Search results for: vector space models

Authors: Milica Ž. Karadžić, Lidija R. Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Z. Kovačević, Anamarija I. Mandić, Katarina Penov-Gaši, Andrea R. Nikolić, Aleksandar M. Oklješa

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This study considered selection of the in silico molecular descriptors and the models for newly synthesized steroid derivatives description and their characterization using chemometric techniques. Multiple linear regression (MLR) models were established and gave the best molecular descriptors for quantitative structure-retention relationship (QSRR) modeling of the retention of the investigated molecules. MLR models were without multicollinearity among the selected molecular descriptors according to the variance inflation factor (VIF) values. Used molecular descriptors were ranked using generalized pair correlation method (GPCM). In this method, the significant difference between independent variables can be noticed regardless almost equal correlation between dependent variable. Generated MLR models were statistically and cross-validated and the best models were kept. Models were ranked using sum of ranking differences (SRD) method. According to this method, the most consistent QSRR model can be found and similarity or dissimilarity between the models could be noticed. In this study, SRD was performed using average values of experimentally observed data as a golden standard. Chemometric analysis was conducted in order to characterize newly synthesized steroid derivatives for further investigation regarding their potential biological activity and further synthesis. This article is based upon work from COST Action (CM1105), supported by COST (European Cooperation in Science and Technology).

Keywords: generalized pair correlation method, molecular descriptors, regression analysis, steroids, sum of ranking differences

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Authors: Rajlekha Sil

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This paper aims to investigate Ray’s portrayal of liminality and the female gaze in ‘Charulata’ (1964) and ‘Ghare Baire’ (1984), both of which primarily articulates the stories of two women (Charu and Bimala respectively), entangled within the cobwebs of their seething, unfulfilled sexuality, amidst a newly-globalised urban culture, punctuated with political turmoils and ideological conflicts. Their tempestuous interactions with the societal space, both tangible and intangible, that surrounds them, and the men in their lives makes them dwell in a liminal space, filled with an ambiguous sense of virtue. In Ray’s films, this sexual ambivalence is characterised by the liminality between the scenic and extrascenic spaces, which, in turn, defines the female gaze as the director’s lenses paint a picture of the new wave of socio-political and socio-cultural movements in early twentieth-century Bengal. Brinda Bose’s essay on ‘Modernity, Globality, Sexuality, and the City: A Reading of Indian Cinema’, analyses the ‘necessary’ process of urbanisation as a marker of ‘moral degeneracy of the nation easily analogous with female sexual transgression/ promiscuity with the nation personified as a woman, by using the concept of the liminal space, a site of both empowerment through transgression and containment through regulation.’ My paper, however, would focus on the liminal space propagated by Ray through his contrasting depiction of scenic and extrascenic spaces to satiate the equivocal voices in Charu and Bimala, along with their way of ‘gazing’ into an equally disheveled society - a gaze that helps them transcend the barriers of politics and urbanization into a state of universal uniformity, symmetrical with their sexual immorality. The first section of the paper would explore Ray’s usage of these theatrical spaces through his character sketches, shots and dialogues, while the second section of the paper would delve into the ‘female gaze’ on a newly revolutionised society.

Keywords: satyajit ray, space, gaze, female sexuality, charulata, ghare baire

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Authors: Mahvish Maqbool, Muhmmad Sohail Sajid

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Ticks (Acari: Ixodidae); bloodsucking parasites of domestic animals, have significant importance in the transmission of diseases and causing huge economic losses. This study aimed to screen endophilic ticks for the Piroplasms using polymerase chain reaction in three districts Sialkot, Gujranwala and Gujarat of Punjab, Pakistan. Ticks were dissected under a stereomicroscope, and internal organs (midguts& salivary glands) were procured to generate pools of optimum weights. DNA extraction was done through standard protocol followed by primer specific PCR for Piroplasma spp. A total of 22.95% tick pools were found positive for piroplasma spp. In districts, Sialkot and Gujranwala Piroplasma prevalence are higher in riverine animals while in Gujarat Prevalence is higher in non-riverine animals. Female animals were found more prone to piroplasma as compared to males. This study will provide useful data on the distribution of Piroplasma in the vector population of the study area and devise future recommendations for better management of ruminants to avoid subclinical and clinical infections and vector transmitted diseases.

