Search results for: coding complexity metric mccabe

Authors: Seyed-Ali Sadegh-Zadeh, Kaveh Kavianpour, Hamed Atashbar, Elham Heidari, Saeed Shiry Ghidary, Amir M. Hajiyavand

Abstract:

Evaluation metrics play a critical role in assessing the performance of machine learning models. In this review paper, we provide a comprehensive overview of performance measurement approaches for machine learning models. For each category, we discuss the most widely used metrics, including their mathematical formulations and interpretation. Additionally, we provide a comparative analysis of performance measurement approaches for metric combinations. Our review paper aims to provide researchers and practitioners with a better understanding of performance measurement approaches and to aid in the selection of appropriate evaluation metrics for their specific applications.

Keywords: evaluation metrics, performance measurement, supervised learning, unsupervised learning, reinforcement learning, model robustness and stability, comparative analysis

Procedia PDF Downloads 76

Authors: Gaelle Candel, David Naccache

Abstract:

t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

Procedia PDF Downloads 144

Authors: Bing Xu, Kerry Lee, Jason M. Stephen

Abstract:

Collaborative learning (CL) has been regarded as a way to facilitate students to gain knowledge and improve social skills. In China, lecturers in higher education institutions have commonly adopted CL in their daily practice. However, such a strategy could not be effective when it is designed and applied in an inappropriate way. Previous research hardly focused on how CL was applied in Chinese universities. This present study aims to gain a deep understanding of how Chinese lecturers design and implement CL tasks. The researchers interviewed ten lecturers from different faculties in various universities in China and usedGroup Learning Activity Instructional Design (GLAID) framework to analyse the data. We found that not all lecturers pay enough attention to eight essential components (proposed by GLAID) when they designed CL tasks, especially the components of Structure and Guidance. Meanwhile, only a small part of lecturers made formative assessment to help students improve learning. We also discuss the strengths and limitations and CL design and further provide suggestions to the lecturers who intend to use CL in class. Research Objectives: The aims of the present research are threefold. We intend to 1) gain a deep understanding of how Chinese lecturers design and implement collaborative learning (CL) tasks, 2) find strengths and limitations of CL design in higher education, and 3) give suggestions about how to improve the design and implement. Research Methods: This research adopted qualitative methods. We applied the semi-structured interview method to interview ten Chinese lecturers about how they designed and implemented CL tasks in their courses. There were 9 questions in the interview protocol focusing on eight components of GLAID. Then, underpinning the GLAID framework, we utilized the coding reliability thematic analysis method to analyse the research data. The coding work was done by two PhD students whose research fields are CL, and the Cohen’s Kappa was 0.772 showing the inter-coder reliability was good. Contribution: Though CL has been commonly adopted in China, few studies have paid attention to the details about how lecturers designed and implemented CL tasks in practice. This research addressed such a gap and found not lecturers were aware of how to design CL and felt it difficult to structure the task and guide the students on collaboration, and further ensure student engagement in CL. In summary, this research advocates for teacher training; otherwise, students may not gain the expected learning outcomes.

Keywords: collaborative learning, higher education, task design, GLAID framework

Procedia PDF Downloads 99

Authors: Farahnaz Sadat Golestan Hashemi, Mohd Razi Ismail, Mohd Y. Rafii

Abstract:

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein (Cas) system can facilitate targeted genome editing in organisms. Dual or single guide RNA (gRNA) can program the Cas9 nuclease to cut target DNA in particular areas; thus, introducing concise mutations either via error-prone non-homologous end-joining repairing or via incorporating foreign DNAs by homologous recombination between donor DNA and target area. In spite of high demand of such promising technology, developing a well-organized procedure in order for reliable mining of potential target sites for gRNAs in large genomic data is still challenging. Hence, we aimed to perform high-throughput detection of target sites by specific PAMs for not only common Streptococcus pyogenes (SpCas9) but also for Neisseria meningitides (NmCas9) CRISPR-Cas systems. Previous research confirmed the successful application of such RNA-guided Cas9 orthologs for effective gene targeting and subsequently genome manipulation. However, Cas9 orthologs need their particular PAM sequence for DNA cleavage activity. Activity levels are based on the sequence of the protospacer and specific combinations of favorable PAM bases. Therefore, based on the specific length and sequence of PAM followed by a constant length of the target site for the two orthogonals of Cas9 protein, we created a reliable procedure to explore possible gRNA sequences. To mine CRISPR target sites, four different searching modes of sgRNA binding to target DNA strand were applied. These searching modes are as follows i) coding strand searching, ii) anti-coding strand searching, iii) both strand searching, and iv) paired-gRNA searching. Finally, a complete list of all potential gRNAs along with their locations, strands, and PAMs sequence orientation can be provided for both SpCas9 as well as another potential Cas9 ortholog (NmCas9). The artificial design of potential gRNAs in a genome of interest can accelerate functional genomic studies. Consequently, the application of such novel genome editing tool (CRISPR/Cas technology) will enhance by presenting increased versatility and efficiency.

