Search results for: tasks graph

Authors: Min Kyung An

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In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks

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Authors: Akshita Gaba, Ria P. Dey, Sanya Karotiya, Smrijanee Dash, Soni Gupta

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This research paper explores the significance of environmental awareness and community outreach initiatives undertaken by the Speak Up World Foundation; a non-profit organization founded in 2021. The study delves into the historical context of environmental issues, identifies the driving factors contributing to environmental degradation, and outlines tasks undertaken by the foundation to promote environmental consciousness. The paper also highlights the impact of these efforts on the community and emphasizes the need for continued dedication to ensure sustainable coexistence with our environment.

Keywords: environment, social service, organization, degradation, survey

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Authors: Imran Badshah

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Pedestrian service time reflects the performance of the facility, and it’s a key parameter to analyze the capability of facilities provided to serve pedestrians. The level of service of pedestrians (LOS) mainly depends on pedestrian time and safety. The pedestrian time utilized by taking a service is mainly influenced by the number of available services and the time utilized by each pedestrian in receiving a service; that is called a delay time. In this paper, we analyzed the simulated pedestrian service time with different delay times. A simulation is performed in AnyLogic by developing a model that reflects the real scenario of pedestrian services such as ticket machine gates at rail stations, airports, shopping malls, and cinema halls. The simulated pedestrian time is determined for various delay values. The simulated result shows how pedestrian time changes with the delay pattern. The histogram and time plot graph of a model gives the mean, maximum and minimum values of the pedestrian time. This study helps us to check the behavior of pedestrian time at various services such as subway stations, airports, shopping malls, and cinema halls.

Keywords: agent-based simulation, anylogic model, pedestrian behavior, time delay

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Authors: Chin-Yu Lin, Kun-Chi Wang, Shih-Hsu Wang, Wei-Chih Wang

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A construction superintendent needs to know not only the amount of quantities of cost items or materials completed to develop a daily report or calculate the daily progress (earned value) in each day, but also the amount of quantities of materials (e.g., reinforced steel and concrete) to be ordered (or moved into the jobsite) for performing the in-progress or ready-to-start construction activities (e.g., erection of reinforced steel and concrete pouring). These daily construction management tasks require great effort in extracting accurate quantities in a short time (usually must be completed right before getting off work every day). As a result, most superintendents can only provide these quantity data based on either what they see on the site (high inaccuracy) or the extraction of quantities from two-dimension (2D) construction drawings (high time consumption). Hence, the current practice of providing the amount of quantity data completed in each day needs improvement in terms of more accuracy and efficiency. Recently, a three-dimension (3D)-based building information model (BIM) technique has been widely applied to support construction quantity takeoffs (QTO) process. The capability of virtual reality (VR) allows to view a building from the first person's viewpoint. Thus, this study proposes an innovative system by integrating VR (using 'Unity') and BIM (using 'Revit') to extract quantities to support the above daily construction management tasks. The use of VR allows a system user to be present in a virtual building to more objectively assess the construction progress in the office. This VR- and BIM-based system is also facilitated by an integrated database (consisting of the information and data associated with the BIM model, QTO, and costs). In each day, a superintendent can work through a BIM-based virtual building to quickly identify (via a developed VR shooting function) the building components (or objects) that are in-progress or finished in the jobsite. And he then specifies a percentage (e.g., 20%, 50% or 100%) of completion of each identified building object based on his observation on the jobsite. Next, the system will generate the completed quantities that day by multiplying the specified percentage by the full quantities of the cost items (or materials) associated with the identified object. A building construction project located in northern Taiwan is used as a case study to test the benefits (i.e., accuracy and efficiency) of the proposed system in quantity extraction for supporting the development of daily reports and the orders of construction materials.

Keywords: building information model, construction management, quantity takeoffs, virtual reality

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Authors: Sonia Mastrangelo

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The Miller Umwelt Assessment Scale is a useful tool for obtaining information about the developmental capacities of children on the autism spectrum. The assessment, made up of 19 tasks in the areas of: body organization, contact with surroundings, expressive and receptive communication, representation, and social-emotional development, has been used with much success over the past 40 years. While many assessments are difficult to administer to children on the autism spectrum, the simplicity of the MUAS reveals key strengths and challenges for both low and high functioning children on the spectrum. The results guide parents and clinicians in providing a curriculum and/or home program that moves children up the developmental ladder.

