Search results for: internet data science

Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Delia Enyedi

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Throughout film history, the regular return of 3D cinema has been discussed in connection to crises caused by the advent of television or the competition of the Internet. In addition, the three waves of stereoscopic 3D (from 1952 up to 1983) and its current digital version have been blamed for adding a challenging technical distraction to the viewing experience. By discussing the films Dial M for Murder (1954) and Goodbye to Language (2014), the paper aims to analyze the response of recognized auteurs to the use of 3D techniques in filmmaking. For Alfred Hitchcock, the solution to attaining perceptual immersion paradoxically resided in restraining the signature effect of 3D, namely protrusion. In Jean-Luc Godard’s vision, 3D techniques allowed him to explore perceptual absorption by means of depth of field, for which he had long advocated as being central to cinema. Thus, both directors contribute to the foundation of an auteur aesthetic in 3D filmmaking.

Keywords: Alfred Hitchcock, authorship, 3D filmmaking, Jean-Luc Godard, perceptual absorption, perceptual immersion

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Authors: Wuttigrai Ngamsirijit

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Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.    

Keywords: decision making, human capital analytics, talent management, talent value chain

Procedia PDF Downloads 187

Authors: Iris Xiaohong Quan

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The global digital health market reached 175 billion U.S. dollars in 2019, and is expected to grow at about 25% CAGR to over 650 billion USD by 2025. Different terms such as digital health, e-health, mHealth, telehealth have been used in the field, which can sometimes cause confusion. The term digital health was originally introduced to refer specifically to the use of interactive media, tools, platforms, applications, and solutions that are connected to the Internet to address health concerns of providers as well as consumers. While mHealth emphasizes the use of mobile phones in healthcare, telehealth means using technology to remotely deliver clinical health services to patients. According to FDA, “the broad scope of digital health includes categories such as mobile health (mHealth), health information technology (IT), wearable devices, telehealth and telemedicine, and personalized medicine.” Some researchers believe that digital health is nothing else but the cultural transformation healthcare has been going through in the 21st century because of digital health technologies that provide data to both patients and medical professionals. As digital health is burgeoning, but research in the area is still inadequate, our paper aims to clear the definition confusion and provide an overall picture of digital health companies. We further investigate how business models are designed and differentiated in the emerging digital health sector. Both quantitative and qualitative methods are adopted in the research. For the quantitative analysis, our research data came from two databases Crunchbase and CBInsights, which are well-recognized information sources for researchers, entrepreneurs, managers, and investors. We searched a few keywords in the Crunchbase database based on companies’ self-description: digital health, e-health, and telehealth. A search of “digital health” returned 941 unique results, “e-health” returned 167 companies, while “telehealth” 427. We also searched the CBInsights database for similar information. After merging and removing duplicate ones and cleaning up the database, we came up with a list of 1464 companies as digital health companies. A qualitative method will be used to complement the quantitative analysis. We will do an in-depth case analysis of three successful unicorn digital health companies to understand how business models evolve and discuss the challenges faced in this sector. Our research returned some interesting findings. For instance, we found that 86% of the digital health startups were founded in the recent decade since 2010. 75% of the digital health companies have less than 50 employees, and almost 50% with less than 10 employees. This shows that digital health companies are relatively young and small in scale. On the business model analysis, while traditional healthcare businesses emphasize the so-called “3P”—patient, physicians, and payer, digital health companies extend to “5p” by adding patents, which is the result of technology requirements (such as the development of artificial intelligence models), and platform, which is an effective value creation approach to bring the stakeholders together. Our case analysis will detail the 5p framework and contribute to the extant knowledge on business models in the healthcare industry.

