Search results for: iterative hard thresholding

Authors: Yang Yu, Jian Wang, Jiqiang Liu, Lei Han, Xudong He, Shaohua Lv

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As the increasing network attacks become more and more complex, network situation assessment based on log analysis cannot meet the requirements to ensure network security because of the low quality of logs and alerts. This paper addresses the lack of consideration of security attributes of hosts and attacks in the network. Identity and effectiveness of Distributed Denial of Service (DDoS) are hard to be proved in risk assessment based on alerts and flow matching. This paper proposes a multi-dimension threat situation assessment method based on network security attributes. First, the paper offers an improved Common Vulnerability Scoring System (CVSS) calculation, which includes confident risk, integrity risk, availability risk and a weighted risk. Second, the paper introduces deterioration rate of properties collected by sensors in hosts and network, which aimed at assessing the time and level of DDoS attacks. Third, the paper introduces distribution of asset value in security attributes considering features of attacks and network, which aimed at assessing and show the whole situation. Experiments demonstrate that the approach reflects effectiveness and level of DDoS attacks, and the result can show the primary threat in network and security requirement of network. Through comparison and analysis, the method reflects more in security requirement and security risk situation than traditional methods based on alert and flow analyzing.

Keywords: DDoS evaluation, improved CVSS, network security attribute, threat situation assessment

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Authors: James Killian, Sarah Cox

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The use of stability criteria within geotechnical engineering is the way the results of analyses are conveyed, and sensitivities and risk assessments are performed. Historically, the primary stability criteria for slope design has been the Factor of Safety (FOS) coming from a limit calculation. Increasingly, the value derived from Strength Reduction Factor (SRF) analysis is being used as the criteria for stability analysis. The purpose of this work was to study in detail the relationship between SRF values produced from a numerical modeling technique and the traditional FOS values produced from Limit Equilibrium (LEM) analyses. This study utilized a model of a 3000-foot-high slope with a 45-degree slope angle, assuming a perfectly plastic mohr-coulomb constitutive model with high cohesion and friction angle values typical of a large hard rock mine slope. A number of variables affecting the values of the SRF in a numerical analysis were tested, including zone size, in-situ stress, tensile strength, and dilation angle. This paper demonstrates that in most cases, SRF values are lower than the corresponding LEM FOS values. Modeled zone size has the greatest effect on the estimated SRF value, which can vary as much as 15% to the downside compared to FOS. For consistency when using SRF as a stability criteria, the authors suggest that numerical model zone sizes should not be constructed to be smaller than about 1% of the overall problem slope height and shouldn’t be greater than 2%. Future work could include investigations of the effect of anisotropic strength assumptions or advanced constitutive models.

Keywords: FOS, SRF, LEM, comparison

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Freya Zinovieff

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In the age of the Anthropocene, all ecosystems, no matter how remote, is influenced by the relations between humans and technology. These influences are evidenced by current extinction rates, changes in species diversity, and species adaptation to pollution. Field recording is a tool through which we are able to document the extent to which life forms associated with the place are entangled with human-technology relationships. This paper documents the convergence of interaction between technologies, species, and landscape via an auto-ethnographic account of a field recording taken from a cell phone tower in Bali, Indonesia. In the recording, we hear a confluence of relations where critter and technology meet. The electrical hum of the tower merges with frogs and the amaranthine throb of crickets, in such a way that it is hard to tell where technology begins and the voice of creatures ends. The outcomes of this venture resulted in a framework for evaluating the sensorial relations within field recording. The framework calls for the soundscape to be understood as a multilayered ontology through which there is a convergence of multispecies relationships, or entanglements, across time and geographic location. These entanglements are not necessarily obvious. Sometimes quiet, sometimes elusive, sometimes only audible through the mediated conduit of digital technology. The paper argues that to be aware of these entanglements is to open ourselves to a type of beauty that is firmly rooted in the present paradigm of extinction and loss. By virtue of this understanding, we are bestowed with an opportunity to embrace the grave reality of the current sixth mass extinction and move forwards with what activist Joanna Macy calls the compassionate action.

Keywords: anthropocene, human-technology relationships, multispecies ethnography, field recording

