Search results for: user profiles

Authors: Frazer G. Thompson

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The 1990s in America ended with a mass-school-shooting (at least four killed by gunfire excluding the perpetrator(s)) at Columbine High School in Littleton, Colorado. Post-event, many demanded that government and civilian experts develop a ‘profile’ of the potential school shooter in order to identify and preempt likely future acts of violence. This grounded theory research study seeks to explore the validity of the original hypotheses proposed by the Federal Bureau of Investigation (FBI) in 2000, as it relates to the commonality of disclosure by perpetrators of mass-school-shootings, by evaluating fourteen mass-school-shooting events between 2000 and 2019 at locations around the United States. Methods: The strategy of inquiry seeks to investigate case files, public records, witness accounts, and available psychological profiles of the shooter. The research methodology is inclusive of one-on-one interviews with members of the FBI’s Critical Incident Response Group seeking perspective on commonalities between individuals; specifically, disclosure of intent pre-event. Results: The research determined that school shooters do not ‘unfailingly’ notify others of their plans. However, in nine of the fourteen mass-school-shooting events analyzed, the perpetrator did inform the third party of their intent pre-event in some form of written, oral, or electronic communication. In the remaining five instances, the so-called ‘red-flag’ indicators of the potential for an event to occur were profound, and unto themselves, might be interpreted as notification to others of an imminent deadly threat. Conclusion: Data indicates that conclusions drawn in the FBI’s threat assessment perspective published in 2000 are relevant and current. There is evidence that despite potential ‘red-flag’ indicators which may or may not include a variety of other characteristics, perpetrators of mass-school-shooting events are likely to share their intentions with others through some form of direct or indirect communication. More significantly, implications of this research might suggest that society is often informed of potential danger pre-event but lacks any equitable means by which to disseminate, prevent, intervene, or otherwise act in a meaningful way considering said revelation.

Keywords: columbine, FBI profiling, guns, mass shooting, mental health, school violence

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Authors: Messaoud Eljamai, Sami Hidouri

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Massive multiple-input multiple-output (MIMO) is an emerging technology for new cellular networks such as 5G systems. Its principle is to use many antennas per cell in order to maximize the network's spectral efficiency. Inter-cellular interference remains a fundamental problem. The use of massive MIMO will not derogate from the rule. It improves performances only when the number of antennas is significantly greater than the number of users. This, considerably, limits the networks spectral efficiency. In this paper, a coordinated detector for an uplink massive MIMO system is proposed in order to mitigate the inter-cellular interference. The proposed scheme combines the coordinated multipoint technique with an interference-cancelling algorithm. It requires the serving cell to send their received symbols, after processing, decision and error detection, to the interfered cells via a backhaul link. Each interfered cell is capable of eliminating intercellular interferences by generating and subtracting the user’s contribution from the received signal. The resulting signal is more reliable than the original received signal. This allows the uplink massive MIMO system to improve their performances dramatically. Simulation results show that the proposed detector improves system spectral efficiency compared to classical linear detectors.

Keywords: massive MIMO, COMP, interference canceling algorithm, spectral efficiency

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Authors: Veniamin Boiarkin, Bruno Bogaz Zarpelão, Muttukrishnan Rajarajan

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The manufacturing sector is a vital component of most economies, which leads to a large number of cyberattacks on organisations, whereas disruption in operation may lead to significant economic consequences. Adversaries aim to disrupt the production processes of manufacturing companies, gain financial advantages, and steal intellectual property by getting unauthorised access to sensitive data. Access to sensitive data helps organisations to enhance the production and management processes. However, the majority of the existing data-sharing mechanisms are either susceptible to different cyber attacks or heavy in terms of computation overhead. In this paper, a privacy-preserving data-sharing scheme for smart utilities is proposed. First, a customer’s privacy adjustment mechanism is proposed to make sure that end-users have control over their privacy, which is required by the latest government regulations, such as the General Data Protection Regulation. Secondly, a local differential privacy-based mechanism is proposed to ensure the privacy of the end-users by hiding real data based on the end-user preferences. The proposed scheme may be applied to different industrial control systems, whereas in this study, it is validated for energy utility use cases consisting of smart, intelligent devices. The results show that the proposed scheme may guarantee the required level of privacy with an expected relative error in utility.