Keywords: babesia, hyalomma, piroplasmposis, tick infectivity

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Authors: Navid Daryasafar, Nima Farshidfar

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In this article, we make attempt to hide error in video with an emphasis on the time-wise use of autoregressive (AR) models. To resolve this problem, we assume that all information in one or more video frames is lost. Then, lost frames are estimated using analogous Pixels time information in successive frames. Accordingly, after presenting autoregressive models and how they are applied to estimate lost frames, two general methods are presented for using these models. The first method which is the same standard method of autoregressive models estimates lost frame in unidirectional form. Usually, in such condition, previous frames information is used for estimating lost frame. Yet, in the second method, information from the previous and next frames is used for estimating the lost frame. As a result, this method is known as bidirectional estimation. Then, carrying out a series of tests, performance of each method is assessed in different modes. And, results are compared.

Keywords: error steganography, unidirectional estimation, bidirectional estimation, AR linear estimation

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Authors: Mohannad Tarrad

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The research deals with the beginnings of the vernacular Jordanian architecture since the establishment of the Jordanian state in the early nineteenth century until now, where the Jordanian architecture was based on the interactions of the Jordanian society with the surrounding environment, where the local materials available in the construction area were used, and the construction systems inherited from previous civilizations were used. The builders in Jordan relied on exchanging knowledge and transferring it from one generation to another, where they were able to formulate a construction style capable of responding to the requirement of architectural spaces, and each region of Jordan has its own way of building, as there are various geographical areas in Jordan, including agricultural, mountainous and desert areas. Then the research touched on a historical study of the architectural space and identifying the value of the architectural space in the Jordanian social life, which is related to the customs and traditions of a society influenced by the Arab Islamic civilization, and then the construction, the structural structure, its characteristics and the constituent elements of the building were defined in the vernacular l Jordanian architecture. From the structural point of view, and then to identify the structural materials used in the structural structure and the impact of the structural structure on the design from several aspects, leading to the interior space and the factors affecting it. The research aims to explain and clarify the interconnected design and construction solutions in the vernacular Jordanian architecture in a manner that respects the environmental context, taking into account the material cost of the building, where the financial situation of the home owner plays an important role in choosing the building material and construction method. Case studies from heritage buildings from several Jordanian regions will be analyzed to illustrate the idea of the research.

Keywords: construction systems, architectural space, environmental context, Jordanian architecture

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: Mohsina Akhter

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Cancer has become a wide spread disease around the globe and takes many lives every year. Different treatments are being practiced but all have potential side effects with somewhat less specificity towards target sites. Pseudomonas aeruginosa is known to secrete a protein azurin with special anti-cancer function. It has unique cell penetrating peptide comprising of 18 amino acids that have ability to enter cancer cells specifically. Reported function of Azurin is to stabilize p53 inside the tumor cells and induces apoptosis through Bax mediated cytochrome c release from mitochondria. At laboratory scale, we have made recombinant azurin through cloning rpTZ57R/T-azu vector into E.coli strain DH-5α and subcloning rpET28-azu vector into E.coli BL21-CodonPlus (DE3). High expression was ensured with IPTG induction at different concentrations then optimized high expression level at 1mM concentration of IPTG for 5 hours. Purification has been done by using Ni+2 affinity chromatography. We have concluded that azurin can be a remarkable improvement in cancer therapeutics if it produces on a large scale. Azurin does not enter into the normal cells so it will prove a safe and secure treatment for patients and prevent them from hazardous anomalies.

Keywords: azurin, pseudomonas aeruginosa, cancer, therapeutics

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Authors: Jeongmin Kim, Eunju Lee, Kwang Ryel Ryu

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This paper addresses the problem of predictive control for energy management of large-scaled exhibition halls, where a lot of energy is consumed to maintain internal atmosphere under certain required conditions. Predictive control achieves better energy efficiency by optimizing the operation of air-conditioning facilities with not only the current but also some future status taken into account. In this paper, we propose to use predictive models learned from past sensor data of hall environment, for use in optimizing the operating plan for the air-conditioning facilities by simulating future environmental change. We have implemented an emulator of an exhibition hall by using EnergyPlus, a widely used building energy emulation tool, to collect data for learning environment-change models. Experimental results show that the learned models predict future change highly accurately on a short-term basis.