Keywords: digital health, business models, entrepreneurship opportunities, healthcare

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Umair Shoukat Ali, Kamil Zafar, Rashna Hoshang Sukhia, Mubassar Fida, Aqeel Ahmed

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Objective: This systematic review and meta-analysis aimed to summarize the effectiveness of bonded and removable retainers (Hawley and Essix retainer) in terms of improvement in occlusal settling (occlusal contact points/areas) after orthodontic treatment. Search Method: We searched the Cochrane Library, CINAHL Plus, PubMed, Web of Science, Orthodontic journals, and Google scholar for eligible studies. We included randomized control trials (RCT) along with Cohort studies. Studies that reported occlusal contacts/areas during retention with fixed bonded and removable retainers were included. To assess the quality of the RCTs Cochrane risk of bias tool was utilized, whereas Newcastle-Ottawa Scale was used for assessing the quality of cohort studies. Data analysis: The data analysis was limited to reporting mean values of occlusal contact points/areas with different retention methods. By utilizing the RevMan software V.5.3, a meta-analysis was performed for all the studies with the quantitative data. For the computation of the summary effect, a random effect model was utilized in case of high heterogeneity. I2 statistics were utilized to assess the heterogeneity among the selected studies. Results: We included 6 articles in our systematic review after scrutinizing 219 articles and eliminating them based on duplication, titles, and objectives. We found significant differences between fixed and removable retainers in terms of occlusal settling within the included studies. Bonded retainer (BR) allowed faster and better posterior tooth settling as compared to Hawley retainer (HR). However, HR showed good occlusal settling in the anterior dental arch. Essix retainer showed a decrease in occlusal contact during the retention phase. Meta-analysis showed no statistically significant difference between BR and removable retainers. Conclusions: HR allowed better overall occlusal settling as compared to other retainers in comparison. However, BR allowed faster settling in the posterior teeth region. Overall, there are insufficient high-quality RCTs to provide additional evidence, and further high-quality RCTs research is needed.

Keywords: orthodontic retainers, occlusal contact, Hawley, fixed, vacuum-formed

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Authors: Balarabe Bilyaminu Ismail, Osman Cavus, Fügen Durlu Özkaya

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This study is aimed at determining the level of awareness of chefs and students of food science and technology on food safety in general and allergens in particular. To get appropriate data, a questionnaire comprising of 19 questions covering many food safety issues and allergens in foods were used to collect information for the study through face to face interviews. Interviews were conducted for 284 people in Nigeria and Turkey. Sixty-eight percent of respondents from Turkey; 31.3% were students and 68.7% were chefs. Thirty-one percent of respondents from Nigeria include 33.7% students and 66.3% chefs. The result of the study indicated that most of the findings of scientific studies on food safety issues have not been directly applied by the people working in the food sector. Additionally, the knowledge level of the gastronomy and culinary arts students on food safety and allergens are significantly higher than the restaurant chefs that prepare the food and serve it to the public. The study, therefore, concluded that proper training of food business operators is critical to ensuring the safety of foods and control of allergens.

Keywords: allergens, food safety, questionnaire survey, training

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Rachid Ed-Dali, Khalid Elasri

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Mass media seem to provide a rich content for language acquisition. Exposure to television, the Internet, the mobile phone and other technological gadgets and devices helps enrich the student’s lexicon positively as well as negatively. The difficulties encountered by students while learning and acquiring second languages in addition to their eagerness to comprehend the content of a particular program prompt them to diversify their methods so as to achieve their targets. The present study highlights the significance of certain media channels and their involvement in language acquisition with the employment of the Natural Approach to further grasp whether students, especially secondary and high school students, learn and acquire errors through watching subtitled television programs. The chief objective is investigating the deductive and inductive relevance of certain programs beside the involvement of peripheral learning while acquiring mistakes.

Keywords: errors, mistakes, Natural Approach, peripheral learning, solecism

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Authors: Babajide Olufemi Diyaolu

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The opportunity provided by advances in technology as regards sports betting is so numerous that even at one's comfort, with the use of a phone, Nigerian youth are found engaging in all kinds of betting. Today it is more difficult to differentiate a true fan as there are quite a number of them that became fans as a result of betting on live games. This study investigated the influence of online sports events on betting among Nigerian youth. A descriptive survey research design was used, and the population consists of all Nigerian youth that engages in betting and live within the southwest zone of Nigeria. A simple random sampling technique was used to pick three states from the southwest zone of Nigeria. Two thousand five hundred respondents comprising males and female were sampled from the three states. A structured questionnaire on online sports event contribution to sports betting (OSECSB) was used. The Instrument consists of three sections. Section A seeks information on the demographic data of the respondents. Section B seeks information on online sports events, while section C is used to extract information on sports betting. The modified instrument, which consists of 14 items, has a reliability coefficient of 0.74. The hypothesis was tested at 0.05 significance level. The completed questionnaire was collated, coded, and analyzed using descriptive statistics of frequency counts, percentage and pie chart, and inferential statistics of multiple regressions. The findings of this study revealed that online sports betting is a significant predictor of an increase in sports betting among Nigerian youth. The media and television, as well as globalization and the internet coupled with social media and various online platforms, have all contributed to the immense increase in sports betting. The increase in the advertisement of the betting platform during live matches, especially football, is becoming more alarming. In most organized international events, the media attention, as well as sponsorship right, are now been given to one or two betting platforms. There is a need for all stakeholders to put in place school-based intervention programs to reorientate our youth about the consequences of addiction to betting. Such programs must include meta-analyses and emotional control towards sports betting.