Keywords: autism spectrum disorder, assessment, reading intervention, Miller method

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Authors: Sreedevi P. Chakyar, Jolly Andrews, V. P. Joseph

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A compact method for measuring the relative permittivity of a dielectric material at different temperatures using a single circular Split Ring Resonator (SRR) metamaterial unit working as a test probe is presented in this paper. The dielectric constant of a material is dependent upon its temperature and the LC resonance of the SRR depends on its dielectric environment. Hence, the temperature of the dielectric material in contact with the resonator influences its resonant frequency. A single SRR placed between transmitting and receiving probes connected to a Vector Network Analyser (VNA) is used as a test probe. The dependence of temperature between 30 ^oC and 60 ^oC on resonant frequency of SRR is analysed. Relative permittivities ‘ε’ of test samples for different temperatures are extracted from a calibration graph drawn between the relative permittivity of samples of known dielectric constant and their corresponding resonant frequencies. This method is found to be an easy and efficient technique for analysing the temperature dependent permittivity of different materials.

Keywords: metamaterials, negative permeability, permittivity measurement techniques, split ring resonators, temperature dependent dielectric constant

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Authors: Ewa Toloczko

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Studies show that developing writing skills throughout years of formal foreign language instruction does not necessarily result in rewarding accomplishments among learners, nor an affirmative attitude they build towards written assignments. What causes this apparently wide-spread bias to writing might be a diminished relevance students attach to it, as opposed to the other productive skill — speaking, insufficient resources available for them to succeed, or the ways writing is approached by instructors, that is inapt teaching techniques that discourage rather that inflame learners’ engagement. The assumption underlying this presentation is that psychological and psycholinguistic factors constitute a key dimension of every writing process, and hence should be seriously considered in both material design and lesson planning. The author intends to demonstrate research in which writing tasks were conceived of as attitudinal rather than technical operations, and consequently turned into meaningful and socially-oriented incidents that students could relate to and have an active hand in. The instrument employed to achieve this purpose and to make writing even more interactive was the format of a project, a carefully devised series of tasks, which involved students as human beings, not only language learners. The projects rested upon the premise that the presence of peers and the teacher in class could be taken advantage of in a supportive rather than evaluative mode. In fact, the research showed that collaborative work and constant meaning negotiation reinforced not only bonds between learners, but also the language form and structure of the output. Accordingly, the role of the teacher shifted from the assessor to problem barometer, always ready to accept the slightest improvements in students’ language performance. This way, written verbal communication, which usually aims to merely manifest accuracy and coherent content for assessment, became part of the enterprise meant to emphasise its social aspect — the writer in real-life setting. The samples of projects show the spectrum of possibilities teachers have when exploring the domain of writing within school curriculum. The ideas are easy to modify and adjust to all proficiency levels and ages. Initially, however, they were meant to suit teenage and young adult learners of English as a foreign language in both European and Asian contexts.

Keywords: projects, psycholinguistic/ psychological dimension of writing, writing as a social enterprise, writing skills, written assignments

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Grid computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using Virtual Organization (VO). Security is a critical issue due to the open nature of the wireless channels in the grid computing which requires three fundamental services: authentication, authorization, and encryption. The privacy and anonymity are considered as an important factor while communicating over publicly spanned network like web. To ensure a high level of security we explored an extension of onion routing, which has been used with dynamic token exchange along with protection of privacy and anonymity of individual identity. To improve the performance of encrypting the layers, the elliptic curve cryptography is used. Compared to traditional cryptosystems like RSA (Rivest-Shamir-Adelman), ECC (Elliptic Curve Cryptosystem) offers equivalent security with smaller key sizes which result in faster computations, lower power consumption, as well as memory and bandwidth savings. This paper presents the estimation of the performance improvements of onion routing using ECC as well as the comparison graph between performance level of RSA and ECC.