Keywords: predictive control, energy management, machine learning, optimization

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Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

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In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

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Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Chris Neale, Jenny Roe, Peter Aspinall, Sara Tilley, Steve Cinderby, Panos Mavros, Richard Coyne, Neil Thin, Catharine Ward Thompson

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This research directly assesses older people’s neural activation in response to walking through a changing urban environment, as measured by electroencephalography (EEG). As the global urban population is predicted to grow, there is a need to understand the role that the urban environment may play on the health of its older inhabitants. There is a large body of evidence suggesting green space has a beneficial restorative effect, but this effect remains largely understudied in both older people and by using a neuroimaging assessment. For this study, participants aged 65 years and over were required to walk between a busy urban built environment and a green urban environment, in a counterbalanced design, wearing an Emotiv EEG headset to record real-time neural responses to place. Here we report on the outputs for these responses derived from both the proprietary Affectiv Suite software, which creates emotional parameters with a real time value assigned to them, as well as the raw EEG output focusing on alpha and beta changes, associated with changes in relaxation and attention respectively. Each walk lasted around fifteen minutes and was undertaken at the natural walking pace of the participant. The two walking environments were compared using a form of high dimensional correlated component regression (CCR) on difference data between the urban busy and urban green spaces. For the Emotiv parameters, results showed that levels of ‘engagement’ increased in the urban green space (with a subsequent decrease in the urban busy built space) whereas levels of ‘excitement’ increased in the urban busy environment (with a subsequent decrease in the urban green space). In the raw data, low beta (13 – 19 Hz) increased in the urban busy space with a subsequent decrease shown in the green space, similar to the pattern shown with the ‘excitement’ result. Alpha activity (9 – 13 Hz) shows a correlation with low beta, but not with dependent change in the regression model. This suggests that alpha is acting as a suppressor variable. These results suggest that there are neural signatures associated with the experience of urban spaces which may reflect the age of the cohort or the spatiality of the settings themselves. These are shown both in the outputs of the proprietary software as well as the raw EEG output. Built busy urban spaces appear to induce neural activity associated with vigilance and low level stress, while this effect is ameliorated in the urban green space, potentially suggesting a beneficial effect on attentional capacity in urban green space in this participant group. The interaction between low beta and alpha requires further investigation, in particular the role of alpha in this relationship.

Keywords: ageing, EEG, green space, urban space

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Authors: Chusak Thanawattano, Roongroj Bhidayasiri

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This paper proposes a novel feature set utilized for classifying the Parkinson tremor and essential tremor. Ten ET and ten PD subjects are asked to perform kinetic, postural and resting tests. The empirical mode decomposition (EMD) is used to decompose collected tremor signal to a set of intrinsic mode functions (IMF). The IMFs are used for reconstructing representative signals. The feature set is composed of peak frequencies of IMFs and reconstructed signals. Hypothesize that the dominant frequency components of subjects with PD and ET change in different directions for different tests, difference of peak frequencies of IMFs and reconstructed signals of pairwise based tests (kinetic-resting, kinetic-postural and postural-resting) are considered as potential features. Sets of features are used to train and test by classifier including the quadratic discriminant classifier (QLC) and the support vector machine (SVM). The best accuracy, the best sensitivity and the best specificity are 90%, 87.5%, and 92.86%, respectively.

Keywords: tremor, Parkinson, essential tremor, empirical mode decomposition, quadratic discriminant, support vector machine, peak frequency, auto-regressive, spectrum estimation

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Authors: Josip Vasilj, Petar Sarajcev, Damir Jakus

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One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds.

Keywords: wind power, uncertainty, stochastic process, Monte Carlo simulation

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Authors: Chantal Hendriks, Harry Gibson, Anna Trett, Penny Hancock, Catherine Moyes

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The use of modern inputs for crop protection, such as insecticides, is strongly underestimated in Sub-Saharan Africa. Several studies measured toxic concentrations of insecticides in fruits, vegetables and fish that were cultivated in Sub-Saharan Africa. The use of agricultural insecticides has impact on human and environmental health, but it also has the potential to impact on insecticide resistance in malaria transmitting mosquitos. To analyse associations between historic use of agricultural insecticides and the distribution of insecticide resistance through space and time, the use and environmental fate of agricultural insecticides needs to be mapped through the same time period. However, data on the use and environmental fate of agricultural insecticides in Africa are limited and therefore risk maps on the spatiotemporal occurrence of agricultural insecticides are created using environmental data. Environmental data on crop density and crop type were used to select the areas that most likely receive insecticides. These areas were verified by a literature review and expert knowledge. Pesticide fate models were compared to select most dominant processes that are involved in the environmental fate of insecticides and that can be mapped at a continental scale. The selected processes include: surface runoff, erosion, infiltration, volatilization and the storing and filtering capacity of soils. The processes indicate the risk for insecticide accumulation in soil, water, sediment and air. A compilation of all available data for traces of insecticides in the environment was used to validate the maps. The risk maps can result in space and time specific measures that reduce the risk of insecticide exposure to non-target organisms.