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

Procedia PDF Downloads 342

Authors: Wenxue Xu

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Fluid resistance damper is an important damping element to attenuate vehicle vibration. It converts vibration energy into thermal energy dissipation through oil throttling. It is a typical fluid-solid-heat coupling problem. A complete three-dimensional flow-structure-thermal coupling dynamics simulation model of a gas-filled fluid-resistance damper was established. The flow-condition-based interpolation (FCBI) method and direct coupling calculation method, the unit's FCBI-C fluid numerical analysis method and iterative coupling calculation method are used to achieve the damper dynamic response of the piston rod under sinusoidal excitation; the air chamber inflation pressure, spring compression characteristics, constant flow passage cross-sectional area and oil parameters, etc. The system parameters, excitation frequency, and amplitude and other excitation parameters are analyzed and compared in detail for the effects of differential pressure characteristics, velocity characteristics, flow characteristics and dynamic response of valve opening, floating piston response and piston rod output force characteristics. Experiments were carried out on some simulation analysis conditions. The results show that the node-based FCBI (flow-condition-based interpolation) fluid numerical analysis method and direct coupling calculation method can better guarantee the conservation of flow field calculation, and the calculation step is larger, but the memory is also larger; if the chamber inflation pressure is too low, the damper will become cavitation. The inflation pressure will cause the speed characteristic hysteresis to increase, and the sealing requirements are too strict. The spring compression characteristics have a great influence on the damping characteristics of the damper, and reasonable damping characteristic needs to properly design the spring compression characteristics; the larger the cross-sectional area of the constant flow channel, the smaller the maximum output force, but the more stable when the valve plate is opening.

Keywords: damper, fluid-structure-thermal coupling, heat generation, heat transfer

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Authors: Yao Cheng, Weihua Zhang

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Although the measured vibration signal contains rich information on machine health conditions, the white noise interferences and the discrete harmonic coming from blade, shaft and mash make the fault diagnosis of rolling element bearings difficult. In order to overcome the interferences of useless signals, a new fault diagnosis method combining Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) and Multipoint Optimal Minimum Entropy Deconvolution (MOMED) is proposed for the fault diagnosis of high-speed train bearings. Firstly, the CEEMDAN technique is applied to adaptively decompose the raw vibration signal into a series of finite intrinsic mode functions (IMFs) and a residue. Compared with Ensemble Empirical Mode Decomposition (EEMD), the CEEMDAN can provide an exact reconstruction of the original signal and a better spectral separation of the modes, which improves the accuracy of fault diagnosis. An effective sensitivity index based on the Pearson's correlation coefficients between IMFs and raw signal is adopted to select sensitive IMFs that contain bearing fault information. The composite signal of the sensitive IMFs is applied to further analysis of fault identification. Next, for propose of identifying the fault information precisely, the MOMED is utilized to enhance the periodic impulses in composite signal. As a non-iterative method, the MOMED has better deconvolution performance than the classical deconvolution methods such Minimum Entropy Deconvolution (MED) and Maximum Correlated Kurtosis Deconvolution (MCKD). Third, the envelope spectrum analysis is applied to detect the existence of bearing fault. The simulated bearing fault signals with white noise and discrete harmonic interferences are used to validate the effectiveness of the proposed method. Finally, the superiorities of the proposed method are further demonstrated by high-speed train bearing fault datasets measured from test rig. The analysis results indicate that the proposed method has strong practicability.

Keywords: bearing, complete ensemble empirical mode decomposition with adaptive noise, fault diagnosis, multipoint optimal minimum entropy deconvolution

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Authors: O. Ekrami, P. Claes, S. Van Dongen

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Fluctuating asymmetry (FA) has been studied for many years as an indicator of developmental stability or ‘genetic quality’ based on the assumption that perfect symmetry is ideally the expected outcome for a bilateral organism. Further studies have also investigated the possible link between FA and attractiveness or levels of masculinity or femininity. These hypotheses have been mostly examined using 2D images, and the structure of interest is usually presented using a limited number of landmarks. Such methods have the downside of simplifying and reducing the dimensionality of the structure, which will in return increase the error of the analysis. In an attempt to reach more conclusive and accurate results, in this study we have used high-resolution 3D scans of human faces and have developed an algorithm to measure and localize FA, taking a spatially-dense approach. A symmetric spatially dense anthropometric mask with paired vertices is non-rigidly mapped on target faces using an Iterative Closest Point (ICP) registration algorithm. A set of 19 manually indicated landmarks were used to examine the precision of our mapping step. The protocol’s accuracy in measurement and localizing FA is assessed using simulated faces with known amounts of asymmetry added to them. The results of validation of our approach show that the algorithm is perfectly capable of locating and measuring FA in 3D simulated faces. With the use of such algorithm, the additional captured information on asymmetry can be used to improve the studies of FA as an indicator of fitness or attractiveness. This algorithm can especially be of great benefit in studies of high number of subjects due to its automated and time-efficient nature. Additionally, taking a spatially dense approach provides us with information about the locality of FA, which is impossible to obtain using conventional methods. It also enables us to analyze the asymmetry of a morphological structures in a multivariate manner; This can be achieved by using methods such as Principal Components Analysis (PCA) or Factor Analysis, which can be a step towards understanding the underlying processes of asymmetry. This method can also be used in combination with genome wide association studies to help unravel the genetic bases of FA. To conclude, we introduced an algorithm to study and analyze asymmetry in human faces, with the possibility of extending the application to other morphological structures, in an automated, accurate and multi-variate framework.