Keywords: data-sharing, local differential privacy, manufacturing, privacy-preserving mechanism, smart utility

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Authors: Mohd Khairezan Rahmat

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Since the impetus of becoming a develop country by the year 2020, the Malaysian government have been proactive in strengthening the integration of ICT into the national educational system. Teacher-education programs have the responsibility to prepare the nation future teachers by instilling in them the desire, confidence, and ability to fully utilized the potential of ICT into their instruction process. In an effort to fulfill this responsibility, teacher-education program are beginning to create alternatives means for preparing cutting-edge teachers. One of the alternatives is the student’s learning portal. In line with this mission, this study investigates the Faculty of Education, University Teknologi MARA (UiTM) pre-service teachers’ perception of usefulness, attitude, and ability toward the usage of the university learning portal, known as iLearn. The study also aimed to predict factors that might hinder the pre-service teachers’ decision to used iLearn as their platform in learning. The Structural Equation Modeling (SEM), was employed in analyzed the survey data. The suggested findings informed that pre-service teacher’s successful integration of the iLearn was highly influenced by their perception of usefulness of the system. The findings also suggested that the more familiar the pre-service teacher with the iLearn, the more possibility they will use the system. In light of similar study, the present findings hope to highlight the important to understand the user’s perception toward any proposed technology.

Keywords: e-learning, prediction factors, pre-service teacher, structural equation modeling (SEM)

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Authors: Emma K. Sales

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Durian is the flagship fruit of Mindanao and there is an abundance of several cultivars with many confusing identities/ names. The project was conducted to develop procedure for reliable and rapid detection and sorting of durian planting materials. Moreover, it is also aimed to establish specific genetic or DNA markers for routine testing and authentication of durian cultivars in question. The project developed molecular procedures for routine testing. SSR primers were also screened and identified for their utility in discriminating durian cultivars collected. Results of the study showed the following accomplishments; 1. Twenty (29) SSR primers were selected and identified based on their ability to discriminate durian cultivars, 2. Optimized and established standard procedure for identification and authentication of Durian cultivars 3. Genetic profile of durian is now available at Biotech Unit. Our results demonstrate the relevance of using molecular techniques in evaluating and identifying durian clones. The most polymorphic primers tested in this study could be useful tools for detecting variation even at the early stage of the plant especially for commercial purposes. The process developed combines the efficiency of the microsatellites development process with the optimization of non-radioactive detection process resulting in a user-friendly protocol that can be performed in two (2) weeks and easily incorporated into laboratories about to start microsatellite development projects. This can be of great importance to extend microsatellite analyses to other crop species where minimal genetic information is currently available. With this, the University can now be a service laboratory for routine testing and authentication of durian clones.

Keywords: DNA, SSR analysis, genotype, genetic diversity, cultivars

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Authors: Priscila A. M. Rodrigues

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In Brazil, higher education is usually offered in three parts of the day: in the morning, afternoon and evening. Students have to decide what part of the day they are going to study in the application process. Most of the admitted students who choose to study in the evening also work during the day, because of their financial conditions. Brazilian higher education courses in the evening were initially created to meet the demand for teacher training. These teacher-training courses are socially discredited and considered easily accessible in the country, mostly due to the fact that students who enroll for those courses come from very poor basic education. The research has analyzed the differences between the social profiles and studying conditions of students of teacher education, especially the training intended for would-be elementary education teachers. An investigation has been conducted with these undergraduate students, who were divided into a group of those who study both in the morning and in the afternoon (group 1) and a group of those who study in the evening (group 2). The hypothesis predicted that students in group 1 would perform better than students in group 2. The analysis of training and studying conditions departed from the point of view of students and their teachers. The hypothesis predicted that students in group 1 would perform better than students in group 2. The analysis of training and studying conditions departed from the point of view of students and their teachers. Data was collected from survey, qualitative interviews, field observation and reports from students. Sociological concepts of habitus, cultural capital, trajectories and strategies are essential for this study as well as the literature on quality of higher education. The research revealed that there are differences of studying conditions between group 1 and group 2, precisely when it comes to the university atmosphere, that is to say, academic support resources and enrichment activities which promote educational, cultural and social opportunities, for example conferences, events, scholarships of different types, etc. In order to counteract the effects of their poor educational performance, students who generally come from popular strata require conditions of greater dedication and investment in higher education, which most of them do not have. Despite the considerable difficulties that students in group 2 encounter in their academic experience, the university experience per se brings a gain for the lives of these students, which translates into the expansion of their capital structure – i.e. symbolic, cultural and educational capital – with repercussions on their social trajectory, especially in professional conditions.

