into every day usage. Local communities need to develop awareness of the usefulness of Litecoin and share the value of Litecoin among friends and family. 2) Computer Science and Business Management staff should develop skills to expand on the benefits of Litecoin within their departments. 3) Further research should be pursued on how Litecoin Value can improve business and tourism within Thailand. 4) Local communities should focus on developing Litecoin awareness by encouraging street vendors to accept Litecoin as another form of payment for services rendered.

Keywords: litecoin, mining, confirmations, payment method

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Authors: Anh Duong

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The three-phase reading lesson has been put forth around the world as the new and innovative framework which is corresponding to the learner-centered trend in English language teaching and learning. Among three stages, pre-reading attracts many teachers’ and researchers’ attention for its vital role in preparing students with knowledge and interest in reading class. The researcher’s desire to exemplify effectiveness of activities prior to text reading has provoked the current study. Three main aspects were investigated in this paper, i.e. teachers’ and student’s perception of pre-reading stage, teachers’ exploitation of pre-reading techniques and teachers’ recommendation of effective pre-reading activities. Aiming at pre-reading techniques for first-year students at English Department, this study involved 200 fresh-men and 10 teachers from Division 1 to participate in the questionnaire survey. Interviews with the teachers and classroom observation were employed as a tool to take an insight into the responses gained from the early instrument. After a detailed procedure of analyzing data, the researcher discovered that thanks to the participants’ acclamation of pre-reading stage, this phase was frequently conducted by the surveyed teachers. Despite the fact that pre-reading activities apparently put a hand in motivating students to read and creating a joyful learning atmosphere, they did not fulfill another function as supporting students’ reading comprehension. Therefore, a range of techniques and notices when preparing and conducting pre-reading phase was detected from the interviewed teachers. The findings assisted the researcher to propose some related pedagogical implications concerning teachers’ source of pre-reading techniques, variations of suggested activities and first-year reading syllabus.

Keywords: pre-reading stage, pre-reading techniques, teaching reading, language teaching

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Yun-Ju Chiu, Feng-Yi Chen

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Kinematics is the beginning to learn mechanics in physics course. The concept of acceleration plays an important role in learning kinematics. Teachers usually instruct the conception through the formulas and graphs of kinematics and the well-known law F = ma. However, over the past few decades, a lot of researchers reveal numerous students’ difficulties in learning acceleration. One of these difficulties is that students frequently confuse acceleration with velocity and force. Why is the concept of acceleration so difficult to learn? The aim of this study is to understand the conceptual evolution of acceleration through the historical textual analysis. Text analysis and one-to-one interviews with high school students and teachers are used in this study. This study finds the history of science constructed from textbooks is usually quite different from the real evolution of history. For example, most teachers and students believe that the best-known law F = ma was written down by Newton. The expression of the second law is not F = ma in Newton’s best-known book Principia in 1687. Even after more than one hundred years, a famous Cambridge textbook titled An Elementary Treatise on Mechanics by Whewell of Trinity College did not express this law as F = ma. At that time of Whewell, the early mid-nineteenth century Britain, the concept of acceleration was not only ambiguous but also confused with the concept of force. The process of learning the concept of acceleration is analogous to its conceptual development in history. The study from the perspective of historical textual analysis will promote the understanding of the concept learning difficulties, the development of professional physics teaching, and the improvement of the context of physics textbooks.

Keywords: acceleration, textbooks, mechanics, misconception, history of science

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Authors: Bishal Baniya

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This paper proposes the sustainability related policy areas for decarbonisation and value addition at an organizational level. General and public sector organizations around the world are usually significant in terms of consuming resources and producing waste – powered through their massive procurement capacity. However, these organizations also possess huge potential to cut resource use and emission as many of these organizations controls supply chain of goods/services. They can therefore be a trend setter and can easily lead other major economic sectors such as manufacturing, construction and mining, transportation, etc. in pursuit towards paradigm shift for sustainability. Whilst the environmental and social awareness has improved in recent years and they have identified policy areas to improve the organizational environmental performance, value addition to the core business of the organization hasn’t been understood and interpreted correctly. This paper therefore investigates ways to align sustainability policy measures in a way that it creates better value proposition relative to benchmark by accounting both eco and social efficiency. Preliminary analysis shows co-benefits other than resource and cost savings fosters the business cases for organizations and this can be achieved by better aligning the policy measures and engaging stakeholders.