Keywords: crop protection, pesticide fate, tropics, insecticide resistance

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Authors: Sylvain Chardon, Timotéo Payre, Hugo Grardel, Yann Quentel, Mathieu Thomachot, Gérald Aigouy, Frank Claeyssen

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With the increasing digitalization of society, access to data has become vital and strategic for individuals and nations. In this context, the number of satellite constellation projects is growing drastically worldwide and is a next-generation challenge of the New Space industry. So far, existing satellite constellations have been using radio frequencies (RF) for satellite-to-ground communications, inter-satellite communications, and feeder link communication. However, RF has several limitations, such as limited bandwidth and low protection level. To address these limitations, space optical communication will be the new trend, addressing both very high-speed and secured encrypted communication. Fast Steering Mirrors (FSM) are key components used in optical communication as well as space imagery and for a large field of functions such as Point Ahead Mechanisms (PAM), Raster Scanning, Beam Steering Mirrors (BSM), Fine Pointing Mechanisms (FPM) and Line of Sight stabilization (LOS). The main challenges of space FSM development for optical communication are to propose both a technology and a supply chain relevant for high quantities New Space approach, which requires secured connectivity for high-speed internet, Earth planet observation and monitoring, and mobility applications. CTEC proposes a mini-FSM technology offering a stroke of +/-6 mrad and a resonant frequency of 1700 Hz, with a mass of 50 gr. This FSM mechanism is a good candidate for giant constellations and all applications on board NanoSats and CubeSats, featuring a very high level of miniaturization and optimized for New Space high quantities cost efficiency. The use of piezo actuators offers a high resonance frequency for optimal control, with almost zero power consumption in step and stay pointing, and with very high-reliability figures > 0,995 demonstrated over years of recurrent manufacturing for Optronics applications at CTEC.

Keywords: fast steering mirror, feeder link, line of sight stabilization, optical communication, pointing ahead mechanism, raster scan

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Authors: Hyojin Lim, Cuong Nguyen Khac, Ho-Youl Jung

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In this paper, we propose moving object detection method which is helpful for driver to safely take his/her car out of parking lot. When moving objects such as motorbikes, pedestrians, the other cars and some obstacles are detected at the rear-side of host vehicle, the proposed algorithm can provide to driver warning. We assume that the host vehicle is just before departure. Gaussian Mixture Model (GMM) based background subtraction is basically applied. Pre-processing such as smoothing and post-processing as morphological filtering are added.We examine “which color space has better performance for detection of moving objects?” Three color spaces including RGB, YCbCr, and Y are applied and compared, in terms of detection rate. Through simulation, we prove that RGB space is more suitable for moving object detection based on background subtraction.

Keywords: gaussian mixture model, background subtraction, moving object detection, color space, morphological filtering

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Authors: Sudip Kumar Kundu, Charu Singh

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As a great physiographic divide, the Himalayas affecting a large system of water and air circulation which helps to determine the climatic condition in the Indian subcontinent to the south and mid-Asian highlands to the north. It creates obstacles by defending chill continental air from north side into India in winter and also defends rain-bearing southwesterly monsoon to give up maximum precipitation in that area in monsoon season. Nowadays extreme weather conditions such as heavy precipitation, cloudburst, flash flood, landslide and extreme avalanches are the regular happening incidents in the region of North Western Himalayan (NWH). The present study has been planned to investigate the suitable model(s) to find out the rainfall pattern over that region. For this investigation, selected models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and Coupled Model Intercomparison Project Phase 5 (CMIP5) has been utilized in a consistent framework for the period of 1976 to 2000 (historical). The ability of these driving models from CORDEX domain and CMIP5 has been examined according to their capability of the spatial distribution as well as time series plot of rainfall over NWH in the rainy season and compared with the ground-based Indian Meteorological Department (IMD) gridded rainfall data set. It is noted from the analysis that the models like MIROC5 and MPI-ESM-LR from the both CORDEX and CMIP5 provide the best spatial distribution of rainfall over NWH region. But the driving models from CORDEX underestimates the daily rainfall amount as compared to CMIP5 driving models as it is unable to capture daily rainfall data properly when it has been plotted for time series (TS) individually for the state of Uttarakhand (UK) and Himachal Pradesh (HP). So finally it can be said that the driving models from CMIP5 are better than CORDEX domain models to investigate the rainfall pattern over NWH region.