Keywords: higher education, higher education students’ performance, quality of higher education, teacher’s education

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Kulsum Maula, Md Kamrul Alam, Md Ibrahim Khalil, Md Nazmul Hasan, Mohammad Omar Faruq

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Background: Percutaneous Tube Thoracostomy (PTT) is an invasive procedure that can save a life now and then in different traumatic and non-traumatic conditions. But still, it is an enigma; how our trainee surgeons are at home in this procedure. Objectives: To evaluate the outcome of the percutaneous tube thoracostomy performed by trainees in both trauma and non-trauma patients. Study design: Prospective, Observational Study. The duration of the study was September 2018 to February 2019. Methods: All patients who need PTT in traumatic and non-traumatic conditions were selected by purposive sampling. Thereafter, they were scrutinized according to eligibility criteria and 96 patients were finalized. A pre-tested, observation-based, peer-reviewed data collection sheet was prepared before the study. Data regarding clinical and surgical outcome profiles were recorded. Data were compiled, edited, and analyzed. Results: Among 96 patients, the highest 32.29% belonged to age group 31-40 years and the lowest 9.37% belonged to the age group ≤20. The mean age of the respondents was 29.19±9.81. We found out of 96 patients, 70(72.91%) were indicated PTT for traumatic conditions and the rest 26(27.08%) were indicated PTT for non-traumatic chest conditions, where 36(37.5%) had simple penumothorax, 21(21.87%) haemothorax, 14(14.58%) massive pleural effusion, 13(13.54%) tension pneumothorax, 10(10.41%) haemopneumothorax, and 2(2.08%) had pyothorax respectively. In 53.12% of patients had right-sided intercostal chest tube (ICT) insertion, whereas 46.87% had left-sided ICT insertion. In our study, 89.55 % of the tube was placed at the normal anatomical position. Besides, 10.41% of tube thoracostomy were performed deviated from anatomical site. Among 96 patients 62.5% patients had length of incision 2-3cm, 35.41% had >3cm and 2.08% had <2cm respectively. Out of 96 patients, 75(78.13%) showed uneventful outcomes, whereas 21(21.87%) had complications, including 11.15%(11) each had wound infection, 4.46%(4) subcutaneous emphysema, 4.28%(3) drain auto expulsion, 2.85%(2) hemorrhage, 1.45%(1) had a non-functioning drain and empyema with ascending infection respectively (p=<0.05). Conclusion: PTT is a life-saving procedure that is most frequently implemented in chest trauma patients in our country. In the majority of cases, the outcome of PTT was uneventful (78.13). Besides this, more than one-third of patients had a length of incision more than 3 cm that needed extra stitches and 10.41% of cases of PTT were placed other than the normal anatomical site. Trainees of Dhaka Medical College Hospitals are doing well in their performance of PTT insertion, but still, some anatomical orientations are necessary to avoid operative and post-operative complications.

Keywords: PTT, trainee, trauma, non-chest trauma patients

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Essam Mansour

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The key purpose of this study is to provide first-hand information about beggars in Egypt, especially from the perspective of their information seeking behaviour including their information needs. The researcher tries to investigate the information-seeking behaviour of Egyptian beggars with regard to their thoughts, perceptions, motivations, attitudes, habits, preferences as well as challenges that may impede their use of information. The research methods used were an adapted form of snowball sampling of a heterogeneous demographic group of participants in the beggary activity in Egypt. This sampling was used to select focus groups to explore a range of relevant issues. Data on the demographic characteristics of the Egyptian beggars showed that they tend to be men, mostly with no formal education, with an average age around 30s, labeled as low-income persons, mostly single and mostly Muslims. A large number of Egyptian beggars were seeking for information to meet their basic needs as well as their daily needs, although some of them were not able to identify their information needs clearly. The information-seeking behaviour profile of a very large number of Egyptian beggars indicated a preference for informal sources of information over formal ones to solve different problems and meet the challenges they face during their beggary activity depending on assistive devices, such as mobile phones. The high degree of illiteracy and the lack of awareness about the basic rights of information as well as information needs were the most important problems Egyptian beggars face during accessing information. The study recommended further research to be conducted about the role of the library in the education of beggars. It also recommended that beggars’ awareness about their information rights should be promoted through educational programs that help them value the role of information in their life.