Keywords: grid computing, privacy, anonymity, onion routing, ECC, RSA

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Authors: F. Tang, S. Boskovich, A. Raheja, Z. Aliyazicioglu, S. Bhandari, N. Tsuchiya

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Calpoly Autonomous Transportation Experience (CATE) is a driverless vehicle that we are developing to provide safe, accessible, and efficient transportation of passengers throughout the Cal Poly Pomona campus for events such as orientation tours. Unlike the other self-driving vehicles that are usually developed to operate with other vehicles and reside only on the road networks, CATE will operate exclusively on walk-paths of the campus (potentially narrow passages) with pedestrians traveling from multiple locations. Safety becomes paramount as CATE operates within the same environment as pedestrians. As driverless vehicles assume greater roles in today’s transportation, this project will contribute to autonomous driving with pedestrian traffic in a highly dynamic environment. The CATE project requires significant interdisciplinary work. Researchers from mechanical engineering, electrical engineering and computer science are working together to attack the problem from different perspectives (hardware, software and system). In this abstract, we describe the software aspects of the project, with a focus on the requirements and the major components. CATE shall provide a GUI interface for the average user to interact with the car and access its available functionalities, such as selecting a destination from any origin on campus. We have developed an interface that provides an aerial view of the campus map, the current car location, routes, and the goal location. Users can interact with CATE through audio or manual inputs. CATE shall plan routes from the origin to the selected destination for the vehicle to travel. We will use an existing aerial map for the campus and convert it to a spatial graph configuration where the vertices represent the landmarks and edges represent paths that the car should follow with some designated behaviors (such as stay on the right side of the lane or follow an edge). Graph search algorithms such as A* will be implemented as the default path planning algorithm. D* Lite will be explored to efficiently recompute the path when there are any changes to the map. CATE shall avoid any static obstacles and walking pedestrians within some safe distance. Unlike traveling along traditional roadways, CATE’s route directly coexists with pedestrians. To ensure the safety of the pedestrians, we will use sensor fusion techniques that combine data from both lidar and stereo vision for obstacle avoidance while also allowing CATE to operate along its intended route. We will also build prediction models for pedestrian traffic patterns. CATE shall improve its location and work under a GPS-denied situation. CATE relies on its GPS to give its current location, which has a precision of a few meters. We have implemented an Unscented Kalman Filter (UKF) that allows the fusion of data from multiple sensors (such as GPS, IMU, odometry) in order to increase the confidence of localization. We also noticed that GPS signals can easily get degraded or blocked on campus due to high-rise buildings or trees. UKF can also help here to generate a better state estimate. In summary, CATE will provide on-campus transportation experience that coexists with dynamic pedestrian traffic. In future work, we will extend it to multi-vehicle scenarios.

Keywords: driverless vehicle, path planning, sensor fusion, state estimate

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Authors: Hamideh Sadat Zekri, Seyed Mojtaba Hosseinalipour, Mohammadreza Hafezi

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Nowadays, an organization cannot usually overcome all tasks singly due to the increasing complexity and vast expanse of projects, increment in uncertainty of activities, fast advances in technology, advent and influence of various factors in decision-making and implication of projects, and competitive atmosphere of different affairs. Thus, firms proceed to outsource the tasks to subcontractors. Nevertheless, large Iranian contracting companies suffer from extra consumed costs and time owing to conflicts between the activities of suppliers and subcontractors. The paucity of coordination in planning and execution, scarcity of coordination among suppliers, subcontractors, and the main contractor during the implementation of construction activities and also the lack of proper management of the aforesaid situation result in the growth of contradictions, number of claims, and legal issues in a project and consequently impose enormous expenses on those companies. Regarding the prosperity of supply chain management in other industries, its importance is increasingly getting appreciated in the field of construction. The ultimate aim of supply chain management is an effective delivery of the best value for customers, which is achievable by encouraging the members to interact and collaborate. In the present research, there was an effort to obtain a set of relevant challenges in the managing of subcontractors by identifying the main contractors and subcontractors and their role in the execution of projects and the supply chain management in the construction industry. Then, some of those challenges were selected in accordance with the views of industry professionals and academic experts. In the next step, a questionnaire was prepared and completed based on the analytic hierarchy process (AHP) and the challenges were prioritized. When it comes to subcontractors, the findings of the research demonstrate that difficulties in timely payments, alterations in approved drawings and the lack of rectification of job after completion by the subcontractor, paucity of a predetermined and legal process for qualifications of subcontractors, neglecting the supply chain processes in material procurement from producers, and delays in delivery of works by a subcontractor are the most significant problems. Finally, some solutions for encountering, eradicating, or reducing of mentioned problems are presented in accordance with previous studies and a survey from specialists.

