Search results for: regression task

Authors: T. Hirata, M. G. Silva, L. M. Rosa

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The balance ability during task kick in soccer is a determining factor in the execution of functional movements that require a high-performance motor coordination. The current experiment explored it during an instep soccer kick and functional task kicking. Their kicking performance was measured in terms of the sway characteristics using lateral and antero-posterior balance of the center of pressure (COP) for the supporting leg and the kinematic data, the supporting leg’s knee angle. The motion was realized with one-legged stance of five male indoor soccer players and using the trigger device ball controller. The results showed large balance in antero-posterior direction than in lateral direction. However, each player adopts a different way to kick the ball, and the media-lateral displacement of the COP showed no correlation with the balance skill.

Keywords: kicking performance, center of pressure, one-legged stance, balance ability

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Authors: Thomas Seemann, Melanie Seemann

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The work environment has changed considerably due to the COVID pandemic. A growing body of empirical research shows that employees feel increasingly stressed and anxious. They consider themselves more detached from the organization they work for than previously. Organizations need to readjust their leadership practices to cope with this situation and rebuild work motivation and resilience. We propose a leadership tool that focuses on two key dimensions, which we call the "task channel" and the "energy channel." Managing the task channel comprises balancing the challenge [C] of a task and the corresponding skill set [S] of the individual performing the task. Recent research findings shed light on how to balance these two factors and create optimal work conditions in the workplace. Managing the energy channel comprise balancing the workload [WL] of an employee and his/her capacity to work [CW]. This ensures that the mid-term and long-term effectiveness of employees is maintained and energy depletion, fatigue, and burn-out are prevented. Organizations can actively apply strategies to leverage wellsprings and effectively reenergize their workforce. Thinking through and acting upon these factors will provide leaders with the insights they need to maximize their people's performance and, at the same time, establish a more mindful workplace.

Keywords: resilience, motivation, employee engagement, leadership

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Authors: Lavanya Ratnabala, Robinroy Peter, E. Y. A. Charles

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This research paper addresses the challenges of exploration and navigation in unknown environments from an evolutionary swarm robotics perspective. Path formation plays a crucial role in enabling cooperative swarm robots to accomplish these tasks. The paper presents a method called the sub-goal-based path formation, which establishes a path between two different locations by exploiting visually connected sub-goals. Simulation experiments conducted in the Argos simulator demonstrate the successful formation of paths in the majority of trials. Furthermore, the paper tackles the problem of inter-collision (traffic) among a large number of robots engaged in path formation, which negatively impacts the performance of the sub-goal-based method. To mitigate this issue, a task allocation strategy is proposed, leveraging local communication protocols and light signal-based communication. The strategy evaluates the distance between points and determines the required number of robots for the path formation task, reducing unwanted exploration and traffic congestion. The performance of the sub-goal-based path formation and task allocation strategy is evaluated by comparing path length, time, and resource reduction against the A* algorithm. The simulation experiments demonstrate promising results, showcasing the scalability, robustness, and fault tolerance characteristics of the proposed approach.

Keywords: swarm, path formation, task allocation, Argos, exploration, navigation, sub-goal

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Authors: Fernanda Goulart, Rita de Cássia Barbirato

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Task-based language teaching has received attention among researchers as it has been pointed out with the potential to provide more significant opportunities for using the target language and therefore generate successful language acquisition. Nevertheless, in the Brazilian context, few studies have analyzed the potential of tasks in English language acquisition. There is also a need for textbooks to meet the needs of Brazilian students. This work is part of doctoral research in its initial phase. It aims to demonstrate and discuss thematic textbook samples on tasks designed to be applied among high school and undergraduate students in a public technological educational context in São Paulo State, Brazil. It is a qualitative study. The data collection process for course design and textbook development initially included a survey administered to 159 students. Questions related to students’ English background knowledge, main learning interests, and needs. Most students reported difficulties communicating in English and showed a strong interest in a communicative English course. The theme “Cultural diversity” was chosen among other options provided. The textbook was then designed and comprised nine task cycles divided into four sequences. Cycles were composed of pre-tasks, tasks, and post-tasks. The main findings of this first phase of the research revealed that designing a task-based textbook is not easy and requires the necessary steps and lots of effort to meet students’ language needs. Several revisions were needed before the conclusion of the final version of the textbook. The material will be further applied in a three-month English course. In this presentation, we hope to contribute to discussions in research on task-based teaching. Also, we intend to support teachers with their knowledge of tasks and thematic material development in this field.