Keywords: CRISPR/Cas9 genome editing, gRNA mining, SpCas9, NmCas9

Procedia PDF Downloads 263

Authors: Hyun Chul Koag, Hyun-Sik Ahn

Abstract:

In this paper, we propose a design approach for the safety mechanism of an actuator used in a commercial vehicle’s EPS system. As the number of electric/electronic system in a vehicle increases, the importance of the functional safety has been receiving much attention. EPS(Electric Power Steering) systems for commercial vehicles require large power than passenger vehicles, and hence, dual motor can be applied to get more torque. We show how to formulate the development process for the design of hardware and software of an EPS system using dual motors. A lot of safety mechanisms for the processor, sensors, and memory have been suggested, however, those for actuators have not been fully researched. It is shown by metric analyses that the target ASIL(Automotive Safety Integrated Level) is satisfied in the point of view of hardware of EPS controller.

Keywords: safety mechanism, functional safety, commercial vehicles, electric power steering

Procedia PDF Downloads 394

Authors: Kenny Raharjo, Ramon Lawrence

Abstract:

Game designers have the challenging task of building games that engage players to spend their time and money on the game. There are an infinite number of game variations and design choices, and it is hard to systematically determine game design choices that will have positive experiences for players. In this work, we demonstrate how multi-arm bandits can be used to automatically explore game design variations to achieve improved player metrics. The advantage of multi-arm bandits is that they allow for continuous experimentation and variation, intrinsically converge to the best solution, and require no special infrastructure to use beyond allowing minor game variations to be deployed to users for evaluation. A user study confirms that applying multi-arm bandits was successful in determining the preferred game variation with highest play time metrics and can be a useful technique in a game designer's toolkit.

Keywords: game design, multi-arm bandit, design exploration and data mining, player metric optimization and analytics

Procedia PDF Downloads 511

Authors: Lal Hussain, Wajid Aziz, Imtiaz Ahmed Awan, Sharjeel Saeed

Abstract:

Multiscale entropy analysis (MSE) is a useful technique recently developed to quantify the dynamics of physiological signals at different time scales. This study is aimed at investigating the electroencephalogram (EEG) signals to analyze the background activity of alcoholic and control subjects by inspecting various coarse-grained sequences formed at different time scales. EEG recordings of alcoholic and control subjects were taken from the publically available machine learning repository of University of California (UCI) acquired using 64 electrodes. The MSE analysis was performed on the EEG data acquired from all the electrodes of alcoholic and control subjects. Mann-Whitney rank test was used to find significant differences between the groups and result were considered statistically significant for p-values<0.05. The area under receiver operator curve was computed to find the degree separation between the groups. The mean ranks of MSE values at all the times scales for all electrodes were higher control subject as compared to alcoholic subjects. Higher mean ranks represent higher complexity and vice versa. The finding indicated that EEG signals acquired through electrodes C3, C4, F3, F7, F8, O1, O2, P3, T7 showed significant differences between alcoholic and control subjects at time scales 1 to 5. Moreover, all electrodes exhibit significance level at different time scales. Likewise, the highest accuracy and separation was obtained at the central region (C3 and C4), front polar regions (P3, O1, F3, F7, F8 and T8) while other electrodes such asFp1, Fp2, P4 and F4 shows no significant results.