Keywords: policy measures, environmental performance, value proposition, organisational level

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Authors: Ari Istiany, Guspri Devi Artanti, M. Si

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Based on the results Riskesdas, it is known that breastfeeding awareness about the importance of exclusive breastfeeding is still low at only 15.3 %. These conditions resulted in a very infant at risk for infectious diseases, such as diarrhea and acute respiratory infection. Therefore, the aim of this study to evaluate the video development about exclusive breastfeeding as a nutrition education media for posyandu cadre. This research used development methods for making the video about exclusive breastfeeding. The study was conducted in urban areas Rawamangun, East Jakarta. Respondents of this study were 1 media experts from the Department of Educational Technology - UNJ, 2 subject matter experts from Department of Home Economics - UNJ and 20 posyandu cadres to assess the quality of the video. Aspects assessed include the legibility of text, image display quality, color composition, clarity of sound, music appropriateness, duration, suitability of the material and language. Data were analyzed descriptively likes frequency distribution table, the average value, and deviation standard. The result of this study showed that the average score assessment according to media experts, subject matter experts, and posyandu cadres respectively was 3.43 ± 0.51 (good), 4.37 ± 0.52 (very good) and 3.6 ± 0.73 (good). The conclusion is on exclusive breastfeeding video as feasible as a media for nutrition education. While suggestions for the improvement of visual media is multiply illustrations, add material about the correct way of breastfeeding and healthy baby pictures.

Keywords: exclusive breastfeeding, posyandu cadre, video, nutrition education

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Authors: Tamanna Goyal, Divya Bansal, Sanjeev Sofat

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In world-threatening terrorist attacks, where early detection, distinction, and prediction are effective diagnosis techniques and for functionally accurate and precise analysis of terrorism data, there are so many data mining & statistical approaches to assure accuracy. The computational extraction of derived patterns is a non-trivial task which comprises specific domain discovery by means of sophisticated algorithm design and analysis. This paper proposes an approach for similarity extraction by obtaining the useful attributes from the available datasets of terrorist attacks and then applying feature selection technique based on the statistical impurity measures followed by clustering techniques on the basis of similarity measures. On the basis of degree of participation of attributes in the rules, the associative dependencies between the attacks are analyzed. Consequently, to compute the similarity among the discovered rules, we applied a weighted similarity measure. Finally, the rules are grouped by applying using hierarchical clustering. We have applied it to an open source dataset to determine the usability and efficiency of our technique, and a literature search is also accomplished to support the efficiency and accuracy of our results.

Keywords: association rules, clustering, similarity measure, statistical approaches

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Aziz Larbi, Widad Sadok

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In Morocco, the mountainous regions extend over about 26% of the national territory where 30% of the total population live. They contain opportunities for agriculture, forestry, pastureland and mining. The production systems in these zones are characterised by crop diversification. However, these areas have become vulnerable to the effects of climate change. To understand these effects in relation to the population living in these areas, a study was carried out in the zone of Taliouine, in the Anti-Atlas. The vulnerability of crop productions to climate change was analysed and the different ways of adaptation adopted by farmers were identified. The work was done on saffron, the most profitable crop in the target area even though it requires much water. Our results show that the majority of the farmers surveyed had noticed variations in the climate of the region: irregularity of precipitation leading to a decrease in quantity and an uneven distribution throughout the year; rise in temperature; reduction in the cold period and less snow. These variations had impacts on the cropping system of saffron and its productivity. To cope with these effects, the farmers adopted various strategies: better management and use of water; diversification of agricultural activities; increase in the contribution of non-agricultural activities to their gross income; and seasonal migration.