Keywords: main contractors, subcontractors, supply chain management, construction supply chain, analytic hierarchy process, solution

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Authors: Sanjeev Kumar Sharma, Arnab Mondal, Ranjit Kumar Upadhyay

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Excitable cells often produce different oscillatory activities that help us to understand the transmitting and processing of signals in the neural system. We consider a FitzHugh-Rinzel (FH-R) model and studied the different dynamics of the model by considering the parameter c as the predominant parameter. The model exhibits different types of neuronal responses such as regular spiking, mixed-mode bursting oscillations (MMBOs), elliptic bursting, etc. Based on the bifurcation diagram, we consider the three regimes (MMBOs, elliptic bursting, and quiescent state). An analytical treatment for the occurrence of the supercritical Hopf bifurcation is studied. Further, we extend our study to a network of a hundred neurons by considering the bi-directional synaptic coupling between them. In this article, we investigate the alternation of spiking propagation and bursting phenomena of an uncoupled and coupled FH-R neurons. We explore that the complete graph of heterogenous desynchronized neurons can exhibit different types of bursting oscillations for certain coupling strength. For higher coupling strength, all the neurons in the network show complete synchronization.

Keywords: excitable neuron model, spiking-bursting, stability and bifurcation, synchronization networks

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Authors: J. F. Reinoso-Gordo, F. J. Ariza-López, A. Mozas-Calvache, J. L. García-Balboa, S. Eddargani

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The DEMs quality may depend on several factors such as data source, capture method, processing type used to derive them, or the cell size of the DEM. The two most important capture methods to produce regional-sized DEMs are photogrammetry and LiDAR; DEMs covering entire countries have been obtained with these methods. The quality of these DEMs has traditionally been evaluated by the national cartographic agencies through punctual sampling that focused on its vertical component. For this type of evaluation there are standards such as NMAS and ASPRS Positional Accuracy Standards for Digital Geospatial Data. However, it seems more appropriate to carry out this evaluation by means of a method that takes into account the superficial nature of the DEM and, therefore, its sampling is superficial and not punctual. This work is part of the Research Project "Functional Quality of Digital Elevation Models in Engineering" where it is necessary to control the quality of a DEM whose data source is an experimental LiDAR flight with a density of 14 points per square meter to which we call Point Cloud Product (PCpro). In the present work it is described the capture data on the ground and the postprocessing tasks until getting the point cloud that will be used as reference (PCref) to evaluate the PCpro quality. Each PCref consists of a patch 50x50 m size coming from a registration of 4 different scan stations. The area studied was the Spanish region of Navarra that covers an area of 10,391 km2; 30 patches homogeneously distributed were necessary to sample the entire surface. The patches have been captured using a Leica BLK360 terrestrial laser scanner mounted on a pole that reached heights of up to 7 meters; the position of the scanner was inverted so that the characteristic shadow circle does not exist when the scanner is in direct position. To ensure that the accuracy of the PCref is greater than that of the PCpro, the georeferencing of the PCref has been carried out with real-time GNSS, and its accuracy positioning was better than 4 cm; this accuracy is much better than the altimetric mean square error estimated for the PCpro (<15 cm); The kind of DEM of interest is the corresponding to the bare earth, so that it was necessary to apply a filter to eliminate vegetation and auxiliary elements such as poles, tripods, etc. After the postprocessing tasks the PCref is ready to be compared with the PCpro using different techniques: cloud to cloud or after a resampling process DEM to DEM.

Keywords: data quality, DEM, LiDAR, terrestrial laser scanner, accuracy

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Ebrahim Alrashed, Yousef Rafique

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Mobile devices have drastically changed the way we do things on the move. They are being extremely relied on to perform tasks that are analogous to desktop computer capability. There has been a rapid increase of computational power on these devices; however, battery technology is still the bottleneck of evolution. The primary modern approach day approach to tackle this issue is offloading computation to the cloud, proving to be latency expensive and requiring high network bandwidth. In this paper, we explore efforts to perform barter-based mobile-to-mobile offloading. We present define a protocol and present an architecture to facilitate the development of such a system. We further highlight the deployment and security challenges.