Keywords: electroencephalogram (EEG), multiscale sample entropy (MSE), Mann-Whitney test (MMT), Receiver Operator Curve (ROC), complexity analysis

Procedia PDF Downloads 376

Authors: Dimakatso Machetele, Kowiyou Yessoufou

Abstract:

Whilst several studies indicated that road accident events might be more complex than thought, we have a limited scientific understanding of this complexity in South Africa. The present project proposes and tests a more comprehensive metamodel that integrates multiple causality relationships among variables previously linked to road accidents. This was done by fitting a structural equation model (SEM) to the data collected from various sources. The study also fitted the GARCH Model (Generalized Auto-Regressive Conditional Heteroskedasticity) to predict the future of road accidents in the country. The analysis shows that the number of road accidents has been increasing since 1935. The road fatality rate follows a polynomial shape following the equation: y = -0.0114x²+1.2378x-2.2627 (R²=0.76) with y = death rate and x = year. This trend results in an average death rate of 23.14 deaths per 100,000 people. Furthermore, the analysis shows that the number of crashes could be significantly explained by the total number of vehicles (P < 0.001), number of registered vehicles (P < 0.001), number of unregistered vehicles (P = 0.003) and the population of the country (P < 0.001). As opposed to expectation, the number of driver licenses issued and total distance traveled by vehicles do not correlate significantly with the number of crashes (P > 0.05). Furthermore, the analysis reveals that the number of casualties could be linked significantly to the number of registered vehicles (P < 0.001) and total distance traveled by vehicles (P = 0.03). As for the number of fatal crashes, the analysis reveals that the total number of vehicles (P < 0.001), number of registered (P < 0.001) and unregistered vehicles (P < 0.001), the population of the country (P < 0.001) and the total distance traveled by vehicles (P < 0.001) correlate significantly with the number of fatal crashes. However, the number of casualties and again the number of driver licenses do not seem to determine the number of fatal crashes (P > 0.05). Finally, the number of crashes is predicted to be roughly constant overtime at 617,253 accidents for the next 10 years, with the worse scenario suggesting that this number may reach 1 896 667. The number of casualties was also predicted to be roughly constant at 93 531 overtime, although this number may reach 661 531 in the worst-case scenario. However, although the number of fatal crashes may decrease over time, it is forecasted to reach 11 241 fatal crashes within the next 10 years, with the worse scenario estimated at 19 034 within the same period. Finally, the number of fatalities is also predicted to be roughly constant at 14 739 but may also reach 172 784 in the worse scenario. Overall, the present study reveals the complexity of road accidents and allows us to propose several recommendations aimed to reduce the trend of road accidents, casualties, fatal crashes, and death in South Africa.

Keywords: road accidents, South Africa, statistical modelling, trends

Procedia PDF Downloads 161

Authors: Alicja Reuben, Amira Annabi

Abstract:

In this paper, we explore the relationship between the selection of valuation techniques by investment banks and the banks’ risk perceptions and performance in the context of the theory of performativity. We use inferential statistics to study these relationships by building a unique dataset based on the disclosure of 12 investment banks’ 2012-2015 annual financial statements. Moreover, we create two constructs, namely intensity of use and risk perception. We measure the intensity of use as a frequency metric of how often a particular bank adopts valuation techniques for a particular asset or liability. We measure risk perception based on disclosed ranges of values for unobservable inputs. Our results are twofold: we find a significant negative correlation between (1) intensity of use and investment bank performance and (2) intensity of use and risk perception. These results indicate that a performative process takes place, and the valuation techniques are enacting their environment.

Keywords: language, linguistics, performativity, financial techniques

Procedia PDF Downloads 161

Authors: Junling Li, Fang Meng, Yichun Zhang

Abstract:

Image saliency detection has been long studied, while several challenging problems are still unsolved, such as detecting saliency inaccurately in complex scenes or suppressing salient objects in the image borders. In this paper, we propose a new saliency detection algorithm in order to solving these problems. We represent the image as a graph with superixels as nodes. By considering appearance similarity between the boundary and the background, the proposed method chooses non-saliency boundary nodes as background priors to construct the background probability model. The probability that each node belongs to the model is computed, which measures its similarity with backgrounds. Thus we can calculate saliency by the transformed probability as a metric. We compare our algorithm with ten-state-of-the-art salient detection methods on the public database. Experimental results show that our simple and effective approach can attack those challenging problems that had been baffling in image saliency detection.