Keywords: betting platform, Nigerian fans, Nigerian youth, sports betting

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Authors: Boussoualim Nacira, Aklouf Youcef

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Software as a Service (SaaS) is a software delivery paradigm in which the product is not installed on-premise, but it is available on Internet and Web. The customers do not pay to possess the software itself but rather to use it. This concept of pay per use is very attractive. Hence, we see increasing number of organizations adopting SaaS. However, each customer is unique, which leads to a very large variation in the requirements off the software. As several suppliers propose SaaS products, the choice of this latter becomes a major issue. When multiple criteria are involved in decision making, we talk about a problem of «Multi-Criteria Decision-Making» (MCDM). Therefore, this paper presents a method to help customers to choose a better SaaS product satisfying most of their conditions and alternatives. Also, we know that a good method of adaptive selection should be based on the correct definition of the different parameters of choice. This is why we started by extraction and analysis the various parameters involved in the process of the selection of a SaaS application.

Keywords: cloud computing, business operation, Multi-Criteria Decision-Making (MCDM), Software as a Service (SaaS)

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Authors: Wookyong Kwon, Sangmoon Lee

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In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.

Keywords: model predictive control, sampled-data control, linear parameter varying systems, LPV

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Authors: Nasser A. Al-Azri

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The effectiveness of passive cooling techniques is assessed based on bioclimatic charts that require the typical meteorological year (TMY) for a specified location for their development. However, TMYs are not always available; mainly due to the scarcity of records of solar radiation which is an essential component used in developing common TMYs intended for general uses. Since solar radiation is not required in the development of the bioclimatic chart, this work suggests developing TMYs based solely on the relevant parameters. This approach improves the accuracy of the developed TMY since only the relevant parameters are considered and it also makes the development of the TMY more accessible since solar radiation data are not used. The presented paper will also discuss the development of the TMY from the raw data available at the NOAA-NCDC archive of world weather data and the construction of the bioclimatic charts for some randomly selected locations around the world.

Keywords: bioclimatic charts, passive cooling, TMY, weather data

Procedia PDF Downloads 240

Authors: Mehmet Kargaci

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Transformation of technology and strategy has been the main factor for the evolution of war. In addition to land, maritime, air and space domains, cyberspace has become the fifth domain with emerge of internet. The current security environment has become more complex and uncertain than ever before. Moreover, warfare has evaluated from conventional to irregular, asymmetric and hybrid war. Weak actors such as terrorist organizations and non-state actors has increasingly conducted cyber-attacks against strong adversaries. Besides, states has developed cyber capabilities in order to defense critical infrastructure regarding the cyber threats. Cyber warfare will be key in future security environment. Although what to do has been placed in operational plans, how to do has lacked and ignored as to cyber defense and attack. The purpose of the article is to put forward a model for how to conduct cyber capabilities in a conventional war. First, cyber operations capabilities will be discussed. Second put forward the necessities of cyberspace environment and develop a model for how to plan an operation using cyber operation capabilities, finally the assessment of the applicability of cyber operation capabilities and offers will be presented.