Keywords: computational offloading, power conservation, cloud, sandboxing

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Authors: M. Fernanda Salgado, Alfonso Tierra, Wilbert Aguilar

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The present project consists in applying the informed search algorithm A star (A*) to solve traveler problems, applying it by urban public transportation routes. The digitization of the information allowed to identify 26% of the total of routes that are registered within the Metropolitan District of Quito. For the validation of this information, data were taken in field on the travel times and the difference with respect to the times estimated by the program, resulting in that the difference between them was not greater than 2:20 minutes. We validate A* algorithm with the Dijkstra algorithm, comparing nodes vectors based on the public transport stops, the validation was established through the student t-test hypothesis. Then we verified that the times estimated by the program using the A* algorithm are similar to those registered on field. Furthermore, we review the performance of the algorithm generating iterations in both algorithms. Finally, with these iterations, a hypothesis test was carried out again with student t-test where it was concluded that the iterations of the base algorithm Dijsktra are greater than those generated by the algorithm A*.

Keywords: algorithm A*, graph, mobility, public transport, travel planning, routes

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Sheetal R. Jadhwani, Daisy Sang, Chang-Shyh Peng

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Web applications are an integral part of modem life. They are mostly based upon the HyperText Markup Language (HTML). While HTML meets the basic needs, there are some shortcomings. For example, applications can cease to work once user goes offline, real-time updates may be lagging, and user interface can freeze on computationally intensive tasks. The latest language specification HTML5 attempts to rectify the situation with new tools and protocols. This paper studies the new Web Storage, Geolocation, Web Worker, Canvas, and Web Socket APIs, and presents applications to test their features and efficiencies.

Keywords: HTML5, web worker, canvas, web socket

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Hyo Seon Choi, Ju Sun Kim, DY Kim

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Introduction: This study was aimed to evaluate temporo-spatial patterns during the reach and grasp task in hemiplegic stroke patients and to identify movement pattern according to severity of motor impairment. Method: 29 subacute post-stroke patients were enrolled in this study. The temporo-spatial and kinematic data were obtained during reach and grasp task through 3D motion analysis (VICON). The reach and grasp task was composed of four sub-tasks: reach (T1), transport to mouth (T2), transport back to table (T3) and return (T4). The movement time, joint angle and sum of deviation angles from normative data were compared between affected side and unaffected side. They were also compared between two groups (mild to moderate group: 28~66, severe group: 0~27) divided by upper-Fugl-Meyer Assessment (FMA) scale. Result: In affected side, total time and durations of all four tasks were significantly longer than those in unaffected side (p < 0.001). The affected side demonstrated significant larger shoulder abduction, shoulder internal rotation, wrist flexion, wrist pronation, thoracic external rotation and smaller shoulder flexion during reach and grasp task (p < 0.05). The significant differences between mild to moderate group and severe group were observed in total duration, durations of T1, T2, and T3 in reach and grasp task (p < 0.01). The severe group showed significant larger shoulder internal rotation during T2 (p < 0.05) and wrist flexion during T2, T3 (p < 0.05) than mild to moderate group. In range of motion during each task, shoulder abduction-adduction during T2 and T3, shoulder internal-external rotation during T2, elbow flexion-extension during T1 showed significant difference between two groups (p < 0.05). The severe group had significant larger total deviation angles in shoulder internal-external rotation and wrist extension-flexion during reach and grasp task (p < 0.05). Conclusion: This study suggests that post-stroke hemiplegic patients have an unique temporo-spatial and kinematic patterns during reach and grasp task, and the movement pattern may be related to affected upper limb severity. These results may be useful to interpret the motion of upper extremity in stroke patients.