Keywords: visual saliency, background probability, boundary knowledge, background priors

Procedia PDF Downloads 430

Authors: Mohammadreza Akbari, Pooya Soleimani Besheli, Reza Khalili, Davood Domiri Danji

Abstract:

In this conference, our aims are accuracy, capabilities and power at solving of the complicated non-linear partial differential. Our purpose is to enhance the ability to solve the mentioned nonlinear differential equations at basic science and engineering field and similar issues with a simple and innovative approach. As we know most of engineering system behavior in practical are nonlinear process (especially basic science and engineering field, etc.) and analytical solving (no numeric) these problems are difficult, complex, and sometimes impossible like (Fluids and Gas wave, these problems can't solve with numeric method, because of no have boundary condition) accordingly in this symposium we are going to exposure an innovative approach which we have named it Akbari-Ganji's Method or AGM in engineering, that can solve sets of coupled nonlinear differential equations (ODE, PDE) with high accuracy and simple solution and so this issue will emerge after comparing the achieved solutions by Numerical method (Runge-Kutta 4th). Eventually, AGM method will be proved that could be created huge evolution for researchers, professors and students in whole over the world, because of AGM coding system, so by using this software we can analytically solve all complicated linear and nonlinear partial differential equations, with help of that there is no difficulty for solving all nonlinear differential equations. Advantages and ability of this method (AGM) as follow: (a) Non-linear Differential equations (ODE, PDE) are directly solvable by this method. (b) In this method (AGM), most of the time, without any dimensionless procedure, we can solve equation(s) by any boundary or initial condition number. (c) AGM method always is convergent in boundary or initial condition. (d) Parameters of exponential, Trigonometric and Logarithmic of the existent in the non-linear differential equation with AGM method no needs Taylor expand which are caused high solve precision. (e) AGM method is very flexible in the coding system, and can solve easily varieties of the non-linear differential equation at high acceptable accuracy. (f) One of the important advantages of this method is analytical solving with high accuracy such as partial differential equation in vibration in solids, waves in water and gas, with minimum initial and boundary condition capable to solve problem. (g) It is very important to present a general and simple approach for solving most problems of the differential equations with high non-linearity in engineering sciences especially at civil engineering, and compare output with numerical method (Runge-Kutta 4th) and Exact solutions.

Keywords: new approach, AGM, sets of coupled nonlinear differential equation, exact solutions, numerical

Procedia PDF Downloads 465

Authors: Jocelyn Solomons, Sheree Bekker, Ryan Groom, Wilbur Kraak

Abstract:

Sports coaching is marked by inherent elements of complexity. Coaches constantly encounter ambiguity, as they are unable to have complete certainty regarding the perspectives and expectations of stakeholders. Moreover, the coaching environment is characterised by its dynamic nature and intricate micro-political dynamics which further add to the complexity that coaches must navigate. This research study offers a unique perspective on the practical manifestation of coaching effectiveness in the South African (SA) context, where the sport is in its early stages of development. With a predominant presence of male coaches training female players and players originating from diverse sporting backgrounds, including a majority of those who commence their rugby careers at the university level, this exploration, along with practical recommendations, becomes essential. It allows for a nuanced understanding of coaching practices within a rugby system that concurrently focuses on development and high performance. By integrating the views of both players and coaches, insights are gained that extend traditional assessments, enabling a comprehensive understanding of coaching effectiveness and its implications in this evolving Women’s Rugby landscape. Through semi-structured interviews, the research delves into their assessments of coaching strategies, methodologies, and outcomes, aiming to understand coaching efficacy and its impact on player development. The findings contribute to a nuanced understanding of coaching effectiveness, paving the way for evidence-based recommendations to enhance coaching development and positively impact the sport's growth and success in SA.

Keywords: women’s rugby, coaching effectiveness, coaching, rugby, coaching education

Procedia PDF Downloads 56

Authors: Giovanni Gonzalez Araujo, Michael Kyrilov, Angelo Kyrilov

Abstract:

STEAMplug is a web-based innovative educational platform which makes teaching easier and learning more effective. It requires no setup, eliminating the barriers to entry, allowing students to focus on their learning throughreal-world development environments. The student-centric tools enable easy collaboration between peers and teachers. Analyzing user interactions with the system enables us to predict student performance and identify at-risk students, allowing early instructor intervention.