Keywords: task-based language teaching, language acquisition, English language teaching, task cycles

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Authors: Deyadeen Ali Alshibani

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In the literature, the multi-armed bandit problem as a statistical decision model of an agent trying to optimize his decisions while improving his information at the same time. There are several different algorithms models and their applications on this problem. In this paper, we evaluate the Regret-regression through comparing with Q-learning method. A simulation on determination of optimal treatment regime is presented in detail.

Keywords: optimal, bandit problem, optimization, dynamic programming

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Authors: Jihye Jeon

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This paper analyzes the conceptual framework of three statistical methods, multiple regression, path analysis, and structural equation models. When establishing research model of the statistical modeling of complex social phenomenon, it is important to know the strengths and limitations of three statistical models. This study explored the character, strength, and limitation of each modeling and suggested some strategies for accurate explaining or predicting the causal relationships among variables. Especially, on the studying of depression or mental health, the common mistakes of research modeling were discussed.

Keywords: multiple regression, path analysis, structural equation models, statistical modeling, social and psychological phenomenon

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Authors: Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Lidija Jevrić, Evgenija Djurendić, Jovana Ajduković

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There are several methods for determination of the lipophilicity of biologically active compounds, however chromatography has been shown as a very suitable method for this purpose. Chromatographic (C18-RP-HPLC) analysis of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives was carried out. The obtained retention indices (logk, methanol (90%) / water (10%)) were correlated with calculated physicochemical and lipophilicity descriptors. The QSRR analysis was carried out applying principal component regression (PCR) and partial least squares regression (PLS). The PCR and PLS model were selected on the basis of the highest variance and the lowest root mean square error of cross-validation. The obtained PCR and PLS model successfully correlate the calculated molecular descriptors with logk parameter indicating the significance of the lipophilicity of compounds in chromatographic process. On the basis of the obtained results it can be concluded that the obtained logk parameters of the analyzed androstane derivatives can be considered as their chromatographic lipophilicity. These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1105.

Keywords: androstane derivatives, chromatography, molecular structure, principal component regression, partial least squares regression

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: Shahadut Hossain, Jacek Wesolowski, Zahirul Hoque

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In many epidemiological and environmental studies covariate measurements are subject to the detection limit. In most applications, covariate measurements are usually truncated from below which is known as left-truncation. Because the measuring device, which we use to measure the covariate, fails to detect values falling below the certain threshold. In regression analyses, it causes inflated bias and inaccurate mean squared error (MSE) to the estimators. This paper suggests a response-based regression calibration method to correct the deleterious impact introduced by the covariate detection limit in the estimators of the parameters of simple logistic regression model. Compared to the maximum likelihood method, the proposed method is computationally simpler, and hence easier to implement. It is robust to the violation of distributional assumption about the covariate of interest. In producing correct inference, the performance of the proposed method compared to the other competing methods has been investigated through extensive simulations. A real-life application of the method is also shown using data from a population-based case-control study of non-Hodgkin lymphoma.

Keywords: environmental exposure, detection limit, left truncation, bias, ad-hoc substitution

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

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Authors: Elham Azimzadeh, Hamidollah Hassanlouei, Hadi Nobari, Georgian Badicu, Jorge Pérez-Gómez, Luca Paolo Ardigò

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Children with cerebral palsy (CP) have weak physical abilities and their limitations may have an effect on performing everyday motor activities. One of the most important and common debilitating factors in CP is the malfunction in the upper extremities to perform motor skills and there is strong evidence that task-specific training may lead to improve general upper limb function among this population. However, augmented feedback enhances the acquisition and learning of a motor task. Practice conditions may alter the difficulty, e.g., the reduced frequency of PG could be more challenging for this population to learn a motor task. So, the purpose of this study was to investigate the effect of physical guidance (PG) on learning a tracking task in children with cerebral palsy (CP). Twenty-five independently ambulant children with spastic hemiplegic CP aged 7-15 years were assigned randomly to five groups. After the pre-test, experimental groups participated in an intervention for eight sessions, 12 trials during each session. The 0% PG group received no PG; the 25% PG group received PG for three trials; the 50% PG group received PG for six trials; the 75% PG group received PG for nine trials; and the 100% PG group, received PG for all 12 trials. PG consisted of placing the experimenter's hand around the children's hand, guiding them to stay on track and complete the task. Learning was inferred by acquisition and delayed retention tests. The tests involved two blocks of 12 trials of the tracking task without any PG being performed by all participants. They were asked to make the movement as accurate as possible (i.e., fewer errors) and the number of total touches (errors) in 24 trials was calculated as the scores of the tests. The results showed that the higher frequency of PG led to more accurate performance during the practice phase. However, the group that received 75% PG had significantly better performance compared to the other groups in the retention phase. It is concluded that the optimal frequency of PG played a critical role in learning a tracking task in children with CP and likely this population may benefit from an optimal level of PG to get the appropriate amount of information confirming the challenge point framework (CPF), which state that too much or too little information will retard learning a motor skill. Therefore, an optimum level of PG may help these children to identify appropriate patterns of motor skill using extrinsic information they receive through PG and improve learning by activating the intrinsic feedback mechanisms.