Keywords: global warming, rainfall, CMIP5, CORDEX, NWH

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Authors: Lei Qi, Rongxin Yan, Lichen Sun

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With the increasing of human space activities, the number of space debris has increased dramatically, and the possibility that spacecrafts on orbit are impacted by space debris is growing. A method is of the vital significance to real-time detect and assess spacecraft damage, determine of gas leak accurately, guarantee the life safety of the astronaut effectively. In this paper, acoustic sensor array is used to detect the acoustic signal which emits from the damage of the spacecraft on orbit. Then, we apply the time difference of arrival and beam forming algorithm to locate the damage and leakage. Finally, the extent of the spacecraft damage is evaluated according to the nonlinear ultrasonic method. The result shows that this method can detect the debris impact and the structural damage, locate the damage position, and identify the damage degree effectively. This method can meet the needs of structural damage detection for the spacecraft in-orbit.

Keywords: acoustic sensor array, spacecraft, damage assessment, leakage location

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Authors: Gunjan Saxena

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This paper presents case insights from India and Mexico to illustrate how tourism can work as a tool for bringing about peace and prosperity in disadvantaged communities living in peripheral rural localities. A reordering of rural space, given the slow but definite shift from production-oriented activities towards more complex and contested blends of production, consumption and protection indicates clearly that rurality is in flux. Whilst on one hand, there is a predominance of urban interests in the countryside, on the other rurality is boxed and presented for consumption in urban localities. Qualitative data, collected using semi-structured interviews and participant observation, is used in illustrating how creative enterprise is bringing about innovative use of rural ethos and space in response to consumer demands. Overall, this work seeks to contribute to debates on how rurality no longer represents a fixed space of tradition, but is packaged and promoted in a multi-faceted manner to creatively perform for and access tourism markets.

Keywords: rural tourism, creative enterprise, India, Mexico

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Authors: Hassan Eshkiki, Benjamin Mora

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The Neighbor Migrating Generator is a simple and efficient approach to finding the closest potential neighbor(s) with a different label for a given instance and so without the need to calibrate any kernel settings at all. This allows determining and explaining the most important features that will influence an AI model. It can be used to either migrate a specific sample to the class decision boundary of the original model within a close neighborhood of that sample or identify global features that can help localising neighbor classes. The proposed technique works by minimizing a loss function that is divided into two components which are independently weighted according to three parameters α, β, and ω, α being self-adjusting. Results show that this approach is superior to past techniques when detecting the smallest changes in the feature space and may also point out issues in models like over-fitting.

Keywords: explainable AI, EX AI, feature importance, counterfactual explanations

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Chuan Yang, Jing Bie, Yueh-Lung Lin, Zhong Wang

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Transportation planning is an academic integration discipline combining research and practice with the aim of mobility and accessibility improvements at both strategic-level policy-making and operational dimensions of practical planning. Transportation planning could build the linkage between traffic and social development goals, for instance, economic benefits and environmental sustainability. The transportation planning analysis and evaluation tend to apply empirical quantitative approaches with the guidance of the fundamental principles, such as efficiency, equity, safety, and sustainability. Space syntax theory has been applied in the spatial distribution of pedestrian movement or vehicle flow analysis, however rare has been written about its application in transportation planning. The correlated relationship between the variables of space syntax analysis and authentic observations have declared that the urban configurations have a significant effect on urban dynamics, for instance, land value, building density, traffic, crime. This research aims to explore the potentials of applying Space Syntax methodology to evaluate urban transportation planning through studying the effects of urban configuration on cities transportation performance. By literature review, this paper aims to discuss the effects that urban configuration with different degrees of integration and accessibility have on three elementary components of transportation planning - transportation efficiency, transportation safety, and economic agglomeration development - via intensifying and stabilising the nature movements generated by the street network. And then the potential and limits of Space Syntax theory to study the performance of urban transportation and transportation planning would be discussed in the paper. In practical terms, this research will help future research explore the effects of urban design on transportation performance, and identify which patterns of urban street networks would allow for most efficient and safe transportation performance with higher economic benefits.