Keywords: cyber war, cyber threats, cyber operation capabilities, operation planning

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Authors: Tuğçe Yavaş Akış

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In recent years, due to an increased use of radioactive materials, radioactive sources are constantly being transported via air, road and ocean ways for medical, industrial, research etc. purposes throughout the world. The quantity of radioactive materials transported all around the world varies from negligible quantities in shipments of consumer products to very large quantities in shipments of irradiated nuclear fuel. Radioactive materials have been less attractive for social science researchers in literature. In this study, it is aimed to discover desired flow of radioactive materials from logistics service quality (LSQ) perspective. In doing so, case study approach will be employed by using secondary data collected from one of the world’s leading transportation companies’ customer care system reports. Movement of radioactive cargoes containing IR-192 and logistics process will be analyzed with the help of logistics service quality dimensions. Based on the case study that will be conducted, interaction between dimensions, the importance of each dimension in desired flow, and their relevance with desired flow of radioactive materials will be explained. This study will bring out the desired flow of radioactive materials transportation and be a guide for all other companies, employees and researchers.

Keywords: logistics service quality, LSQ dimension , radioactive material, transportation

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Authors: D. Nam Kim, D. Jin Kim, Jeonghwan Jeon

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Defense Defensive Modeling and Simulation (M&S) is a system which enables impracticable training for reducing constraints of time, space and financial resources. The necessity of defensive M&S has been increasing not only for education and training but also virtual fight. Soldiers who are using defensive M&S for education and training will obtain empirical knowledge and know-how. However, the obtained knowledge of individual soldiers have not been managed and utilized yet since the nature of military organizations: confidentiality and frequent change of members. Therefore, this study aims to develop a management system for the experience of defensive M&S based on data mining approach. Since individual empirical knowledge gained through using the defensive M&S is both quantitative and qualitative data, data mining approach is appropriate for dealing with individual empirical knowledge. This research is expected to be helpful for soldiers and military policy makers.

Keywords: data mining, defensive m&s, management system, knowledge management

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Authors: Girma Yohannis Bade, Olga Kolesnikova, Grigori Sidorov

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Named entity recognition (NER) is a building block for information extraction. Though the information extraction process has been automated using a variety of techniques to find and extract a piece of relevant information from unstructured documents, the discovery of targeted knowledge still poses a number of research difficulties because of the variability and lack of structure in Web data. NER, a subtask of information extraction (IE), came to exist to smooth such difficulty. It deals with finding the proper names (named entities), such as the name of the person, country, location, organization, dates, and event in a document, and categorizing them as predetermined labels, which is an initial step in IE tasks. This survey paper presents the roles and importance of NER to IE from the perspective of different algorithms and application area domains. Thus, this paper well summarizes how researchers implemented NER in particular application areas like finance, medicine, defense, business, food science, archeology, and so on. It also outlines the three types of sequence labeling algorithms for NER such as feature-based, neural network-based, and rule-based. Finally, the state-of-the-art and evaluation metrics of NER were presented.

Keywords: the role of NER, named entity recognition, information extraction, sequence labeling algorithms, named entity application area

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Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Eusebio Odiari, Mark Birkin, Susan Grant-Muller, Nicolas Malleson

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Big data typically refers to consumer datasets revealing some detailed heterogeneity in human behavior, which if harnessed appropriately, could potentially revolutionize our understanding of the collective phenomena of the physical world. Inadvertent missing values skew these datasets and compromise the validity of the thesis. Here we discuss a conceptually consistent strategy for identifying other relevant datasets to combine with available big data, to plug the gaps and to create a rich requisite comprehensive dataset for subsequent analysis. Specifically, emphasis is on how these methodologies can for the first time enable the construction of more detailed pictures of passenger demand and drivers of mobility on the railways. These methodologies can predict the influence of changes within the network (like a change in time-table or impact of a new station), explain local phenomena outside the network (like rail-heading) and the other impacts of urban morphology. Our analysis also reveals that our new imputation data model provides for more equitable revenue sharing amongst network operators who manage different parts of the integrated UK railways.

Keywords: big-data, micro-simulation, mobility, ticketing-data, commuters, transport, synthetic, population

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Authors: Muhammedamin Hussen Saad, Geoffrey Haagler, Onno Omta, Gerben Van Der Velde

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Unfolding chaos and persistent disruptions pose threats to companies’ performance especially in vulnerable settings of SME’s particularly in developing countries. Attention for resilience research in the academic world has increased considerably during the last decade looking at the number of papers published. As we are interested in adding to the understanding of the foundation and development of the concept of resilience, we focus especially on structuring the literature of business resilience in those vulnerable settings. A well-structured systematic search & review procedure was deployed. First, we defined key search terms and applied these to multiple databases (Scopus, Web of Science, Google Scholar, Emerald, and Science Direct). To make our literature search more encompassing, we augmented with co-citation, reference checking including hand searching techniques. The paper offers (1) an overview of SMEs resilience literature from 2000 up to March 2017 comprising 88 articles, and (2) special attention, within that overview, to developing countries. This review concludes that resilience literature is very much diverse in definitions and its measurements, and is inconclusive about its influencing factors. Furthermore, resilience literature is based predominantly on research in the developed world. On the bases of how the concept resilience emerges from the literature we describe distinct features of resilience, give options to extend the theoretical bases of research into resilience and describe concrete ideas for further research.