Keywords: user studies, information-seeking behaviour, information needs, information sources, beggars, Egypt

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Authors: Camille Van Eupen, Dirk Maes, Marc Herremans, Kristijn R. R. Swinnen, Ben Somers, Stijn Luca

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In ecology, species distribution models are commonly implemented to study species-environment relationships. These models increasingly rely on opportunistic citizen science data when high-quality species records collected through standardized recording protocols are unavailable. While these opportunistic data are abundant, uncertainty is usually high, e.g., due to observer effects or a lack of metadata. Data quality filtering is often used to reduce these types of uncertainty in an attempt to increase the value of studies relying on opportunistic data. However, filtering should not be performed blindly. In this study, recommendations are built for data quality filtering of opportunistic species occurrence data that are used as input for species distribution models. Using an extensive database of 5.7 million citizen science records from 255 species in Flanders, the impact on model performance was quantified by applying three data quality filters, and these results were linked to species traits. More specifically, presence records were filtered based on record attributes that provide information on the observation process or post-entry data validation, and changes in the area under the receiver operating characteristic (AUC), sensitivity, and specificity were analyzed using the Maxent algorithm with and without filtering. Controlling for sample size enabled us to study the combined impact of data quality filtering, i.e., the simultaneous impact of an increase in data quality and a decrease in sample size. Further, the variation among species in their response to data quality filtering was explored by clustering species based on four traits often related to data quality: commonness, popularity, difficulty, and body size. Findings show that model performance is affected by i) the quality of the filtered data, ii) the proportional reduction in sample size caused by filtering and the remaining absolute sample size, and iii) a species ‘quality profile’, resulting from a species classification based on the four traits related to data quality. The findings resulted in recommendations on when and how to filter volunteer generated and opportunistically collected data. This study confirms that correctly processed citizen science data can make a valuable contribution to ecological research and species conservation.

Keywords: citizen science, data quality filtering, species distribution models, trait profiles

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Authors: Hana Maresova, Daniel Ecler

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The paper focuses on the use of 3D multi-user virtual environments (MUVE) in language teaching and presents the results of four years of research at the Faculty of Education, Palacký University in Olomouc (Czech Republic). In the form of an experiment, mother tongue language teaching in the 3D virtual worlds Second Life and Kitely (experimental group) and parallel traditional teaching on identical topics representing teacher's interpretation using a textbook (control group) were implemented. The didactic test, which was presented to the experimental and control groups in an identical form before and after the instruction, verified the effect of the instruction in the experimental group by comparing the results obtained by both groups. Within the three components of mother-tongue teaching (vocabulary, literature, style and communication education), the students in the literature group achieved partially better results (statistically significant in the case of items devoted to the area of visualization of the learning topic), while in the case of grammar and style education the respondents of the control group achieved better results. On the basis of the results obtained, we can conclude that the most appropriate use of MUVE can be seen in the teaching of those topics that provide the possibility of dramatization, experiential learning and group involvement and cooperation, on the contrary, with regard to the need to divide students attention between the topic taught and the control of avatar and movement in virtual reality as less suitable for teaching in the area of memorization of the topic or concepts.

Keywords: distance learning, 3D virtual environments, online teaching, language teaching

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Authors: Kathryn Nderitu, Atunga Nyachieo, Ezekiel Mecha

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Introduction: Solanum nigrum , also known as black nightshade, biosynthesizes various phytochemical compounds with various pharmacological activities, including treating cardiovascular diseases and type 2 diabetes, among others. Materials and Methods: To assess the anti-obesity effects of Solanum nigrum using high-fat-fed diet rats, Sprague Dawley male rats (n = 35) of weights 160–180 g were assigned randomly into seven groups comprising n = 5 rats each. Each group was fed for 11 weeks as follows: normal group (normal chow rat feed); high-fat diet control (HFD); HFD and standard drug (Orlistat 30 mg/kg bw); HFD and methanolic extract 150 mg/kgbw; HFD and methanolic extract 300 mg/kgbw; HFD and dichloromethane extract 150 mg/kgbw; HFD and dichloromethane extract 300 mg/kgbw. Body mass index and food intake were monitored per week, and an oral glucose tolerance test was measured in weeks 5 and 10. Lipid profiles, liver function tests, adipose tissue, liver weights, and phytochemical analysis of Solanum nigrum were later carried out. Results: High-fat diet control group rats exhibited a significant increase in body mass index (BMI), while rats administered with leaf extracts of Solanum nigrum showed a reduction in BMI. Both low doses of dichloromethane (150 mg/kgbw) and high doses of methanol extracts (300 mg/kgbw) showed a better reduction in BMI than the other treatment groups. A significant decrease (p <0.05) in low-density lipoprotein-cholesterol, triglycerides, and cholesterol was observed among the rats administered with Solanum nigrum extracts compared to those of HFD control. Moreover, the HFD control group significantly increased liver and adipose tissue weights compared to other treatment groups (p<0.05). Solanum nigrum also decreased glycemic levels and normalized the hepatic enzymes of HFD control. However, food intake among the groups showed no significant difference (p>0.05). Qualitative analysis of Solanum nigrum leaf extracts indicated the presence of various bioactive compounds associated with anti-obesity. Conclusion: These results validate the use of Solanum nigrum in controlling obesity.