Keywords: Fugl-Meyer Assessment (FMA), motion analysis, reach and grasp, stroke

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Authors: Ahmad Abu-Akel, Emilie De Montpellier, Sophie Von Bentivegni, Lyn Luechinger, Alessandro Ishii, Christine Mohr

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Convergent and divergent thinking are two main components of creativity that have been viewed as complementary. While divergent thinking refers to the fluency and flexibility of generating new ideas, convergent thinking refers to the ability to systematically apply rules and knowledge to arrive at the optimal solution or idea. These creativity components have been shown to be susceptible to variation in subclinical expressions of autistic and schizotypal traits within the general population. Research, albeit inconclusively, mainly linked positive schizotypal traits with divergent thinking and autistic traits with convergent thinking. However, cumulative evidence suggests that these trait dimensions can co-occur in the same individual more than would be expected by chance and that their concurrent effect can be diametric and even interactive. The current study aimed at investigating the concurrent effect of these trait dimensions on tasks assessing convergent and divergent thinking abilities. We predicted that individuals with high positive schizotypal traits alone would perform particularly well on the divergent thinking task, whilst those with high autistic traits alone would perform particularly well on the convergent thinking task. Crucially, we also predicted that individuals who are high on both autistic and positive schizotypal traits would perform particularly well on both the divergent and convergent thinking tasks. This was investigated in a non-clinical sample of 142 individuals (Males = 45%; Mean age = 21.45, SD = 2.30), sufficient to minimally observe an effect size f² ≥ .10. Divergent thinking was evaluated using the Alternative Uses Task, and convergent thinking with the Anagrams Task. Autistic and schizotypal traits were respectively assessed with the Autism Quotient Questionnaire (AQ) and the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE). Regression analyses revealed that the positive association of autistic traits with convergent thinking scores was qualified with an interaction with positive schizotypal traits. Specifically, positive schizotypal traits were negatively associated with convergent thinking scores when AQ scores were relatively low, but this trend was reversed when AQ scores were high. Conversely, the positive effect of AQ scores on convergent thinking progressively increased with increasing positive schizotypal traits. The results of divergent thinking task are currently being analyzed and will be reported at the conference. The association of elevated autistic and positive schizotypal traits with convergent thinking may represent a unique profile of creative thinkers who are able to simultaneously draw on trait-specific advantages conferred by autistic and positively schizotypal traits such as local and global processing. This suggests that main-effect models can tell an incomplete story regarding the effect of autistic and positive schizotypal traits on creativity-related processes. Future creativity research should consider their interaction and the benefits conferred by their co-presence.

Keywords: autism, schizotypy, convergent thinking, divergent thinking, comorbidity

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Authors: Manojkumar Sabanayagam

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The Riemann hypothesis is an unsolved millennium problem and the search for a solution to the Riemann hypothesis is to study the pattern of prime number distribution. The scope of this paper is to identify the solution for the critical strip and the critical line axis, which has the non-trivial zero solutions using complex plane functions. The Riemann graphical plot is constructed using a linear complex variable function (X+iY) and is applicable only when X>1. But the investigation shows that complex variable behavior has two zones. The first zone is the transformation zone, where the definition of the complex plane should be a non-linear variable which is the critical strip zone in the graph (X=0 to 1). The second zone is the transformed zone (X>1) defined using linear variables conventionally. This paper deals with the Non-linear function in the transformation zone derived using cosine and sinusoidal time lag w.r.t imaginary number ‘i’. The alternate complex variable (Cosθ+i Sinθ) is used to understand the variables in the critical strip zone. It is concluded that the non-trivial zeros present in the Real part 0.5 are because the linear function is not the correct approach in the critical strip. This paper provides the solution to Reimann's hypothesis.

Keywords: Reimann hypothesis, critical strip, complex plane, transformation zone

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Authors: Gajaanuja Megalathan, Banuka Athuraliya

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Crime is one of our society's most intimidating and threatening challenges. With the majority of the population residing in cities, many experts and data provided by local authorities suggest a rapid increase in the number of crimes committed in these cities in recent years. There has been an increasing graph in the crime rates. People living in Sri Lanka have the right to know the exact crime rates and the crime rates in the future of the place they are living in. Due to the current economic crisis, crime rates have spiked. There have been so many thefts and murders recorded within the last 6-10 months. Although there are many sources to find out, there is no solid way of searching and finding out the safety of the place. Due to all these reasons, there is a need for the public to feel safe when they are introduced to new places. Through this research, the author aims to develop a mobile application that will be a solution to this problem. It is mainly targeted at tourists, and people who recently relocated will gain advantage of this application. Moreover, the Arima Model combined with ANN is to be used to predict crime rates. From the past researchers' works, it is evidently clear that they haven’t used the Arima model combined with Artificial Neural Networks to forecast crimes.