Keywords: business resilience, systematic review, SMEs, developing countries

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Authors: Randy D. Colon

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Through a freedom of information request, data pertaining to Housing Choice Voucher (HCV) households has been obtained from the Chicago Housing Authority, including rent price and number of bedrooms per HCV household, community area, and zip code from 2013 to the first quarter of 2018. Similar data pertaining to the private rental market will be obtained through public records found through the United States Department of Housing and Urban Development. The datasets will be analyzed through statistical and mapping software to investigate the potential link between HCV households and distorted rent prices. Quantitative data will be supplemented by qualitative data to investigate the lived experience of Chicago residents. Qualitative data will be collected at community meetings in the Chicago Englewood neighborhood through participation in neighborhood meetings and informal interviews with residents and community leaders. The qualitative data will be used to gain insight on the lived experience of community leaders and residents of the Englewood neighborhood in relation to housing, the rental market, and HCV. While there is an abundance of quantitative data on this subject, this qualitative data is necessary to capture the lived experience of local residents effected by a changing rental market. This topic reflects concerns voiced by members of the Englewood community, and this study aims to keep the community relevant in its findings.

Keywords: Chicago, housing, housing choice voucher program, housing subsidies, rental market

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Authors: Amir Tosson, Mohammad Reza, Christian Gutt

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Metadata stands at the forefront of advancing data management practices within research communities, with particular significance in the realms of neutron and photon scattering. This paper introduces a groundbreaking approach—dynamic metadata schema—within the context of X-ray Photon Correlation Spectroscopy (XPCS). XPCS, a potent technique unravelling nanoscale dynamic processes, serves as an illustrative use case to demonstrate how dynamic metadata can revolutionize data acquisition, sharing, and analysis workflows. This paper explores the challenges encountered by the neutron and photon communities in navigating intricate data landscapes and highlights the prowess of dynamic metadata in addressing these hurdles. Our proposed approach empowers researchers to tailor metadata definitions to the evolving demands of experiments, thereby facilitating streamlined data integration, traceability, and collaborative exploration. Through tangible examples from the XPCS domain, we showcase how embracing dynamic metadata standards bestows advantages, enhancing data reproducibility, interoperability, and the diffusion of knowledge. Ultimately, this paper underscores the transformative potential of dynamic metadata, heralding a paradigm shift in data management within the neutron and photon research communities.

Keywords: metadata, FAIR, data analysis, XPCS, IoT

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Authors: Jae Young Park, Hwansu Jung, Jong Tae Kim

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Whether the data has been well parallelized is an important factor in the Solid-State-Drive (SSD) performance. SSD parallelization is affected by allocation scheme and it is directly connected to SSD performance. There are dynamic allocation and static allocation in representative allocation schemes. Dynamic allocation is more adaptive in exploiting write operation parallelism, while static allocation is better in read operation parallelism. Therefore, it is hard to select the appropriate allocation scheme when the workload is mixed read and write operations. We simulated conditions on a few mixed data patterns and analyzed the results to help the right choice for better performance. As the results, if data arrival interval is long enough prior operations to be finished and continuous read intensive data environment static allocation is more suitable. Dynamic allocation performs the best on write performance and random data patterns.

Keywords: dynamic allocation, NAND flash based SSD, SSD parallelism, static allocation

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Authors: Du Zhiqin

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WeChat is an instant messaging service similar to WhatsApp developed by Tencent, one of China's largest and most used Internet service portals. This paper investigates the penetration of WeChat among university students through a questionnaire, analyzes the effect of brand influence, information quality, interactivity, opinions of opinion-leaders, promotion, personal interests on the attitudes of university students towards WeChat marketing. This paper concludes that the penetration rate of WeChat marketing among university students is generally high enough, WeChat has generally been accepted by students as a way for businesses to market their products, and that all the above-mentioned five factors play a positive role in the process of WeChat marketing. It also proposes that businesses could maximize the effect of WeChat marketing by focusing more resources on the increase of brand influence, and the interactivity of WeChat contents and the use of opinions of opinion-leaders.