Keywords: cerebral palsy, challenge point framework, motor learning, physical guidance, tracking task

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Authors: Premjit K. Sanjram, Gagan Jakhotiya, Apoorv Goyal, Shanu Shukla

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In psychological research, laboratory experiments often face the trade-off issue between experimental control and mundane realism. With the advent of Immersive Virtual Environment Technology (IVET), this issue seems to be at bay. However there is a growing challenge within the IVET itself to design and develop system or software that captures the psychological phenomenon of everyday lives. One such phenomena that is of growing interest is ‘media-multitasking’ To aid laboratory researches in media-multitasking this paper introduces Query Task Modulator (QTM), a computerized experimentation system to study media-multitasking behavior in a controlled laboratory environment. The system provides a computerized platform in conducting an experiment for experimenters to study media-multitasking in which participants will be involved in a query task. The system has Instant Messaging, E-mail, and Voice Call features. The answers to queries are provided on the left hand side information panel where participants have to search for it and feed the information in the respective communication media blocks as fast as possible. On the whole the system will collect multitasking behavioral data. To analyze performance there is a separate output table that records the reaction times and responses of the participants individually. Information panel and all the media blocks will appear on a single window in order to ensure multi-modality feature in media-multitasking and equal emphasis on all the tasks (thus avoiding prioritization to a particular task). The paper discusses the development of QTM in the light of current techniques of studying media-multitasking.

Keywords: experimentation system, human performance, media-multitasking, query-task

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: Raya Al Khayari, Rasha Al Jassim, Muna Al Balushi, Fatma Al Moqbali, Said El Hajjar

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This study utilizes linear regression analysis to investigate the correlation between user account passwords and the probability of civil ID exposure, offering statistical insights into civil ID security. The study employs multiple linear regression (MLR) analysis to further investigate the elements that influence consumers’ views of civil ID security. This aims to increase awareness and improve preventive measures. The results obtained from the MLR analysis provide a thorough comprehension and can guide specific educational and awareness campaigns aimed at promoting improved security procedures. In summary, the study’s results offer significant insights for improving existing security measures and developing more efficient tactics to reduce risks related to civil ID security in Oman. By identifying key factors that impact consumers’ perceptions, organizations can tailor their strategies to address vulnerabilities effectively. Additionally, the findings can inform policymakers on potential regulatory changes to enhance civil ID security in the country.

Keywords: civil-id disclosure, awareness, linear regression, multiple regression

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Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

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In urban context, urban nodes such as amenity or hazard will certainly affect house price, while classic hedonic analysis will employ distance variables measured from each urban nodes. However, effects from distances to facilities on house prices generally do not represent the true price of the property. Distance variables measured on the same surface are suffering a problem called multicollinearity, which is usually presented as magnitude variance and mean value in regression, errors caused by instability. In this paper, we provided a theoretical framework to identify and gather the data with less bias, and also provided specific sampling method on locating the sample region to avoid the spatial multicollinerity problem in three distance variable’s case.

Keywords: hedonic regression, urban node, distance variables, multicollinerity, collinearity

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Authors: Dilruba Showkat, Cindy Grimm

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As robots become more ubiquitous, it is significant for us to understand how different groups of people respond to possible ways of interacting with the robot. In this study, we focused on gender differences while users were tele-operating a humanoid robot that was physically co-located with them. We investigated three factors during the human-robot interaction (1) information processing strategy (2) self-efficacy and (3) tinkering or exploratory behavior. The experimental results show that the information on how to use the robot was processed comprehensively by the female participants whereas males processed them selectively (p < 0.001). Males were more confident when using the robot than females (p = 0.0002). Males tinkered more with the robot than females (p = 0.0021). We found that tinkering was positively correlated (p = 0.0068) with task success and negatively correlated (p = 0.0032) with task completion time. Tinkering might have resulted in greater task success and lower task completion time for males. Findings from this research can be used for making design decisions for robots and open new research directions. Our results show the importance of accounting for gender differences when developing interfaces for interacting with robots and open new research directions.