Keywords: solanum nigrum, High fat diet, phytocompounds, obesity

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: cold-start learning, expectation propagation, multi-armed bandits, Thompson Sampling, variational inference

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Authors: Elyas Palantei, Dewiani, Farid Armin, Ardiansyah

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High precision of printed microwave sensor utilized for sensing and monitoring the potential breast cancer existed in women breast tissue was optimally computed. The single element of UWB printed sensor that successfully modeled through several numerical optimizations was multiple fabricated and incorporated with woman bra to form the spherical sensors array. One sample of UWB microwave sensor obtained through the numerical computation and optimization was chosen to be fabricated. In overall, the spherical sensors array consists of twelve stair patch structures, and each element was individually measured to characterize its electrical properties, especially the return loss parameter. The comparison of S11 profiles of all UWB sensor elements is discussed. The constructed UWB sensor is well verified using HFSS programming, CST programming, and experimental measurement. Numerically, both HFSS and CST confirmed the potential operation bandwidth of UWB sensor is more or less 4.5 GHz. However, the measured bandwidth provided is about 1.2 GHz due to the technical difficulties existed during the manufacturing step. The configuration of UWB microwave sensing and monitoring system implemented consists of 12 element UWB printed sensors, vector network analyzer (VNA) to perform as the transceiver and signal processing part, the PC Desktop/Laptop acting as the image processing and displaying unit. In practice, all the reflected power collected from whole surface of artificial breast model are grouped into several numbers of pixel color classes positioned on the corresponding row and column (pixel number). The total number of power pixels applied in 2D-imaging process was specified to 100 pixels (or the power distribution pixels dimension 10x10). This was determined by considering the total area of breast phantom of average Asian women breast size and synchronizing with the single UWB sensor physical dimension. The interesting microwave imaging results were plotted and together with some technical problems arisen on developing the breast sensing and monitoring system are examined in the paper.

Keywords: UWB sensor, UWB microwave imaging, spherical array, breast cancer monitoring, 2D-medical imaging

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Authors: Gina Cynthia Raphita Hasibuan

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The housing sector in Indonesia is in critical condition where majority of low-income citizens live in substandard dwellings, and the number housing backlog is increasing every year. The housing problem becomes more urgent when the term 'sustainability' is considered, and sustainable affordable housing is yet to gain its successful implementation. Global urbanization develops fastest in developing countries like Indonesia where informal settlements are rapidly escalating, hence, making sustainable affordable housing strategies very critical in this context. The problem in developing countries like Indonesia lies on the institutional capacity of newly-established local governments having greater power to determine a development policy but apparently still lacking institutional capability and coordination with the central government and collaborative governance are still not established yet. The concept of upgrading informal settlements are seen changed over time and inconsistent. Despite much research on theme such as sustainable housing concept within Indonesian context, there has been a dearth of research examining the role of collaborative governance, as the current approach still shows fragmented approach between the stakeholders and the lack of community participation as the end user, and thus this research attempts to fill the gap on the aforementioned problems. By using case study with multi-methods conducted in Jakarta, this research has an overall aim to critically assess the role of collaborative governance in addressing sustainable affordable housing in Indonesia and to understand informal settlements and interventions in Indonesia rather than imposing a framework from western perspectives.

Keywords: affordable housing, collaborative governance, sustainability, urban planning

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: Sharon Q. Yang, Robert J. Congleton

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Information security is a broad concept that covers any issues and concerns about the proper access and use of information on the Internet, including measures and procedures to protect intellectual property and private data from illegal access and online theft; the act of hacking; and any defensive technologies that contest such cybercrimes. As more research and commercial activities are conducted online, cybercrimes have increased significantly, putting sensitive information at risk. Information security has become critically important for organizations and private citizens alike. Hackers scan for network vulnerabilities on the Internet and steal data whenever they can. Cybercrimes disrupt our daily life, cause financial losses, and instigate fear in the public. Since the start of the pandemic, most data related cybercrimes targets have been either financial or health information from companies and organizations. Libraries also should have a high interest in understanding and adopting information security methods to protect their patron data and copyrighted materials. But according to information security professionals, higher education and cultural organizations, including their libraries, are the least prepared entities for cyberattacks. One recent example is that of Steven’s Institute of Technology in New Jersey in the US, which had its network hacked in 2020, with the hackers demanding a ransom. As a result, the network of the college was down for two months, causing serious financial loss. There are other cases where libraries, colleges, and universities have been targeted for data breaches. In order to build an effective defense, we need to understand the most common types of cybercrimes, including phishing, whaling, social engineering, distributed denial of service (DDoS) attacks, malware and ransomware, and hacker profiles. Our research will focus on each hacking technique and related defense measures; and the social background and reasons/purpose of hacker and hacking. Our research shows that hacking techniques will continue to evolve as new applications, housing information, and data on the Internet continue to be developed. Some cybercrimes can be stopped with effective measures, while others present challenges. It is vital that people understand what they face and the consequences when not prepared.