Keywords: climate change, Taliouine, saffron, perceptions, adaptation strategies

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Authors: Kuan-Han Lin, Kuan-Yin Lin, Ka-Chun Siu

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The purpose of this systematic review was to determine the impact of post-traumatic stress disorders (PTSD) symptom or depressive symptoms on return to work (RTW) after occupational injury. The original articles of clinical trials and observational studies from PubMed, MEDLINE, and PsycINFO between January 1980 and November 2016 were retrieved. Two reviewers evaluated the abstracts identified by the search criteria for full-text review. To be included in the final analysis, studies were required to use either intervention or observational study design to examine the association between psychiatric symptoms and RTW. A modified checklist designed by Downs & Black and Crombie was used to assess the methodological quality of included study. A total of 58 articles were identified from the electronic databases after duplicate removed. Seven studies fulfilled the inclusion criteria and were critically reviewed. The rates of RTW in the included studies were reported to be 6% to 63.6% among workers after occupational injuries. This review found that post-traumatic stress symptom and depressive symptoms were negatively associated with RTW. Although the impact of psychiatric symptoms on RTW after occupational injury remains poorly understood, this review brought up the important information that injured workers with psychiatric symptoms had poor RTW outcome. Future work should address the effective management of psychiatric factors affecting RTW among workers.

Keywords: depressive symptom, occupational injury, post-traumatic stress disorder, return to work

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Authors: Khushboo Gokani, Renu Josan

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Mahasweta Devi and Toni Morrison are the two stalwarts of the Indian-English and the Afro-American literature respectively. The writings of these two novelists are authentic and powerful records of the lives of the people because much of their personal experiences have gone into the making of their works. Devi, a representative force of the Indian English literature, is also a social activist working with the tribals of Bihar, Jharkhand, Orissa and West Bengal. Most of her works echo the lives and struggles of the subalterns as is evident in her 'best-beloved book' Chotti Munda and His Arrow. The novelist focuses on the struggle of the tribals against the colonial and the feudal powers to create their identity, thereby, embarking on the ideological project called Setting the Record Straight. The Nobel laureate Toni Morrison, on the other hand, brings to the fore the crucial issues of gender, race, and class in many of her significant works. In one of her representative works, Sula, the protagonist emerges as a non-conformist and directly confronts the notion of a ‘good woman’ nurtured by the community of the Blacks. In addition to this, the struggle of the Blacks against the White domination, also become an important theme of the text. The thrust of the paper lies in making a critical analysis of the portrayal of the heroic attempts of the subaltern protagonist and the artistic endeavor of the novelists in challenging the stereotypes.

Keywords: the struggle of the muted groups, subaltern, center and periphery, challenging the stereotypes

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Authors: Sahib Dino Naseerani

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The presented scientific text discusses the concept of imaginary pressure and its role in different relative motions. It explores how imaginary pressure, which is the combined effect of external atmospheric pressure and real pressure, affects various substances and their physical properties. The study aims to understand the impact of imaginary pressure and its potential applications in different contexts, such as spaceflight. The main objective of this study is to investigate the role of imaginary pressure on different relative motions. Specifically, the researchers aim to examine how imaginary pressure affects the contraction and mass variation of a body when it is in motion at the speed of light. The study seeks to provide insights into the behavior and consequences of imaginary pressure in various scenarios. The data was collected using three research papers. This research contributes to a better understanding of the theoretical implications of imaginary pressure. It elucidates how imaginary pressure is responsible for the contraction and mass variation of a body in motion, particularly at the speed of light. The findings shed light on the behavior of substances under the influence of imaginary pressure, providing valuable insights for future scientific studies. The study addresses the question of how imaginary pressure influences various relative motions and their associated physical properties. It aims to understand the role of imaginary pressure in the contraction and mass variation of a body, particularly at high speeds. By examining different substances in liquid and solid forms, the research explores the consequences of imaginary pressure on their volume, length, and mass.

Keywords: imaginary pressure, contraction, variation, relative motion

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Authors: Vivien Chee Pei Wei

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Today we live in a scenario in which letters share space with images on screens that vary in size, shape, and style. The popularization of television, then the computer and now the e-readers, tablets, and smartphones made the electronic assume the role that previously was restricted to printed materials. Since the extensive use of new technologies to produce, disseminate, collect and access electronic publications began, the changes to reading has been intensified. To be able to read online, it involves more than just utilizing specific skills, strategies, and practices, but also in negotiating multiple information sources. In this study, different perspectives of digital reading are being explored in order to define the key aspects of the term. The focus is to explore how new technologies affect how undergraduates’ reading behavior, which in turn, gives readers different reading levels and engagement with the text and other support materials in the same media. There is also the importance of the relationship between reading platforms, reading levels and formats of electronic publications. The study looks at the online reading practices of about 100 undergraduates from a local university. The data collected using the survey and interviews with the respondents are analyzed thematically. Findings from this study found that both digital and traditional reading are interrelated, and should not be viewed as separate, but complementary to each other. However, reading online complicates some of the skills required by traditional reading. Consequently, in order to successfully read and comprehend multiple sources of information online, undergraduates need regular opportunities to practice and develop their skills as part of their natural reading practices.