Keywords: plagiarism detection, identifying at-Risk Students, education technology, e-learning system, collaborative development, learning and teaching with technology

Procedia PDF Downloads 152

Authors: M. McHugh, D. Lillis

Abstract:

Mobile applications are being used to perform a wide variety of tasks in day-to-day life, ranging from checking email to controlling your home heating. Application developers have recognized the potential to transform a smart device into a medical device, by using a mobile medical application i.e. a mobile phone or a tablet. When initially conceived these mobile medical applications performed basic functions e.g. BMI calculator, accessing reference material etc.; however, increasing complexity offers clinicians and patients a range of functionality. As this complexity and functionality increases, so too does the potential risk associated with using such an application. Examples include any applications that provide the ability to inflate and deflate blood pressure cuffs, as well as applications that use patient-specific parameters and calculate dosage or create a dosage plan for radiation therapy. If an unapproved mobile medical application is marketed by a medical device organization, then they face significant penalties such as receiving an FDA warning letter to cease the prohibited activity, fines and possibility of facing a criminal conviction. Regulatory bodies have finalized guidance intended for mobile application developers to establish if their applications are subject to regulatory scrutiny. However, regulatory controls appear contradictory with the approaches taken by mobile application developers who generally work with short development cycles and very little documentation and as such, there is the potential to stifle further improvements due to these regulations. The research presented as part of this paper details how by adopting development techniques, such as agile software development, mobile medical application developers can meet regulatory requirements whilst still fostering innovation.

Keywords: agile, applications, FDA, medical, mobile, regulations, software engineering, standards

Procedia PDF Downloads 361

Authors: Mayank Raj, V. G. Narendra

Abstract:

Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

Procedia PDF Downloads 159

Authors: Garry Gorman, Nigel McKelvey, James Connolly

Abstract:

This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

Procedia PDF Downloads 88

Authors: Sung Ham, Chanho Song, Jiabin Wu

Abstract:

Employees are commonly incentivized on both quantity and quality performance and much of the extant literature focuses on demonstrating that multitasking incentives lead to tradeoffs. Alternatively, we consider potential solutions to the tradeoff problem from both a theoretical and an experimental perspective. Across two field experiments from a call center, we find that tradeoffs can be mitigated when incentives are jointly enhanced across tasks, where previous research has suggested that incentives be reduced instead of enhanced. In addition, we also propose and test, in a laboratory setting, the implications of revising the metric used to assess quality. Our results indicate that metrics can be adjusted to align quality and quantity more efficiently. Thus, this alignment has the potential to thwart the classic tradeoff problem. Finally, we validate our findings with an economic experiment that verifies that effort is largely consistent with our theoretical predictions.

Keywords: incentives, multitasking, field experiment, experimental economics

Procedia PDF Downloads 159

Authors: Peter Boyes, Sarah Sharples, Paul Tennent, Gary Priestnall, Jeremy Morley

Abstract:

Significant long-term investment projects can involve complex decisions. These are often described as capital projects, and the factors that contribute to their complexity include budgets, motivating reasons for investment, stakeholder involvement, interdependent projects, and the delivery phases required. The complexity of these projects often requires management groups to be established involving stakeholder representatives; these teams are inherently multidisciplinary. This study uses two university campus capital projects as case studies for this type of management group. Due to the interaction of projects with wider campus infrastructure and users, decisions are made at varying spatial granularity throughout the project lifespan. This spatial-related context brings complexity to the group decisions. Sensemaking is the process used to achieve group situational awareness of a complex situation, enabling the team to arrive at a consensus and make a decision. The purpose of this study is to understand the role of people and data in the complex spatial related long-term decision and sensemaking processes. The paper aims to identify and present issues experienced in practical settings of these types of decision. A series of exploratory semi-structured interviews with members of the two projects elicit an understanding of their operation. From two stages of thematic analysis, inductive and deductive, emergent themes are identified around the group structure, the data usage, and the decision making within these groups. When data were made available to the group, there were commonly issues with the perception of veracity and validity of the data presented; this impacted the ability of group to reach consensus and, therefore, for decisions to be made. Similarly, there were different responses to forecasted or modelled data, shaped by the experience and occupation of the individuals within the multidisciplinary management group. This paper provides an understanding of further support required for team sensemaking and decision making in complex capital projects. The paper also discusses the barriers found to effective decision making in this setting and suggests opportunities to develop decision support systems in this team strategic decision-making process. Recommendations are made for further research into the sensemaking and decision-making process of this complex spatial-related setting.