Keywords: cybercrimes, hacking technologies, higher education, information security, libraries

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Authors: Dongwan Kang, Joohyung Oh, Chaetae Im

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Advancement of communication technologies and smart devices in the recent times is leading to changes into the integrated wired and wireless communication environments. Since early days, businesses had started introducing environments for mobile device application to their operations in order to improve productivity (efficiency) and the closed corporate environment gradually shifted to an open structure. Recently, individual user's interest in working environment using mobile devices has increased and a new corporate working environment under the concept of BYOD is drawing attention. BYOD (bring your own device) is a concept where individuals bring in and use their own devices in business activities. Through BYOD, businesses can anticipate improved productivity (efficiency) and also a reduction in the cost of purchasing devices. However, as a result of security threats caused by frequent loss and theft of personal devices and corporate data leaks due to low security, companies are reluctant about adopting BYOD system. In addition, without considerations to diverse devices and connection environments, there are limitations in detecting abnormal behaviors such as information leaks which use the existing network-based security equipment. This study suggests a method to detect abnormal behaviors according to individual behavioral patterns, rather than the existing signature-based malicious behavior detection and discusses applications of this method in BYOD environment.

Keywords: BYOD, security, anomaly behavior detection, security equipment, communication technologies

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Authors: Janneth Gonzalez, Andrés Pinzon Velasco, Maria Angarita

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Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatorypathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β‐oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA‐mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation of metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.

Keywords: astrocytes, data integration, palmitic acid, computational model, multi-omics

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Authors: Suraj Nair, Aravindkumar Vijayalingam, Alexander Perzylo, Alois Knoll

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In the last decade robotics research and development activities have grown rapidly, especially in the domain of service robotics. Integrating service robots into human occupied spaces such as homes, offices, hospitals, etc. has become increasingly worked upon. The primary motive is to ease daily lives of humans by taking over some of the household/office chores. However, several challenges remain in systematically integrating such systems in human shared work-spaces. In addition to sensing and indoor-navigation challenges, programmability of such systems is a major hurdle due to the fact that the potential user cannot be expected to have knowledge in robotics or similar mechatronic systems. In this paper, we propose a cognitive system for service robotics which allows non-expert users to easily model system behaviour in an underspecified manner through abstract tasks and objects associated with them. The system uses domain knowledge expressed in the form of an ontology along with logical reasoning mechanisms to infer all the missing pieces of information required for executing the tasks. Furthermore, the system is also capable of recovering from failed tasks arising due to on-line disturbances by using the knowledge base and inferring alternate methods to execute the same tasks. The system is demonstrated through a coffee fetching scenario in an office environment using a mobile robot equipped with sensors and software capabilities for autonomous navigation and human-interaction through natural language.

Keywords: cognitive robotics, reasoning, service robotics, task based systems

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Authors: Supriya Patibanda, Xiaopeng Gong, Krishna N. Jonnalagadda, Ralph Abrahams

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Yttria stabilized zirconia (YSZ) is used as a top coat in thermal barrier coatings in high-temperature turbine/jet engine applications. The mechanical behaviour of YSZ depends on the microstructural features like crack density and porosity, which are a result of coating method. However, experimentally ascertaining their individual effect is difficult due to the inherent challenges involved like material synthesis and handling. The current work deals with the development of a phenomenological model to replicate the tensile behavior of air plasma sprayed YSZ obtained from experiments. Initially, uniaxial tensile experiments were performed on freestanding YSZ coatings of ~300 µm thick for different crack densities and porosities. The coatings exhibited a nonlinear behavior and also a huge variation in strength values. With the obtained experimental tensile curve as a base and crack density and porosity as prime variables, a phenomenological model was developed using ABAQUS interface with new user material defined employing VUMAT sub routine. The relation between the tensile stress and the crack density was empirically established. Further, a parametric study was carried out to investigate the effect of the individual features on the non-linearity in these coatings. This work enables to generate new coating designs by varying the key parameters and predicting the mechanical properties with the help of a simulation, thereby minimizing experiments.