Keywords: developmental stability, fluctuating asymmetry, morphometrics, 3D image processing

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Authors: Mohammadreza Akbari, Sara Akbari, Davood Domiri Ganji, Pooya Solimani, Reza Khalili

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In this paper, three complicated nonlinear differential equations(PDE,ODE) in the field of engineering and non-vibration have been analyzed and solved completely by new method that we have named it Akbari-Ganji's Method (AGM) . As regards the previous published papers, investigating this kind of equations is a very hard task to do and the obtained solution is not accurate and reliable. This issue will be emerged after comparing the achieved solutions by Numerical Method. Based on the comparisons which have been made between the gained solutions by AGM and Numerical Method (Runge-Kutta 4th), it is possible to indicate that AGM can be successfully applied for various differential equations particularly for difficult ones. Furthermore, It is necessary to mention that a summary of the excellence of this method in comparison with the other approaches can be considered as follows: It is noteworthy that these results have been indicated that this approach is very effective and easy therefore it can be applied for other kinds of nonlinear equations, And also the reasons of selecting the mentioned method for solving differential equations in a wide variety of fields not only in vibrations but also in different fields of sciences such as fluid mechanics, solid mechanics, chemical engineering, etc. Therefore, a solution with high precision will be acquired. With regard to the afore-mentioned explanations, the process of solving nonlinear equation(s) will be very easy and convenient in comparison with the other methods. And also one of the important position that is explored in this paper is: Trigonometric and exponential terms in the differential equation (the method AGM) , is no need to use Taylor series Expansion to enhance the precision of the result.

Keywords: new method (AGM), complex non-linear partial differential equations, damping ratio, energy lost per cycle

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Authors: Abdool Qaiyum Mohabuth, Syed Munir Ahmad

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Assessment enables students to focus on their learning, assessment. It engages them to work hard and motivates them in devoting time to their studies. Student learning is directly influenced by the type of assessment involved in the programme. Summative Assessment aims at providing measurement of student understanding. In fact, it is argued that summative assessment is used for reporting and reviewing, besides providing an overall judgement of achievement. While summative assessment is a well defined process for learning that takes place in the classroom environment, its application within the practice environment is still being researched. This paper discusses findings from a mixed-method study for exploring the effectiveness of summative assessment in practice learning. A survey questionnaire was designed for exploring the perceptions of mentors and students about summative assessment in practice learning. The questionnaire was administered to the University of Mauritius students and mentors who supervised students for their Work-Based Learning (WBL) practice at the respective placement settings. Some students, having undertaken their WBL practice, were interviewed, for capturing their views and experiences about the application of summative assessment in practice learning. Semi-structured interviews were also conducted with three experienced mentors who have assessed students on practice learning. The findings reveal that though learning in the workplace is entirely different from learning at the University, most students had positive experiences about their summative assessments in practice learning. They felt comfortable and confident to be assessed by their mentors in their placement settings and wished that the effort and time that they devoted to their learning be recognised and valued. Mentors on their side confirmed that the summative assessment is valid and reliable, enabling them to better monitor and coach students to achieve the expected learning outcomes.

Keywords: practice learning, judgement, summative assessment, knowledge, skills, workplace

Procedia PDF Downloads 338

Authors: Ralph Adaimy, Wassim El-Hajj, Ghassen Ben Brahim, Hazem Hajj, Haidar Safa

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Huge amounts of data and personal information are being sent to and retrieved from web applications on daily basis. Every application has its own confidentiality and integrity policies. Violating these policies can have broad negative impact on the involved company’s financial status, while enforcing them is very hard even for the developers with good security background. In this paper, we propose a framework that enforces security-by-construction in web applications. Minimal developer effort is required, in a sense that the developer only needs to annotate database attributes by a security class. The web application code is then converted into an intermediary representation, called Extended Program Dependence Graph (EPDG). Using the EPDG, the provided annotations are propagated to the application code and run against generic security enforcement rules that were carefully designed to detect insecure information flows as early as they occur. As a result, any violation in the data’s confidentiality or integrity policies is reported. As a proof of concept, two PHP web applications, Hotel Reservation and Auction, were used for testing and validation. The proposed system was able to catch all the existing insecure information flows at their source. Moreover and to highlight the simplicity of the suggested approaches vs. existing approaches, two professional web developers assessed the annotation tasks needed in the presented case studies and provided a very positive feedback on the simplicity of the annotation task.

Keywords: web applications security, secure information flow, program dependence graph, database annotation

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Authors: Saeed Ullah Jan

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This study has examined the e-readiness in libraries of public sector universities in Khyber Pakhtunkhwa. Efforts were made to evaluate the availability of human resources, electronic infrastructure, and network services and programs in the public sector university libraries. The population of the study was the twenty-seven public sector university libraries of Khyber Pakhtunkhwa. A quantitative approach was adopted, and a questionnaire-based survey was conducted to collect data from the librarian/in charge of public sector university libraries. The collected data were analyzed using Statistical Package for Social Sciences version 22 (SPSS). The mean score of the knowledge component interpreted magnitudes below three which indicates that the respondents are poorly or moderately satisfied regards knowledge of libraries. The satisfaction level of the respondents about the other components, such as electronic infrastructure, network services and programs, and enhancers of the networked world, was rated as average or below. The study suggested that major aspects of existing public-sector university libraries require significant transformation. For this purpose, the government should provide all the required resources and facilities to meet the population's informational and recreational demands. The Information Communication Technology (ICT) infrastructure of public university libraries needs improvement in terms of the availability of computer equipment, databases, network servers, multimedia projectors, digital cameras, uninterruptible power supply, scanners, and backup devices such as hard discs and Digital Video Disc/Compact Disc.