Keywords: decision making, decisions under uncertainty, real decisions, sensemaking, spatial, team decision making

Procedia PDF Downloads 132

Authors: Ahmed Stifi, Sascha Gentes, Fritz Gehbauer

Abstract:

The construction industry is perhaps one of the oldest industry of the world. The ancient monuments like the egyptian pyramids, the temples of Greeks and Romans like Parthenon and Pantheon, the robust bridges, old Roman theatres, the citadels and many more are the best testament to that. The industry also has a symbiotic relationship with other . Some of the heavy engineering industry provide construction machineries, chemical industry develop innovative construction materials, finance sector provides fund solutions for complex construction projects and many more. Construction Industry is not only mammoth but also very complex in nature. Because of the complexity, construction industry is prone to various tribulations which may have the propensity to hamper its growth. The comparitive study of this industry with other depicts that it is associated with a state of tardiness and delay especially when we focus on the managerial aspects and the study of triple constraint (time, cost and scope). While some institutes says the complexity associated with it as a major reason, others like lean construction, refers to the wastes produced across the construction process as the prime reason. This paper introduces corruption as one of the prime factors for such delays.To support this many international reports and studies are available depicting that construction industry is one of the most corrupt sectors worldwide, and the corruption can take place throught the project cycle comprising project selection, planning, design, funding, pre-qualification, tendering, execution, operation and maintenance, and even through the reconstrction phase. It also happens in many forms such as bribe, fraud, extortion, collusion, embezzlement and conflict of interest and the self-sufficient. As a solution to cope the corruption in construction industry, the paper introduces the integrity as a key factor and build a new integrity framework to develop and implement an integrity management system for construction companies and construction projects.

Keywords: corruption, construction industry, integrity, lean construction

Procedia PDF Downloads 378

Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

Abstract:

Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

Procedia PDF Downloads 526

Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

Abstract:

An estimating equation technique is an alternative method of the widely used maximum likelihood methods, which enables us to ease some complexity due to the complex characteristics of time-varying covariates. In the situations, when both the time-varying covariates and left-truncation are considered in the model, the maximum likelihood estimation procedures become much more burdensome and complex. To ease the complexity, in this study, the modified estimating equations those have been given high attention and considerations in many researchers under semiparametric transformation model was proposed. The purpose of this article was to develop the modified estimating equation under flexible and general class of semiparametric transformation models for left-truncated and right censored survival data with time-varying covariates. Besides the commonly applied Cox proportional hazards model, such kind of problems can be also analyzed with a general class of semiparametric transformation models to estimate the effect of treatment given possibly time-varying covariates on the survival time. The consistency and asymptotic properties of the estimators were intuitively derived via the expectation-maximization (EM) algorithm. The characteristics of the estimators in the finite sample performance for the proposed model were illustrated via simulation studies and Stanford heart transplant real data examples. To sum up the study, the bias for covariates has been adjusted by estimating density function for the truncation time variable. Then the effect of possibly time-varying covariates was evaluated in some special semiparametric transformation models.

Keywords: EM algorithm, estimating equation, semiparametric transformation models, time-to-event outcomes, time varying covariate

Procedia PDF Downloads 155

Authors: Mohammadamin Hashemi, Mohammadreza Kashizadeh

Abstract:

Droughts pose a significant threat to sustainable water resource management, agriculture, and socioeconomic sectors, particularly in the field of climate change. This study investigates drought simulation using rainfall-runoff modelling in the Cuneo district, Italy, over the past 60-year period. The study leverages the TUW model, a lumped conceptual rainfall-runoff model with a semi-distributed operation capability. Similar in structure to the widely used Hydrologiska Byråns Vattenbalansavdelning (HBV) model, the TUW model operates on daily timesteps for input and output data specific to each catchment. It incorporates essential routines for snow accumulation and melting, soil moisture storage, and streamflow generation. Multiple catchments' discharge data within the Cuneo district form the basis for thorough model calibration employing the Kling-Gupta Efficiency (KGE) metric. A crucial metric for reliable drought analysis is one that can accurately represent low-flow events during drought periods. This ensures that the model provides a realistic picture of water availability during these critical times. Subsequent validation of monthly discharge simulations thoroughly evaluates overall model performance. Beyond model development, the investigation delves into drought analysis using the robust Standardized Runoff Index (SRI). This index allows for precise characterization of drought occurrences within the study area. A meticulous comparison of observed and simulated discharge data is conducted, with particular focus on low-flow events that characterize droughts. Additionally, the study explores the complex interplay between land characteristics (e.g., soil type, vegetation cover) and climate variables (e.g., precipitation, temperature) that influence the severity and duration of hydrological droughts. The study's findings demonstrate successful calibration of the TUW model across most catchments, achieving commendable model efficiency. Comparative analysis between simulated and observed discharge data reveals significant agreement, especially during critical low-flow periods. This agreement is further supported by the Pareto coefficient, a statistical measure of goodness-of-fit. The drought analysis provides critical insights into the duration, intensity, and severity of drought events within the Cuneo district. This newfound understanding of spatial and temporal drought dynamics offers valuable information for water resource management strategies and drought mitigation efforts. This research deepens our understanding of drought dynamics in the Cuneo region. Future research directions include refining hydrological modelling techniques and exploring future drought projections under various climate change scenarios.