Keywords: humanoid robots, tele-operation, gender differences, human-robot interaction

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Authors: Yi-Hsiu Lai

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The present study aimed to elucidate the lexical-semantic processing for Chinese as second language (CSL) learners. Twenty L1 speakers of Chinese and twenty CSL learners in Taiwan participated in a picture naming task and a category fluency task. Based on their Chinese proficiency levels, these CSL learners were further divided into two sub-groups: ten CSL learners of elementary Chinese proficiency level and ten CSL learners of intermediate Chinese proficiency level. Instruments for the naming task were sixty black-and-white pictures: thirty-five object pictures and twenty-five action pictures. Object pictures were divided into two categories: living objects and non-living objects. Action pictures were composed of two categories: action verbs and process verbs. As in the naming task, the category fluency task consisted of two semantic categories – objects (i.e., living and non-living objects) and actions (i.e., action and process verbs). Participants were asked to report as many items within a category as possible in one minute. Oral productions were tape-recorded and transcribed for further analysis. Both error types and error frequency were calculated. Statistical analysis was further conducted to examine these error types and frequency made by CSL learners. Additionally, category effects, pictorial effects and L2 proficiency were discussed. Findings in the present study helped characterize the lexical-semantic process of Chinese naming in CSL learners of different Chinese proficiency levels and made contributions to Chinese vocabulary teaching and learning in the future.

Keywords: lexical-semantic processing, Mandarin Chinese, naming, category effects

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Authors: Mikel Alonso López

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In order to begin the process of studying the task of making consumer decisions, the main decision models must be analyzed. The objective of this task is to see if there is a presence of emotions in those models, and analyze how authors that have created them consider their impact in consumer choices. In this paper, the most important models of consumer behavior are analysed. This review is useful to consider an unproblematic background knowledge in the literature. The order that has been established for this study is chronological.

Keywords: consumer behaviour, emotions, decision making, consumer psychology

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Authors: Jessica Hemberg, Mikaela Miller

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Unreasonable workload and work-related stress can reduce nurse leaders’ job satisfaction and productivity and can increase absence and burnout. Nurse leaders’ workload in public healthcare settings is relatively unresearched. The aim of this study was to investigate nurse leaders’ perceptions of workload and task distribution with relation to leading work tasks in public healthcare. A qualitative explorative design was used. The data material consisted of texts from interviews with nurse leaders in public healthcare (N=8). The method was inspired by content analysis. The COREQ checklist was used. Informed consent was sought from the participants regarding study participation and the storage and handling of data for research purposes. Six main themes were found: Increased and unreasonable workload, Length of work experience as nurse leader affects perception of workload, Number of staff and staff characteristics affect perception of workload, Versatile and flexible task distribution, Working overtime as a way of managing high workload, and Insufficient time for leadership mission. The workload for nurse leaders in a public healthcare setting was perceived to be unreasonable. Common measures for managing high workload included working overtime, delegating work tasks and organizing more staff resources in the form of additional staff. How nurse leaders perceive their workload was linked to both the number of staff and staff characteristics. These should both be considered equally important when determining staff levels and measuring nurse leaders’ workload. Future research should focus on investigating workload and task distribution from nurses’ perspectives.

Keywords: nurse leaders, workload, task distribution, public healthcare, qualitative

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Authors: Hung-Chu Chen, Han Qi, Bauke de Vries

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Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared.