Keywords: ICT-libraries, e-readiness-libraries, e-readiness-university libraries, e-readiness-Pakistan

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: Maria Sana Amin, Anum Atiq, Haya Fatimah

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Secure attachment in childhood creates a healthy love attitude in the adulthood. Child secure attachment develops a positive relation attitude in their adulthood, similarly, anxiety-avoidant attachment develops negative attitude toward relations. The aim of this paper is twofold: 1) We investigate the relationship between Friendship Attitude and Attachment Styles; and 2) explore the impact of gender on Love Attitudes and Attachment styles. Data was collected by convincing sampling among the students of University of Management and Technology age group 18- 25. The sample consists 60 young adults (Male=36, Female =54). The Love Attitudes Scales subscale Storage was used to measure attitudes towards friendship love and The Experiences in Close Relationships-Revised questionnaire was used to measure Adult Attachment Style. The result of Independent T-Test analysis shows that there was no significant difference in anxiety for female and male conditions; t (58) =-.768, p=.446 and avoidance for female and male conditions; t (58) =1.63, p=.108. Moreover, also there was no significant difference in friendship love for female (M=27.37, SD=6.371) and male (M=26.08, SD=5.709) conditions; t (58) =-.820, p=.416. Pearson correlation analysis shows significantly negative correlation between love attitude-friendship and attachment style- avoidance, (r=-.433, p=.008) among male and love attitude-friendship and attachment style- avoidance (r=-.438, p=.032) among female. There are no gender differences in attachment styles i.e. anxiety, avoidance and their relationship with friendship love attitude. People have avoidant attachment find it hard to fall in love and develop intimacy, and they tend to search for independence.

Keywords: avoidance attachment style, anxiety attachment style, friendship love attitude, gender difference/similarity

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Authors: Michelle Jennett, Jana Dengler, Maytal Perlman

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Background: Cervical spinal cord injury (SCI) is a devastating event that results in upper limb paralysis, loss of independence, and disability. People living with cervical SCI have identified improvement of upper limb function as a top priority. Nerve and tendon transfer surgery has successfully restored upper limb function in cervical SCI but is not universally used or available to all eligible individuals. This exploratory mixed-methods study used an implementation science approach to better understand these factors that influence access to upper limb reconstruction in the Canadian context and design an intervention to increase access to care. Methods: Data from the Canadian Institute for Health Information’s Discharge Abstracts Database (CIHI-DAD) and the National Ambulatory Care Reporting System (NACRS) were used to determine the annual rate of nerve transfer and tendon transfer surgeries performed in cervical SCI in Canada over the last 15 years. Semi-structured interviews informed by the consolidated framework for implementation research (CFIR) were used to explore Ontario healthcare provider knowledge and practices around upper limb reconstruction. An inductive, iterative constant comparative process involving descriptive and interpretive analyses was used to identify themes that emerged from the data. Results: Healthcare providers (n = 10 upper extremity surgeons, n = 10 SCI physiatrists, n = 12 physical and occupational therapists working with individuals with SCI) were interviewed about their knowledge and perceptions of upper limb reconstruction and their current practices and discussions around upper limb reconstruction. Data analysis is currently underway and will be presented. Regional variation in rates of upper limb reconstruction and trends over time are also currently being analyzed. Conclusions: Utilization of nerve and tendon transfer surgery to improve upper limb reconstruction in Canada remains low. There are a complex array of interrelated individual-, provider- and system-level barriers that prevent individuals with cervical SCI from accessing upper limb reconstruction. In order to offer equitable access to care, a multi-modal approach addressing current barriers is required.