Keywords: concepts, digital reading, literacy, traditional reading

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Authors: Muhammad Fadli Muslimin

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Negeri Para Bedebah is known as one of the works of Tere Liye, an Indonesia author. In the literary works, the fiction as always tries to reflect the reality of the society where the author or the social groups lived in. The essential or nature of society is generally a reality while literary work is fiction and both of them are social fact. Negeri Para Bedebah is a novel fiction which is a social fact and which holds an important role in reality. It is more likely as the representation of social, economy and politic aspects in Indonesia. The purpose of this study is to reveal the world view of Tere Liye throughout novel Negeri Para Bedebah. By analyzing the object using genetic structuralism Lucien Goldmann which chiefly focuses on world view, it is stated that the literary work is an structure and it has homology with the structure in society. The structure of literary work is not chiefly homolog to the structure of society but homolog to the world view which is growing and developing inside the society. The methodological research used in this paper is a dialectic method which focuses on the starting and ending points lied in the literary text by paying attention to the coherent meanings. The result of this study is that Tere Liye shows us his world view about the structure of the society where he is living in, but one is an imaginative form of the world and the homology to the reality itself.

Keywords: homology, literary work, society, structure, world view

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Muhammad Shakeel Rehman Hissam

Abstract:

This study aims at analyzing gender in Othello by applying Judith Butler’s Post-Structural theory of gender and gender performance. The analysis of the play provides us context by which we can examine what kinds of effects the drama have on understanding of the researchers regarding gender identity. The study sets out to examine that, is there any evidence or ground in Shakespearean selected work which leads to challenge the patriarchal taken for granted prescribed roles of gender? This would be the focal point in study of Othello that actions and performances of characters determine their gender identity rather than their sexuality. It argues that gender of Shakespearean characters has no constant, fixed and structural impression. On the contrary, they undergo consistent variations in their behavior and performance which impart fluidity and volatility to them. The focal point of the present study is Butler’s prominent work; Gender Trouble: Feminism and subversion of Identity and her post structural theory of Gender performativity as the theoretical underpinning of the text. It analyzes the selected play in Post-Structural gender perspective. The gender-centric plot of the play is riddled with fluidity of gender. The most fascinating aspect of the play is the transformations of genders on the basis of performances by different characters and through these transformations; gender identity is revealed and determined. The study reconstructs the accepted gender norms by challenging the traditional concept of gender that is based on sexual differences of characters.

Keywords: post structural, gender, performativity, socio-cultural gender norms, binaries, Othello, Butler, identity

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

Abstract:

Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

Abstract:

Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Sara Herrero Jaén, José María Santamaría García, María Lourdes Jiménez Rodríguez, Jorge Luis Gómez González, Adriana Cercas Duque, Alexandra González Aguna

Abstract:

Background: Health concept has evolved throughout history. The health level is determined by the own individual perception. It is a dynamic process over time so that you can see variations from one moment to the next. In this way, knowing the health of the patients you care for, will facilitate decision making in the treatment of care. Objective: To design a technological tool that calculates the people health level in a sequential way over time. Material and Methods: Deductive methodology through text analysis, extraction and logical knowledge formalization and education with expert group. Studying time: September 2015- actually. Results: A computational tool for the use of health personnel has been designed. It has 11 variables. Each variable can be given a value from 1 to 5, with 1 being the minimum value and 5 being the maximum value. By adding the result of the 11 variables we obtain a magnitude in a certain time, the health level of the person. The health calculator allows to represent people health level at a time, establishing temporal cuts being useful to determine the evolution of the individual over time. Conclusion: The Information and Communication Technologies (ICT) allow training and help in various disciplinary areas. It is important to highlight their relevance in the field of health. Based on the health formalization, care acts can be directed towards some of the propositional elements of the concept above. The care acts will modify the people health level. The health calculator allows the prioritization and prediction of different strategies of health care in hospital units.

Keywords: calculator, care, eHealth, health

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Authors: Kun Xu, Yuan Xu, Jia Qiao

Abstract:

The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

Procedia PDF Downloads 332