Keywords: energy demand model, geographically weighted regression, normalized difference built-up index, normalized difference vegetation index, spatial statistics

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Authors: Liliana Villavicencio, Yohn Garcia, Pallavi Singh, Luis Fernando Cruz, Wilfrido Moreno

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Psychophysiological adaptive automation is a concept that combines human physiological data and computer algorithms to create personalized interfaces and experiences for users. This approach aims to enhance human learning by adapting to individual needs and preferences and optimizing the interaction between humans and machines. According to neurosciences, the working memory demand during the student learning process is modified when the student is learning a new subject or topic, managing and/or fulfilling a specific task goal. A sudden increase in working memory demand modifies the level of students’ attention, engagement, and cognitive load. The proposed psychophysiological adaptive automation system will adapt the task requirements to optimize cognitive load, the process output variable, by monitoring the student's brain activity. Cognitive load changes according to the student’s previous knowledge, the type of task, the difficulty level of the task, and the overall psychophysiological state of the student. Scaling the measured cognitive load as low, medium, or high; the system will assign a task difficulty level to the next task according to the ratio between the previous-task difficulty level and student stress. For instance, if a student becomes stressed or overwhelmed during a particular task, the system detects this through signal measurements such as brain waves, heart rate variability, or any other psychophysiological variables analyzed to adjust the task difficulty level. The control of engagement and stress are considered internal variables for the hypermedia system which selects between three different types of instructional material. This work assesses the feasibility of a fuzzy controller to track a student's physiological responses and adjust the learning content and pace accordingly. Using an industrial automation approach, the proposed fuzzy logic controller is based on linguistic rules that complement the instrumentation of the system to monitor and control the delivery of instructional material to the students. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the delivery of academic content based on the working memory demand without compromising students’ health. This work has a potential application in the instructional design of virtual reality environments for training and education.

Keywords: fuzzy logic controller, hypermedia control system, personalized education, psychophysiological adaptive automation

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Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

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Authors: Maha ALjohani, James Blustein

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Communicating users' needs, goals and problems help designers and developers overcome challenges faced by end users. Personas are used to represent end users’ needs. In our research, creating personas allowed the following questions to be answered: Who are the potential user groups? What do they want to achieve by using the service? What are the problems that users face? What should the service provide to them? To develop realistic personas, we conducted a focus group discussion with undergraduate and graduate students and also interviewed a university librarian. The personas were created to help evaluating the Institutional Repository that is based on the DSpace system. The profiles helped to communicate users' needs, abilities, tasks, and problems, and the task scenarios used in the heuristic evaluation were based on these personas. Four personas resulted of a focus group discussion with undergraduate and graduate students and from interviewing a university librarian. We then used these personas to create focused task-scenarios for a heuristic evaluation on the system interface to ensure that it met users' needs, goals, problems and desires. In this paper, we present the process that we used to create the personas that led to devise the task scenarios used in the heuristic evaluation as a follow up study of the DSpace university repository.

Keywords: heuristic evaluation, institutional repositories, user experience, human computer interaction, user profiles, personas, task scenarios, heuristics

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Authors: Mehmet Alpaslan Köroğlu, Musa Hakan Arslan, Muslu Kazım Körez

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Basic objective of this study is to create a regression analysis method that can estimate the length of a plastic hinge which is an important design parameter, by making use of the outcomes of (lateral load-lateral displacement hysteretic curves) the experimental studies conducted for the reinforced square concrete columns. For this aim, 170 different square reinforced concrete column tests results have been collected from the existing literature. The parameters which are thought affecting the plastic hinge length such as cross-section properties, features of material used, axial loading level, confinement of the column, longitudinal reinforcement bars in the columns etc. have been obtained from these 170 different square reinforced concrete column tests. In the study, when determining the length of plastic hinge, using the experimental test results, a regression analysis have been separately tested and compared with each other. In addition, the outcome of mentioned methods on determination of plastic hinge length of the reinforced concrete columns has been compared to other methods available in the literature.

Keywords: columns, plastic hinge length, regression analysis, reinforced concrete

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Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

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Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

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Authors: Shahadut Hossain

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Measurement errors in continuous covariates and/or misclassifications in categorical covariates are common in epidemiological studies. Regression analysis ignoring such mismeasurements seriously biases the estimated main and interaction effects of covariates on the outcome of interest. Thus, adjustments for such mismeasurements are necessary. In this research, we propose a Bayesian parametric framework for eliminating deleterious impacts of covariate mismeasurements in logistic regression. The proposed adjustment method is unified and thus can be applied to any generalized linear and non-linear regression models. Furthermore, adjustment for covariate mismeasurements requires validation data usually in the form of either gold standard measurements or replicates of the mismeasured covariates on a subset of the study population. Initial investigation shows that adequacy of such adjustment depends on the sizes of main and validation samples, especially when prevalences of the categorical covariates are low. Thus, we investigate the impact of main and validation sample sizes on the adjusted estimates, and provide a general guideline about these sample sizes based on simulation studies.