Keywords: cervical spinal cord injury, nerve and tendon transfer surgery, spinal cord injury, upper extremity reconstruction

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Authors: Abdul Murad Zainal Abidin, Tuan Suhaimi Salleh, Siti Nor Azila Khalid, Noryati Mustapa

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Quarry dust is a quarry end product from rock crushing processes, which is a concentrated material used as an alternative to fine aggregates for concreting purposes. In quarrying activities, the rocks are crushed into aggregates of varying sizes, from 75mm until less than 4.5 mm, the size of which is categorized as quarry dust. The quarry dust is usually considered as waste and not utilized as a recycled aggregate product. The dumping of the quarry dust at the quarry plant poses the risk of environmental pollution and health hazard. Therefore, the research is an attempt to identify the potential of quarry dust as an alternative building material that would reduce the materials and construction costs, as well as contribute effort in mitigating depletion of natural resources. The objectives are to conduct material characterization and evaluate the properties of fresh and hardened engineering brick with quarry dust mix proportion. The microstructures of quarry dust and the bricks were investigated using scanning electron microscopy (SEM), and the results suggest that the shape and surface texture of quarry dust is a combination of hard and angular formation. The chemical composition of the quarry dust was also evaluated using X-ray fluorescence (XRF) and compared against sand and concrete. The quarry dust was found to have a higher presence of alumina (Al₂O₃), indicating the possibility of an early strength effect for brick. They are utilizing quarry dust waste as replacement material has the potential of conserving non-renewable resources as well as providing a viable alternative to disposal of current quarry waste.

Keywords: building materials, cement replacement, quarry microstructure, quarry product, sustainable materials

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Authors: Shanshan Zhu, Mohammad Nasim

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Financial fraud continues to be a major threat to financial institutions across the world, causing colossal money losses and undermining public trust. Fraud prevention techniques, based on hard rules, have become ineffective due to evolving patterns of fraud in recent times. Against such a background, the present study probes into distinct methodologies that exploit emergent AI-driven techniques to further strengthen fraud detection. We would like to compare the performance of generative adversarial networks and graph neural networks with other popular techniques, like gradient boosting, random forests, and neural networks. To this end, we would recommend integrating all these state-of-the-art models into one robust, flexible, and smart system for real-time anomaly and fraud detection. To overcome the challenge, we designed synthetic data and then conducted pattern recognition and unsupervised and supervised learning analyses on the transaction data to identify which activities were fishy. With the use of actual financial statistics, we compare the performance of our model in accuracy, speed, and adaptability versus conventional models. The results of this study illustrate a strong signal and need to integrate state-of-the-art, AI-driven fraud detection solutions into frameworks that are highly relevant to the financial domain. It alerts one to the great urgency that banks and related financial institutions must rapidly implement these most advanced technologies to continue to have a high level of security.

Keywords: AI-driven fraud detection, financial security, machine learning, anomaly detection, real-time fraud detection

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Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: G. Roshan Deen, J. S. Pedersen

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Temperature-responsive poly(N-isopropyl acrylamide) PNIPAM microgels crosslinked with a new hydrophobic chemical crosslinker was prepared by surfactant-mediated precipitation emulsion polymerization. The temperature-responsive property of the microgel and the influence of the crosslinker on the swelling behaviour was studied systematically by light scattering and small-angle X-ray scattering (SAXS). The radius of gyration (Rg) and the hydrodynamic radius (Rh) of the microgels decreased with increase in temperature due to the volume phase transition from a swollen to a collapsed state. The ratio of Rg/Rh below the transition temperature was lower than that of hard-spheres due to the lower crosslinking density of the microgels. The SAXS data was analysed by a model in which the microgels were modelled as core-shell particles with a graded interface. The model at intermediate temperatures included a central core and a more diffuse outer layer describing pending polymer chains with a low crosslinking density. In the fully swollen state, the microgels were modelled with a single component with a broad graded surface. In the collapsed state they were modelled as homogeneous and relatively compact particles. The polymer volume fraction inside the microgel was also derived based on the model and was found to increase with increase in temperature as a result of collapse of the microgel to compact particles. The polymer volume fraction in the core of the microgel in the collapsed state was about 60% which is higher than that of similar microgels crosslinked with hydrophilic and flexible cross-linkers.

Keywords: microgels, SAXS, hydrophobic crosslinker, light scattering

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Authors: Ahmed E. Hodaib, Mohamed A. Hashem

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In engineering applications, a design has to be as fully perfect as possible in some defined case. The designer has to overcome many challenges in order to reach the optimal solution to a specific problem. This process is called optimization. Generally, there is always a function called “objective function” that is required to be maximized or minimized by choosing input parameters called “degrees of freedom” within an allowed domain called “search space” and computing the values of the objective function for these input values. It becomes more complex when we have more than one objective for our design. As an example for Multi-Objective Optimization Problem (MOP): A structural design that aims to minimize weight and maximize strength. In such case, the Pareto Optimal Frontier (POF) is used, which is a curve plotting two objective functions for the best cases. At this point, a designer should make a decision to choose the point on the curve. Engineers use algorithms or iterative methods for optimization. In this paper, we will discuss the Evolutionary Algorithms (EA) which are widely used with Multi-objective Optimization Problems due to their robustness, simplicity, suitability to be coupled and to be parallelized. Evolutionary algorithms are developed to guarantee the convergence to an optimal solution. An EA uses mechanisms inspired by Darwinian evolution principles. Technically, they belong to the family of trial and error problem solvers and can be considered global optimization methods with a stochastic optimization character. The optimization is initialized by picking random solutions from the search space and then the solution progresses towards the optimal point by using operators such as Selection, Combination, Cross-over and/or Mutation. These operators are applied to the old solutions “parents” so that new sets of design variables called “children” appear. The process is repeated until the optimal solution to the problem is reached. Reliable and robust computational fluid dynamics solvers are nowadays commonly utilized in the design and analyses of various engineering systems, such as aircraft, turbo-machinery, and auto-motives. Coupling of Computational Fluid Dynamics “CFD” and Multi-Objective Evolutionary Algorithms “MOEA” has become substantial in aerospace engineering applications, such as in aerodynamic shape optimization and advanced turbo-machinery design.