Keywords: WeChat, university students, marketing, AISAS

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Authors: Rima Shishakly, Mervyn Misajon

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Education is similar to other businesses and industries. Achieving data integrity is essential in order to attain a significant supporting for all the stakeholders in the educational sector. Efficient data collect, flow, processing, storing and retrieving are vital in order to deliver successful solutions to the different stakeholders. Ministry of Education (MOE) in United Arab Emirates (UAE) has adopted ‘Education 2020’ a series of five-year plans designed to introduce advanced education management information systems. As part of this program, in 2010 MOE implemented Student Information Systems (SIS) to manage and monitor the students’ data and information flow between MOE and international private schools in UAE. This paper is going to discuss data integrity concerns between MOE, and private schools. The paper will clarify the data integrity issues and will indicate the challenges that face private schools in UAE.

Keywords: education management information systems (EMIS), student information system (SIS), United Arab Emirates (UAE), ministry of education (MOE), (KHDA) the knowledge and human development authority, Abu Dhabi educational counsel (ADEC)

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Authors: Uwalomwa Uwuigbe, Olubukola Ranti Uwuigbe, Jinadu Olugbenga, Otekunrin Adegbola

Abstract:

Corporate reporting has evolved over the years resulting from criticisms of the precedent by shareholders, stakeholders and other relevant financial institutions. Integrated reporting has become a globalized corporate reporting style, with its adoption around the world occurring rapidly to bring about an improvement in the quality of corporate reporting. While some countries have swiftly clinched into reporting in an integrated manner, others have not. In addition, there are ample research that has been conducted on the benefits of adopting integrated reporting, however, the same is not true in developing economies like Nigeria. Hence, this study basically examined the factors determining the adoption of integrated reporting in Nigeria. One hundred (100) copies of questionnaire was administered to financial managers of 20 selected listed companies in the Nigeria stock exchange market. The data obtained was analysed using the Spearman Rank Order Correlation via the Statistical Package for Social Science. This study observed that there is a significant relationship between the social pressures of isomorphic changes and integrated reporting adoption in Nigeria. The study recommends the need for an enforcement mechanism to be put in place while considering the adoption of integrated reporting in Nigeria, enforcement mechanisms should put into consideration the investors demand, the level of economic development, and the degree of corporate social responsibility.

Keywords: corporate social responsibility, isomorphic, integrated reporting, Nigeria, sustainability

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Authors: Leonardo C. Pacheco-Londoño, Nataly J. Galan-Freyle, Lisandro Pacheco-Lugo, Antonio Acosta, Elkin Navarro, Gustavo Aroca-Martínez, Karin Rondón-Payares, Samuel P. Hernández-Rivera

Abstract:

In our Life Science Research Center of the University Simon Bolivar (LSRC), one of the focuses is the diagnosis and prognosis of different diseases; we have been implementing the use of gold nanoparticles (Au-NPs) for various biomedical applications. In this case, Au-NPs were used for Surface-Enhanced Raman Spectroscopy (SERS) in different diseases' diagnostics, such as Lupus Nephritis (LN), hypertension (H), preeclampsia (PC), and others. This methodology is proposed for the diagnosis of each disease. First, good signals of the different metabolites by SERS were obtained through a mixture of urine samples and Au-NPs. Second, PLS-DA models based on SERS spectra to discriminate each disease were able to differentiate between sick and healthy patients with different diseases. Finally, the sensibility and specificity for the different models were determined in the order of 0.9. On the other hand, a second methodology was developed using machine learning models from all data of the different diseases, and, as a result, a discriminant spectral map of the diseases was generated. These studies were possible thanks to joint research between two university research centers and two health sector entities, and the patient samples were treated with ethical rigor and their consent.