Keywords: crack density, finite element method, plasma sprayed coatings, VUMAT

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Authors: Liwen Wang, Yuting Chen, Tong Wu, Shaolei Hu

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In order to enhance the security of critical financial infrastructure, this study carries out a transformation of the architecture of a financial trading terminal to a zero trust architecture (ZTA), constructs an active defense system for cybersecurity, improves the security level of trading services in the Internet environment, enhances the ability to prevent network attacks and unknown risks, and reduces the industry and security risks brought about by cybersecurity risks. This study introduces the SDP technology of ZTA, adapts and applies it to a financial trading terminal to achieve security optimization and fine-grained business grading control. The upgraded architecture of the trading terminal moves security protection forward to the user access layer, replaces VPN to optimize remote access, and significantly improves the security protection capability of Internet transactions. The study achieves 1. deep integration with the access control architecture of the transaction system; 2. no impact on the performance of terminals and gateways, and no perception of application system upgrades; 3. customized checklist and policy configuration; 4. introduction of industry-leading security technology such as single-packet authorization (SPA) and secondary authentication. This study carries out a successful application of ZTA in the field of financial trading and provides transformation ideas for other similar systems while improving the security level of financial transaction services in the Internet environment.

Keywords: zero trust, trading terminal, architecture, network security, cybersecurity

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Alireza Dantism

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Presently, describing the function of a protein sequence is one of the most common problems in biology. Usually, this problem can be facilitated by studying the three-dimensional structure of proteins. In the absence of a protein structure, comparative modeling often provides a useful three-dimensional model of the protein that is dependent on at least one known protein structure. Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) mainly based on its alignment with one or more proteins of known structure (templates). Comparative modeling consists of four main steps 1. Similarity between the target sequence and at least one known template structure 2. Alignment of target sequence and template(s) 3. Build a model based on alignment with the selected template(s). 4. Prediction of model errors 5. Optimization of the built model There are many computer programs and web servers that automate the comparative modeling process. One of the most important advantages of these servers is that it makes comparative modeling available to both experts and non-experts, and they can easily do their own modeling without the need for programming knowledge, but some other experts prefer using programming knowledge and do their modeling manually because by doing this they can maximize the accuracy of their modeling. In this study, a web-based tool has been designed to predict the tertiary structure of proteins using PHP and Python programming languages. This tool is called EasyModel. EasyModel can receive, according to the user's inputs, the desired unknown sequence (which we know as the target) in this study, the protein sequence file (template), etc., which also has a percentage of similarity with the primary sequence, and its third structure Predict the unknown sequence and present the results in the form of graphs and constructed protein files.

Keywords: structural bioinformatics, protein tertiary structure prediction, modeling, comparative modeling, modeller

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Authors: Debasish Das, Mainak Mitra, A.Chaudhuri

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For the purpose of producing moisture vapor permeable waterproof cotton fabric to be used for protective apparel against rain, cotton fabric was coated with the blend of natural rubber and chloroprene rubber containing ammonium acetate as the water-soluble salt, employing a calendar coating technique. Rubber formulations also contained filler, homogenizer, and a typical sulphur curing system. Natural rubber and chloroprene blend in the blend ratio of 30: 70, containing 25 parts of sodium acetate per hundred parts of rubber was coated on the fabric. The coated fabric was vulcanized thereafter at 140oC for 3 h. Coated and vulcanized fabric was subsequently dipped in water for 45 min, followed by drying in air. Such set of treatments produced optimum results. Coated, vulcanized, washed and dried cotton fabric showed optimum developments in the property profiles in respect of waterproofness, breathability as revealed by moisture vapor transmission rate, coating adhesion, tensile properties, abrasion resistance, flex endurance and fire retardancy. Incorporation of highly water-soluble ammonium acetate salt in the coating formulation and their subsequent removal from vulcanized coated layer affected by post washing in consequent to dipping in the water-bath produced holes of only a few microns in the coating matrix of the fabric. Such microporous membrane formed on the cotton fabric allowed only transportation of moisture vapor through them, giving a moisture vapor transmission rate of 3734 g/m2/24h, while acting as a barrier for large liquid water droplet resisting 120cm of the water column in the hydrostatic water-head tester, rendering the coated cotton fabric waterproof. Examination of surface morphology of vulcanized coating by scanning electron microscopy supported the mechanism proposed for development of breathable waterproof layer on cotton fabric by the process employed above. Such process provides an easy and cost-effective route for achieving moisture vapor permeable waterproof cotton.