Keywords: mathematical optimization, multi-objective evolutionary algorithms "MOEA", computational fluid dynamics "CFD", aerodynamic shape optimization

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Authors: Md. Arifur Rahman, Ummya Salma, Nazrul Islam

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Private sector banking business is one of the leading businesses of Bangladesh as it is profitable and directly attached with the economic development of the country. Training has got very high importance in this sector for increasing the performance of the banks. It has a long term impact on a number of aspects of the bank employees and their performances. It is an investment of the organization that is permanent in nature. Study shows that there are positive relationships between training and the employee commitment, job retention, job satisfaction and company performance. Training is also concerned with promotion, compensation, work-life policies, career development, task and contextual performance of the employees. As such, this paper aims at identifying the impact of training on employee commitment, job retention, job satisfaction and the performance of the private sector banks in Bangladesh. Both primary and secondary data were used to conduct the study. Data were collected from the bank officers who were trained in their banks. Both descriptive and inferential statistics were used to analyze the data. Descriptive statistics were used to describe the present situation of the banks and their employees. Inferential statistics were used to identify the factors and their significance concerned with training. Results show that there is a significant relationship between the performance and the training of the employees. It also shows that the training can motivate employees and encourage them to work hard. However, this study did not find any relationship between the commitment of the employees and the training. This study suggests that for increasing the performance of the banks, training is a must which is to be given deliberately for improving the specific skills of the bank employees.

Keywords: training, promotion, compensation, work-life policies

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Authors: Sarah Kalaouze, Maxine Iannucelli, Kristen Dunfield

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Children’s implicit beliefs regarding intelligence (i.e., intelligence mindsets) influence their motivation, perseverance, and success. Previous research suggests that the way parents perceive failure influences the development of their child’s intelligence mindsets. We invited 151 children-parent dyads (Age= 5–6 years) to complete a series of difficult puzzles over zoom. We assessed parents’ intelligence and failure mindsets using questionnaires and recorded parents’ person/performance-oriented (e.g., “you are smart” or "you were almost able to complete that one) and process-oriented (e.g., “you are trying really hard” or "maybe if you place the bigger pieces first") failure feedback. We were interested in observing the relation between parental mindsets and the type of feedback provided. We found that parents’ intelligence mindsets were not predictive of the feedback they provided children. Failure mindsets, on the other hand, were predictive of failure feedback. Parents who view failure-as-debilitating provided more person-oriented feedback, focusing on performance and personal ability. Whereas parents who view failure-as-enhancing provided process-oriented feedback, focusing on effort and strategies. Taken all together, our results allow us to determine that although parents might already have a growth intelligence mindset, they don’t necessarily have a failure-as-enhancing mindset. Parents adopting a failure-as-enhancing mindset would influence their children to view failure as a learning opportunity, further promoting practice, effort, and perseverance during challenging tasks. The focus placed on a child’s learning, rather than their performance, encourages them to perceive intelligence as malleable (growth mindset) rather than fix (fixed mindset). This implies that parents should not only hold a growth mindset but thoroughly understand their role in the transmission of intelligence beliefs.

Keywords: mindset(s), failure, intelligence, parental feedback, parents

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Authors: Yosi Maria Wijaya, Florisma Arista Riti Tegu

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Background: Hypertension in pregnancy can be prevented by controlling the lifestyle. However, the majority of research on this topic has been conducted on lifestyle in women with normal pregnancy. Few studies of lifestyle have focused on Indonesian pregnant women with hypertension. Aim: The purpose of this study is to determine the association of demographic characteristics and the lifestyle of pregnant women who have hypertension. Methods: In this cross-sectional study, 76 women with hypertension during pregnancy were recruited from primary health care, West Java, Indonesia. Inclusion criteria were gestational age ≥ 28 weeks with the blood pressure systole ≥ 140 mmHg and diastole ≥ 90 mmHg. Data were collected using two instruments: demographic data and Health Promoting Life Style Profile (HPLP II). Data were analyzed with descriptive statistic and linear regression analysis. Results: The majority of participants were married, mean age was 27.96 years old (SD=6.77) with the mean of gestational age 33.21 (SD=3.49), most of them unemployed (94.7%) and more than a half participants have an education less than twelve years (59.2%). The total score of lifestyle was 2.44 (SD=0.34), more than a half participants experience unhealthy lifestyle (59.2%). Lifestyle was predicted by income, education years, occupation, and access to health care services, accounting for 20.8% of the total variance. Conclusion: Pregnant women with hypertension with low income, low level of education, non-occupational and hard to access health care services were related to unhealthy lifestyle. Understanding the lifestyle and associated factors contributes to health care providers ability to design effective interventions intended to improve healthy lifestyle among pregnant women with hypertension.