Keywords: hydrologic extremes, hydrological drought, hydrological modelling, machine learning, rainfall-runoff modelling

Procedia PDF Downloads 43

Authors: Alexander N. Pisarchik, Parth Chholak

Abstract:

Intrinsic brain noise was estimated via magneto-encephalograms (MEG) recorded during perception of flickering visual stimuli with frequencies of 6.67 and 8.57 Hz. First, we measured the mean phase difference between the flicker signal and steady-state event-related field (SSERF) in the occipital area where the brain response at the flicker frequencies and their harmonics appeared in the power spectrum. Then, we calculated the probability distribution of the phase fluctuations in the regions of frequency locking and computed its kurtosis. Since kurtosis is a measure of the distribution’s sharpness, we suppose that inverse kurtosis is related to intrinsic brain noise. In our experiments, the kurtosis value varied among subjects from K = 3 to K = 5 for 6.67 Hz and from 2.6 to 4 for 8.57 Hz. The majority of subjects demonstrated leptokurtic kurtosis (K < 3), i.e., the distribution tails approached zero more slowly than Gaussian. In addition, we found a strong correlation between kurtosis and brain complexity measured as the correlation dimension, so that the MEGs of subjects with higher kurtosis exhibited lower complexity. The obtained results are discussed in the framework of nonlinear dynamics and complex network theories. Specifically, in a network of coupled oscillators, phase synchronization is mainly determined by two antagonistic factors, noise, and the coupling strength. While noise worsens phase synchronization, the coupling improves it. If we assume that each neuron and each synapse contribute to brain noise, the larger neuronal network should have stronger noise, and therefore phase synchronization should be worse, that results in smaller kurtosis. The described method for brain noise estimation can be useful for diagnostics of some brain pathologies associated with abnormal brain noise.

Keywords: brain, flickering, magnetoencephalography, MEG, visual perception, perception time

Procedia PDF Downloads 150

Authors: Rosita Kanapeckaite

Abstract:

The modern operational military environment is a combination of factors such as change, uncertainty, complexity and ambiguity. Stiehm (2002) refers to such situations as VUCA situations. VUCA is an acronym commonly used to describe the volatility, uncertainty, complexity and ambiguity of various situations and conditions. Increasingly fast-paced military operations require military personnel to demonstrate readiness and resilience under stressful conditions in order to maintain the optimum cognitive and physical performance necessary to achieve success. Military resilience can be defined as the ability to cope with the negative effects of setbacks and associated stress on military performance and combat effectiveness. In the volatile, uncertain, complex and ambiguous modern operational environment, both current and future operations require and place a higher priority on enhancing and maintaining troop readiness and resilience to win decisively in multidimensional combat. This paper explores the phenomenon of soldiers' psychological resilience, theories of leadership, and commitment to the organisation. The aim of the study is to examine the relationship between soldiers' psychological resilience, leadership style and commitment to the organisation. The study involved 425 professional soldiers, the research method was a questionnaire survey. The instruments used were measures of psychological resilience, leadership styles and commitment to the organisation. Results: transformational leadership style predicts higher psychological resilience, and psychologically resilient professional servicemen are more committed to the organisation. The study confirms the importance of soldiers' psychological resilience for their commitment to the organisation. The paper also discusses practical applications.

Keywords: resilience, commitment, solders, leadership style

Procedia PDF Downloads 74

Authors: A. K. Borah, A. K. Singh

Abstract:

In this paper we deal building blocks of the computer simulation of the multiphase flows. Whole simulation procedure can be viewed as two super procedures; The implementation of VOF method and the solution of Navier Stoke’s Equation. Moreover, a sequential code for a Navier Stoke’s solver has been studied.

Keywords: Bi-conjugate gradient stabilized (Bi-CGSTAB), ILUT function, krylov subspace, multifluid flows preconditioner, simple algorithm

Procedia PDF Downloads 528

Authors: Guang-Ho Cha

Abstract:

This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.