Keywords: moisture vapour permeability, waterproofness, chloroprene, calendar coating, coating adhesion, fire retardancy

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Authors: Rebecca Oakes

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In the New Zealand public health system, work has been taking place to use electronic systems to convey data from the ‘floor to the board’ that makes patient needs, and therefore nursing work, visible. For nurses, these developments in health information technology puts us in a very new and exciting position of being able to articulate the work of nursing through a language understood at all levels of an organisation, the language of acuity. Nurses increasingly have a considerable stake-hold in patient acuity data. Patient acuity systems, when used well, can assist greatly in demonstrating how much work is required, the type of work, and when it will be required. The New Zealand Safe Staffing Unit is supporting New Zealand nurses to create a culture of shared governance, where nursing data is informing policies, staffing methodologies and forecasting within their organisations. Assisting organisations to understand their acuity data, strengthening user confidence in using electronic patient acuity systems, and ensuring nursing and midwifery workload is accurately reflected is critical to the success of the safe staffing programme. Nurses and midwives have the capacity via an acuity tool to become key informers of organisational planning. Quality patient care, best use of health resources and a quality work environment are essential components of a safe, resilient and well resourced organisation. Nurses are the key informers of this information. In New Zealand a national level approach is paving the way for significant changes to the understanding and use of patient acuity and nursing workload information.

Keywords: nursing workload, patient acuity, safe staffing, New Zealand

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Authors: Arshad Alfoqaha, Seth Bard, Dustin Demetriou

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A new multi-physics acoustic modeling approach using ANSYS Mechanical FEA and FLUENT CFD methods is developed for modeling servers mounted to racks, such as IBM Z and IBM Power Systems, in data centers. This new approach allows users to determine the thermal and acoustic conditions that people are exposed to within the data center. The sound pressure level (SPL) exposure for a human working inside a hot aisle containment system inside the data center is studied. The SPL is analyzed at the noise source, at the human body, on the rack walls, on the containment walls, and on the ceiling and flooring plenum walls. In the acoustic CFD simulation, it is assumed that a four-inch diameter sphere with monopole acoustic radiation, placed in the middle of each rack, provides a single-source representation of all noise sources within the rack. Ffowcs Williams & Hawkings (FWH) acoustic model is employed. The target frequency is 1000 Hz, and the total simulation time for the transient analysis is 1.4 seconds, with a very small time step of 3e-5 seconds and 10 iterations to ensure convergence and accuracy. A User Defined Function (UDF) is developed to accurately simulate the acoustic noise source, and a Dynamic Mesh is applied to ensure acoustic wave propagation. Initial validation of the acoustic CFD simulation using a closed-form solution for the spherical propagation of an acoustic point source is performed.

Keywords: data centers, FLUENT, acoustics, sound pressure level, SPL, hot aisle containment, IBM

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Authors: Renu Saini, Deepak Agrawal

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Background: Patients with head and spinal cord injury frequently have deranged coagulation profiles and require blood products transfusion perioperatively. Thromboelastography (TEG) is a ‘bedside’ global test of coagulation which may have role in deciding the need of transfusion in such patients. Aim: To assess the usefulness of TEG in department of neurosurgery in decreasing transfusion rates and coagulation-related mortality in traumatic head and spinal cord injury. Method and Methodology: A retrospective comparative study was carried out in the department of neurosurgery over a period of 1 year. There are two groups in this study. ‘Control’ group constitutes the patients in whom data was collected over 6 months (1/6/2009-31/12/2009) prior to installation of TEG machine. ‘Test’ group includes patients in whom data was collected over 6months (1/1/2013-30/6/2013) post TEG installation. Total no. of platelet, FFP, and cryoprecipitate transfusions were noted in both groups along with in hospital mortality and length of stay. Result: Both groups were matched in age and sex of patients, number of head and spinal cord injury cases, number of patients with thrombocytopenia and number of patients who underwent operation. Total 178 patients (135 head injury and 43 spinal cord injury patents) were admitted in neurosurgery department during time period June 2009 to December 2009 i.e. prior to TEG installation and after TEG installation a total of 243 patients(197 head injury and 46 spinal cord injury patents) were admitted. After TEG introduction platelet transfusion significantly reduced (p=0.000) compare to control group (67 units to 34 units). Mortality rate was found significantly reduced after installation (77 patients to 57 patients, P=0.000). Length of stay was reduced significantly (Prior installation 1-211days and after installation 1-115days, p=0.02). Conclusion: Bedside TEG can dramatically reduce platelet transfusion components requirement in department of neurosurgery. TEG also lead to a drastic decrease in mortality rate and length of stay in patients with traumatic head and spinal cord injuries. We recommend its use as a standard of care in the patients with traumatic head and spinal cord injuries.

Keywords: blood component transfusion, mortality, neurosurgery ICU, thromboelastography

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

Procedia PDF Downloads 143