Keywords: demographic characteristics, hypertension, lifestyle, pregnancy

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Authors: M. Devamanalan, K. Pothiraj, M. Sudhan

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In competitive market like India, there is a high demand on the equal contribution on performance and its durability aspect of any system. In General vehicle has multiple sub-systems such as powertrain, BIW, Brakes, Actuations, Suspension and Seats etc., To withstand the market challenges, the contribution of each sub-system is very vital. The malfunction of any one sub system will directly have an impact on the performance of the major system which lead to dis-satisfaction to the end user. The Powertrain system consists of several sub-systems in which clutch is one of the prime sub-systems in MT vehicles which assist for smoother gear shifts with proper clutch dis-engagement and engagement. In general, most of the vehicles will have a mechanical or semi or full hydraulic clutch release system, whereas in small Commercial Vehicles (SCV) the majorly used clutch release system is mechanical cable release system due to its lesser cost and functional requirements. The major bottle neck in the cable type clutch release system is increase in pedal effort due to hysteresis increase and Gear shifting hard due to efficiency loss / cable slackness over the mileage accumulation of the vehicle. This study is to mainly focus on how the efficiency and hysteresis change over the mileage of the vehicle occurs because of the design architecture of outer and inner cable. The study involves several cable design validation results from vehicle level and rig level through the defined cable routing and test procedures. Results are compared to evaluate the suitable cable design architecture based on better efficiency and lower hysteresis parameters at initial and end of the validation.

Keywords: clutch, clutch cable, efficiency, architecture, cable routing

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Authors: Changju Lee, Seungmo Ku, Sondo Kim, Woojin Chang

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How to detecting financial bubble? Addressing this simple question has been the focus of a vast amount of empirical research spanning almost half a century. However, financial bubble is hard to observe and varying over the time; there needs to be more research on this area. In this paper, we used abnormal difference between common stocks price and those preferred stocks price to explain financial bubble. First, we proposed the ‘W-index’ which indicates spread between common stocks and those preferred stocks in stock market. Second, to prove that this ‘W-index’ is valid for measuring financial bubble, we showed that there is an inverse relationship between this ‘W-index’ and S&P500 rate of return. Specifically, our hypothesis is that when ‘W-index’ is comparably higher than other periods, financial bubbles are added up in stock market and vice versa; according to our hypothesis, if investors made long term investments when ‘W-index’ is high, they would have negative rate of return; however, if investors made long term investments when ‘W-index’ is low, they would have positive rate of return. By comparing correlation values and adjusted R-squared values of between W-index and S&P500 return, VIX index and S&P500 return, and TED index and S&P500 return, we showed only W-index has significant relationship between S&P500 rate of return. In addition, we figured out how long investors should hold their investment position regard the effect of financial bubble. Using this W-index, investors could measure financial bubble in the market and invest with low risk.

Keywords: financial bubble detection, future return, forecasting, pairs trading, preferred stocks

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Authors: Lian Yang

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Object-oriented programming (OOP) is the dominant programming paradigm in today’s software industry and it has literally enabled average software developers to develop millions of commercial strength software applications in the era of INTERNET revolution over the past three decades. On the other hand, the lack of strict mathematical model and domain constraint features at the language level has long perplexed the computer science academia and OOP engineering community. This situation resulted in inconsistent system qualities and hard-to-understand designs in some OOP projects. The difficulties with regards to fix the current situation are also well known. Although the power of OOP lies in its unbridled flexibility and enormously rich data modeling capability, we argue that the ambiguity and the implicit facade surrounding the conceptual model of a class and an object should be eliminated as much as possible. We listed the five major usage of class and propose to separate them by proposing new language constructs. By using well-established theories of set and FSM, we propose to apply certain simple, generic, and yet effective constraints at OOP language level in an attempt to find a possible solution to the above-mentioned issues regarding OOP. The goal is to make OOP more theoretically sound as well as to aid programmers uncover warning signs of irregularities and domain-specific issues in applications early on the development stage and catch semantic mistakes at runtime, improving correctness verifiability of software programs. On the other hand, the aim of this paper is more practical than theoretical.