Keywords: arima model, ANN, crime prediction, data analysis

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Authors: Garry Gorman, Nigel McKelvey, James Connolly

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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

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Authors: Metehan Mutlu, Meltem Elitas

Abstract:

This study aims to provide an automated method for placing the balloon uterine stents after hysteroscopy adhesiolysis. Currently, there are no automatized tools to place the balloon uterine stent; therefore, surgeons into the endometrial cavity manually fit it. However, it is very hard to pass the balloon stent through the cervical canal, which is roughly 10mm after the surgery. Our method aims to provide an effective and practical way of placing the stent, by automating the procedure through our designed device. Furthermore, our device does the required tasks fast compared to traditional methods, reduces the narcosis time, and decreases the bacterial contamination risks.

Keywords: balloon uterine stent, endometrial cavity, hysteroscopy, motorized-tool

Procedia PDF Downloads 275

Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

Abstract:

Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

Procedia PDF Downloads 256

Authors: John Onyima, Ikechukwu Ezepue

Abstract:

Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

Procedia PDF Downloads 297

Authors: E. Cuevas, M. Feigel'man, L. Ioffe, M. Mezard

Abstract:

The origin of continuous energy spectrum in large disordered interacting quantum systems is one of the key unsolved problems in quantum physics. While small quantum systems with discrete energy levels are noiseless and stay coherent forever in the absence of any coupling to external world, most large-scale quantum systems are able to produce thermal bath, thermal transport and excitation decay. This intrinsic decoherence is manifested by a broadening of energy levels which acquire a finite width. The important question is: What is the driving force and mechanism of transition(s) between two different types of many-body systems - with and without decoherence and thermal transport? Here, we address this question via two complementary approaches applied to the same model of quantum spin-1/2 system with XY-type exchange interaction and random transverse field. Namely, we develop analytical theory for this spin model on a Bethe lattice and implement numerical study of exact level statistics for the same spin model on random graph. This spin model is relevant to the study of pseudogaped superconductivity and S-I transition in some amorphous materials.

Keywords: strongly correlated electrons, quantum phase transitions, superconductor, insulator

Procedia PDF Downloads 578

Authors: Simone B. K. Aires, Cinthia O. de A. Freitas, Luiz E. S. Oliveira

Abstract:

This paper contributes to the CBIR systems applied to trademark retrieval. The proposed model includes aspects from visual perception of the shapes, by means of feature extractor associated to a non-symmetrical perceptual zoning mechanism based on the Principles of Gestalt. Thus, the feature set were performed using Scale Invariant Feature Transform (SIFT). We carried out experiments using four different zonings strategies (Z = 4, 5H, 5V, 7) for matching and retrieval tasks. Our proposal method achieved the normalized recall (Rn) equal to 0.84. Experiments show that the non-symmetrical zoning could be considered as a tool to build more reliable trademark retrieval systems.

Keywords: CBIR, Gestalt, matching, non-symmetrical zoning, SIFT

Procedia PDF Downloads 308

Authors: Q. Raza

Abstract:

Getting old is inevitable, tasks which were once simple are now a daily struggle. This paper is a study of how older users interact with web application based upon a series of experiments. The experiments conducted involved 12 participants and the experiments were split into two parts. The first set gives the users a feel of current social networks and the second set take into considerations from the participants and the results of the two are compared. This paper goes in detail on the psychological aspects such as social exclusion, Metacognition memory and Therapeutic memories and how this relates to users becoming isolated from society, social networking can be the roof on a foundation of successful computer interaction. The purpose of this paper is to carry out a study and to propose new ideas to help users to be able to use social networking sites easily and efficiently.

Keywords: cognitive psychology, special memory, social networking and human computer interaction

Procedia PDF Downloads 441

Authors: Gulfam Haider, sana danish

Abstract:

Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

Procedia PDF Downloads 122