Keywords: context-sensitive search, image search, similarity ranking, similarity search

Procedia PDF Downloads 366

Authors: Naji Ali Albakay, Abdulrahman Alothaim, Isa Barshushi

Abstract:

The most common quadrature amplitude modulator (QAM) applies two Mach-Zehnder Modulators (MZM) and one phase shifter to generate high order modulation format. The bias of MZM changes over time due to temperature, vibration, and aging factors. The change in the biasing causes distortion to the generated QAM signal which leads to deterioration of bit error rate (BER) performance. Therefore, it is critical to be able to lock MZM’s Q point to the required operating point for good performance. We propose a technique for automatic bias control (ABC) of QAM transmitter using BER measurements and gradient descent optimization algorithm. The proposed technique is attractive because it uses the pertinent metric, BER, which compensates for bias drifting independently from other system variations such as laser source output power. The proposed scheme performance and its operating principles are simulated using OptiSystem simulation software for 4-QAM and 16-QAM transmitters.

Keywords: automatic bias control, optical fiber communication, optical modulation, optical devices

Procedia PDF Downloads 190

Authors: O. O. Onafowokan, K. Anderson, M. R. Norton, R. G. Middleton

Abstract:

Revision total hip arthroplasty (THA) is known to be a challenging procedure with potential for poor outcomes. Due to its lack of metaphyseal encroachment, hip resurfacing arthroplasty (HRA) is classified as a bone conserving procedure. Although the literature postulates that this is an advantage at time of revision surgery, there is no evidence to either support or refute this claim. We identified 129 hips that had undergone HRA and 129 controls undergoing first revision THA. We recorded the clinical assessment and survivorship of implants in a multi-surgeon, single centre, retrospective case control series for both arms. These were matched for age and sex. Data collected included demographics, indications for surgery, Oxford Hip Score (OHS), length of surgery, length of hospital stay, blood transfusion, implant complexity and further surgical procedures. Significance was taken as p < 0.05. Mean follow up was 7.5 years (1 to 15). There was a significant 6 point difference in postoperative OHS in favour of the revision resurfacing group (p=0.0001). The revision HRA group recorded 48 minutes less length of surgery (p<0.0001), 2 days less in length of hospital stay (p=0.018), a reduced need for blood transfusion (p=0.0001), a need for less complexity in revision implants (p=0.001) and a reduced probability of further surgery being required (P=0.003). Whilst we acknowledge the limitations of this study our results suggest that, in contrast to THA, the bone conservation element of HRA may make for a less traumatic revision procedure with better functional outcomes. Use of HRA has seen a dramatic decline as a result of concerns regarding metallosis. However, this information remains of relevance when counselling young active patients about their arthroplasty options and may become pertinent in the future if the promise of ceramic hip resurfacing is ever realized.

Keywords: hip resurfacing, metallosis, revision surgery, total hip arthroplasty

Procedia PDF Downloads 89

Authors: Raghava Dhanya, Shashank S.

Abstract:

This paper delves into the intersection of chess and computer science, specifically focusing on the efficient representation of chess game states. We propose two methods: the Static Method and the Dynamic Method, each offering unique advantages in terms of space efficiency and computational complexity. The Static Method aims to represent the game state using a fixedlength encoding, allocating 192 bits to capture the positions of all pieces on the board. This method introduces a protocol for ordering and encoding piece positions, ensuring efficient storage and retrieval. However, it faces challenges in representing pieces no longer in play. In contrast, the Dynamic Method adapts to the evolving game state by dynamically adjusting the encoding length based on the number of pieces in play. By incorporating Alive Bits for each piece kind, this method achieves greater flexibility and space efficiency. Additionally, it includes provisions for encoding additional game state information such as castling rights and en passant squares. Our findings demonstrate that the Dynamic Method offers superior space efficiency compared to traditional Forsyth-Edwards Notation (FEN), particularly as the game progresses and pieces are captured. However, it comes with increased complexity in encoding and decoding processes. In conclusion, this study provides insights into optimizing the representation of chess game states, offering potential applications in chess engines, game databases, and artificial intelligence research. The proposed methods offer a balance between space efficiency and computational overhead, paving the way for further advancements in the field.

Keywords: chess, optimisation, encoding, bit manipulation

Procedia PDF Downloads 50

Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

Abstract:

The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool

Procedia PDF Downloads 440