Keywords: new language constructs, set theory, FSM theory, user defined value type, function groups, membership qualification attribute (MQA), check-constraint (CC)

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Authors: Timothy Antoniuk

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This paper will demonstrate why the most successful and livable cities in the future will require multi-disciplinary designers to develop a deep understanding of peoples’ changing lifestyles, and why new generations of deeply integrated products, services and experiences need to be created. Disseminating research from the UNEP Creative Economy Reports and through a variety of other consumption and economic-based statistics, a compelling argument will be made that it is peoples’ living spaces that offer the easiest and most significant affordances for inducing positive changes to their wellbeing, and to a city’s economic and environmental prosperity. This idea, that leveraging happiness, wellbeing and prosperity through creating new concepts and typologies of ‘home’, puts people and their needs, wants, desires, aspirations and lifestyles at the beginning of the design process, not at the end, as so often occurs with current-day multi-unit housing construction. As an important part of the creative-reflective and statistical comparisons that are necessary for this on-going body of research and practice, Professor Antoniuk created the Micro Habitation Lab (mHabLab) in 2016. By focusing on testing the functional and economic feasibility of activating small spaces with different types of architectural robotics, a variety of movable, expandable and interactive objects have been hybridized and integrated into the architectural structure of the Lab. Allowing the team to test new ideas continually and accumulate thousands of points of feedback from everyday consumers, a series of on-going open houses is allowing the public-at-large to see, physically engage with, and give feedback on the items they find most and least valuable. This iterative approach of testing has exposed two key findings: Firstly, that there is a clear opportunity to improve the macro and micro functionality of small living spaces; and secondly, that allowing people to physically alter smaller elements of their living space lessens feelings of frustration and enhances feelings of pride and a deeper perception of “home”. Equally interesting to these findings is a grouping of new research questions that are being exposed which relate to: The duality of space; how people can be in two living spaces at one time; and how small living spaces is moving the Extended Home into the public realm.

Keywords: architectural robotics, extended home, interactivity, micro living spaces

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Authors: Zaid Jamal Saeed Almahmoud

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Our increasing reliance on electricity, with inefficient consumption trends, has resulted in several economical and environmental threats. These threats include wasting billions of dollars, draining limited resources, and elevating the impact of climate change. As a solution, the smart grid is emerging as the future power grid, with smart techniques to optimize power consumption and electricity generation. Minimizing the peak power consumption under a fixed delay requirement is a significant problem in the smart grid. In addition, matching demand to supply is a key requirement for the success of the future electricity. In this work, we consider the problem of minimizing the peak demand under appliances constraints by scheduling power jobs with uniform release dates and deadlines. As the problem is known to be NP-Hard, we propose two versions of a heuristic algorithm for solving this problem. Our theoretical analysis and experimental results show that our proposed heuristics outperform existing methods by providing a better approximation to the optimal solution. In addition, we consider dynamic pricing methods to minimize the peak load and match demand to supply in the smart grid. Our contribution is the proposal of generic, as well as customized pricing heuristics to minimize the peak demand and match demand with supply. In addition, we propose optimal pricing algorithms that can be used when the maximum deadline period of the power jobs is relatively small. Finally, we provide theoretical analysis and conduct several experiments to evaluate the performance of the proposed algorithms.

Keywords: heuristics, optimization, smart grid, peak demand, power supply

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Authors: Sangwoo Han, Saeed Khaleghi Rahimian, Ying Liu

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Vehicle electrification is gaining momentum, and many car manufacturers promise to deliver more electric vehicle (EV) models to consumers in the coming years. In controlling the battery pack, the battery management system (BMS) must maintain optimal battery performance while ensuring the safety of a battery pack. Tasks related to battery performance include determining state-of-charge (SOC), state-of-power (SOP), state-of-health (SOH), cell balancing, and battery charging. Safety related functions include making sure cells operate within specified, static and dynamic voltage window and temperature range, derating power, detecting faulty cells, and warning the user if necessary. The BMS often utilizes an RC circuit model to model a Li-ion cell because of its robustness and low computation cost among other benefits. Because an equivalent circuit model such as the RC model is not a physics-based model, it can never be a prognostic model to predict battery state-of-health and avoid any safety risk even before it occurs. A physics-based Li-ion cell model, on the other hand, is more capable at the expense of computation cost. To avoid the high computation cost associated with a full-order model, many researchers have demonstrated the use of a single particle model (SPM) for BMS applications. One drawback associated with the single particle modeling approach is that it forces to use the average current density in the calculation. The SPM would be appropriate for simulating drive cycles where there is insufficient time to develop a significant current distribution within an electrode. However, under a continuous or high-pulse electrical load, the model may fail to predict cell voltage or Li⁺ plating potential. To overcome this issue, a multi-particle reduced-order model is proposed here. The use of multiple particles combined with either linear or nonlinear charge-transfer reaction kinetics enables to capture current density distribution within an electrode under any type of electrical load. To maintain computational complexity like that of an SPM, governing equations are solved sequentially to minimize iterative solving processes. Furthermore, the model is validated against a full-order model implemented in COMSOL Multiphysics.

Keywords: battery management system, physics-based li-ion cell model, reduced-order model, single-particle and multi-particle model

Procedia PDF Downloads 108