Keywords: measurement errors, misclassification, mismeasurement, validation sample, Bayesian adjustment

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Authors: M. Ouassaf, S. Belaid

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A series of quinoline derivatives with antimalarial activity were subjected to two-dimensional quantitative structure-activity relationship (2D-QSAR) studies. Three models were implemented using multiple regression linear MLR, a regression partial least squares (PLS), nonlinear regression (MNLR), to see which descriptors are closely related to the activity biologic. We relied on a principal component analysis (PCA). Based on our results, a comparison of the quality of, MLR, PLS, and MNLR models shows that the MNLR (R = 0.914 and R² = 0.835, RCV= 0.853) models have substantially better predictive capability because the MNLR approach gives better results than MLR (R = 0.835 and R² = 0,752, RCV=0.601)), PLS (R = 0.742 and R² = 0.552, RCV=0.550) The model of MNLR gave statistically significant results and showed good stability to data variation in leave-one-out cross-validation. The obtained results suggested that our proposed model MNLR may be useful to predict the biological activity of derivatives of quinoline.

Keywords: antimalarial, quinoline, QSAR, PCA, MLR , MNLR, MLR

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Authors: M. K. Benhaoua, A. K. Singh, A. E. H. Benyamina, A. Kumar, P. Boulet

Abstract:

In this paper, we propose a new packing strategy to find free resources for run-time mapping of application tasks on NoC-based Heterogeneous MPSoCs. The proposed strategy minimizes the task mapping time in addition to placing the communicating tasks close to each other. To evaluate our approach, a comparative study is carried out. Experiments show that our strategy provides better results when compared to latest dynamic mapping strategies reported in the literature.

Keywords: heterogeneous MPSoCs, NoC, dynamic mapping, routing

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Authors: Z. M. T. Lim, W. Q. Yow

Abstract:

Diversity in individual problem-solving approaches, culture and nationality have been shown to have positive effects on collaborative creative processes in organizational and scholastic settings. For example, diverse graduate and organizational teams consisting of members with both structured and unstructured problem-solving styles were found to have more creative ideas on a collaborative idea generation task than teams that comprised solely of members with either structured or unstructured problem-solving styles. However, being different may not always provide benefits to the collaborative creative process. In particular, speaking different languages may hinder mutual engagement through impaired communication and thus collaboration. Instead, sharing similar languages may have facilitative effects on mutual engagement in collaborative tasks. However, no studies have explored the relations between language diversity and adults’ collaborative creative problem solving. Sixty-four Singaporean English-speaking bilingual undergraduates were paired up into similar or dissimilar language pairs based on the second language they spoke (e.g., for similar language pairs, both participants spoke English-Mandarin; for dissimilar language pairs, one participant spoke English-Mandarin and the other spoke English-Korean). Each participant completed the Ravens Progressive Matrices Task individually. Next, they worked in pairs to complete a collaborative divergent thinking task where they used mind-mapping techniques to brainstorm ideas on a given problem together (e.g., how to keep insects out of the house). Lastly, the pairs worked on a collaborative insight problem-solving task (Triangle of Coins puzzle) where they needed to flip a triangle of ten coins around by moving only three coins. Pairs who had prior knowledge of the Triangle of Coins puzzle were asked to complete an equivalent Matchstick task instead, where they needed to make seven squares by moving only two matchsticks based on a given array of matchsticks. Results showed that, after controlling for intelligence, similar language pairs completed the collaborative insight problem-solving task faster than dissimilar language pairs. Intelligence also moderated these relations. Among adults of lower intelligence, similar language pairs solved the insight problem-solving task faster than dissimilar language pairs. These differences in speed were not found in adults with higher intelligence. No differences were found in the number of ideas generated in the collaborative divergent thinking task between similar language and dissimilar language pairs. In conclusion, sharing similar languages seem to enrich collaborative creative processes. These effects were especially pertinent to pairs with lower intelligence. This provides guidelines for the formation of groups based on shared languages in collaborative creative processes. However, the positive effects of shared languages appear to be limited to the insight problem-solving task and not the divergent thinking task. This could be due to the facilitative effects of other factors of diversity as found in previous literature. Background diversity, for example, may have a larger facilitative effect on the divergent thinking task as compared to the insight problem-solving task due to the varied experiences individuals bring to the task. In conclusion, this study contributes to the understanding of the effects of language diversity in collaborative creative processes and challenges the general positive effects that diversity has on these processes.

Keywords: bilingualism, diversity, creativity, collaboration

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