Keywords: SERS, Raman, PLS-DA, diseases

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Authors: Kamel Belammi, Houria Fatrim

Abstract:

imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

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Authors: Thiago R. Pereira

Abstract:

The philosophical hermeneutics is able to change the Brazilian Judiciary because of the understanding of the characteristics of the human being. It is impossible for humans, to be invested in the function of being a judge, making absolutely neutral decisions, but the philosophical hermeneutics can assist the judge making impartial decisions, based on the federal constitution. The normative legal positivism imagined a neutral judge, a judge able to try without any preconceived ideas, without allowing his/her background to influence him/her. When a judge arbitrates based on legal rules, the problem is smaller, but when there are no clear legal rules, and the judge must try based on principles, the risk of the decision is based on what they believe in. Solipsistically, this issue gains a huge dimension. Today, the Brazilian judiciary is independent, but there must be a greater knowledge of philosophy and the philosophy of law, partially because the bigger problem is the unpredictability of decisions made by the judiciary. Actually, when a lawsuit is filed, the result of this judgment is absolutely unpredictable. It is almost a gamble. There must be the slightest legal certainty and predictability of judicial decisions, so that people, with similar cases, may not receive opposite sentences. The relativism, since classical antiquity, believes in the possibility of multiple answers. Since the Greeks in in the sixth century before Christ, through the Germans in the eighteenth century, and even today, it has been established the constitution as the great law, the Groundnorm, and thus, the relativism of life can be greatly reduced when a hermeneut uses the Constitution as North interpretational, where all interpretation must act as the hermeneutic constitutional filter. For a current philosophy of law, that inside a legal system with a Federal Constitution, there is a single correct answer to a specific case. The challenge is how to find this right answer. The only answer to this question will be that we should use the constitutional principles. But in many cases, a collision between principles will take place, and to resolve this issue, the judge or the hermeneut will choose a solipsism way, using what they personally believe to be the right one. For obvious reasons, that conduct is not safe. Thus, a theory of decision is necessary to seek justice, and the hermeneutic philosophy and the linguistic turn will be necessary for one to find the right answer. In order to help this difficult mission, it will be necessary to use philosophical hermeneutics in order to find the right answer, which is the constitutionally most appropriate response. The constitutionally appropriate response will not always be the answer that individuals agree to, but we must put aside our preferences and defend the answer that the Constitution gives us. Therefore, the hermeneutics applied to Law, in search constitutionally appropriate response, should be the safest way to avoid judicial individual decisions. The aim of this paper is to present the science of law starting from the linguistic turn, the philosophical hermeneutics, moving away from legal positivism. The methodology used in this paper is qualitative, academic and theoretical, philosophical hermeneutics with the mission to conduct research proposing a new way of thinking about the science of law. The research sought to demonstrate the difficulty of the Brazilian courts to depart from the secular influence of legal positivism. Moreover, the research sought to demonstrate the need to think science of law within a contemporary perspective, where the linguistic turn, philosophical hermeneutics, will be the surest way to conduct the science of law in the present century.

Keywords: hermeneutic, right answer, solipsism, Brazilian judiciary

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Authors: Alexander G. Yushchenko

Abstract:

From a multi-science point of view, we analyze threats to security resulting from globalization of international information space and information and communication aggression of Russia. A definition of Ruschism is formulated as an ideology supporting aggressive actions of modern Russia against the Euro-Atlantic community. Stages of the hybrid war Russia is leading against Ukraine are described, including the elements of subversive activity of the special services, the activation of the military phase and the gradual shift of the focus of confrontation to the realm of information and communication technologies. We reveal an emergence of a threat for democratic states resulting from the destabilizing impact of a target state’s mass media and social networks being exploited by Russian secret services under freedom-of-speech disguise. Thus, we underline the vulnerability of cyber- and information security of the network society in regard of hybrid war. We propose to define the latter a synergetic war. Our analysis is supported with a long-term qualitative monitoring of representation of top state officials on popular TV channels and Facebook. From the memetics point of view, we have detected a destructive psycho-information technology used by the Kremlin, a kind of information catastrophe, the essence of which is explained in detail. In the conclusion, a comprehensive plan for information protection of the public consciousness and mentality of Euro-Atlantic citizens from the aggression of the enemy is proposed.

Keywords: cyber and information security, hybrid war, psycho-information technology, synergetic war, Ruschism

Procedia PDF Downloads 134