Keywords: transportation planning, space syntax, economic agglomeration, transportation efficiency, transportation safety

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: H. C. Chinwenyi, H. D. Ibrahim, F. A. Ahmed

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In modern financial mathematics, valuing derivatives such as options is often a tedious task. This is simply because their fair and correct prices in the future are often probabilistic. This paper examines three different Stochastic Differential Equation (SDE) models in finance; the Constant Elasticity of Variance (CEV) model, the Balck-Karasinski model, and the Heston model. The various Martingales option price valuation formulas for these three models were obtained using the replicating portfolio method. Also, the numerical solution of the derived Martingales options price valuation equations for the SDEs models was carried out using the Monte Carlo method which was implemented using MATLAB. Furthermore, results from the numerical examples using published data from the Nigeria Stock Exchange (NSE), all share index data show the effect of increase in the underlying asset value (stock price) on the value of the European Put Option for these models. From the results obtained, we see that an increase in the stock price yields a decrease in the value of the European put option price. Hence, this guides the option holder in making a quality decision by not exercising his right on the option.

Keywords: equivalent martingale measure, European put option, girsanov theorem, martingales, monte carlo method, option price valuation formula

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Authors: Adilson Elias Xavier, Otto Corrêa Rotunno Filho, Paulo Canedo De Magalhães

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This paper addresses the issue of automatic parameter estimation in conceptual rainfall-runoff (CRR) models. Due to threshold structures commonly occurring in CRR models, the associated mathematical optimization problems have the significant characteristic of being strongly non-differentiable. In order to face this enormous task, the resolution method proposed adopts a smoothing strategy using a special C∞ differentiable class function. The final estimation solution is obtained by solving a sequence of differentiable subproblems which gradually approach the original conceptual problem. The use of this technique, called Hyperbolic Smoothing Method (HSM), makes possible the application of the most powerful minimization algorithms, and also allows for the main difficulties presented by the original CRR problem to be overcome. A set of computational experiments is presented for the purpose of illustrating both the reliability and the efficiency of the proposed approach.

Keywords: rainfall-runoff models, automatic calibration, hyperbolic smoothing method

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss

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Authors: Jian-Heng Wu, Bor-Shen Lin

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The temperature-salinity relationship is one of the most important characteristics used for identifying water masses in marine research. Temperature-salinity characteristics, however, may change dynamically with respect to the geographic location and is quite sensitive to the depth at the same location. When depth is taken into consideration, however, it is not easy to compare the characteristics of different water masses efficiently for a wide range of areas of the ocean. In this paper, the Gaussian mixture model was proposed to analyze the temperature-salinity-depth characteristics of water masses, based on which comparison between water masses may be conducted. Gaussian mixture model could model the distribution of a random vector and is formulated as the weighting sum for a set of multivariate normal distributions. The temperature-salinity-depth data for different locations are first used to train a set of Gaussian mixture models individually. The distance between two Gaussian mixture models can then be defined as the weighting sum of pairwise Bhattacharyya distances among the Gaussian distributions. Consequently, the distance between two water masses may be measured fast, which allows the automatic and efficient comparison of the water masses for a wide range area. The proposed approach not only can approximate the distribution of temperature, salinity, and depth directly without the prior knowledge for assuming the regression family, but may restrict the complexity by controlling the number of mixtures when the amounts of samples are unevenly distributed. In addition, it is critical for knowledge discovery in marine research to represent, manage and share the temperature-salinity-depth characteristics flexibly and responsively. The proposed approach has been applied to a real-time visualization system of ocean data, which may facilitate the comparison of water masses by aggregating the data without degrading the discriminating capabilities. This system provides an interface for querying geographic locations with similar temperature-salinity-depth characteristics interactively and for tracking specific patterns of water masses, such as the Kuroshio near Taiwan or those in the South China Sea.

Keywords: water mass, Gaussian mixture model, data visualization, system framework

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Authors: Jun Ha Whang

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As New York City weathers a swelling 'affordability crisis' marked by rapid transformation in land development and urban culture, much of the associated scholarly debate has turned to questions of the underlying mechanisms of gentrification. Though classically approached from the point of view of urban planning, increasingly these questions have been addressed with an eye to understanding the role of cultural capital in neighborhood valuation. This paper will examine the construction of an artist-specific affordable housing development in the Spanish Harlem neighborhood of Manhattan in order to identify and discuss several cultural parameters of gentrification. This study’s goal is not to argue that the development in question, named Art space PS 109, straightforwardly increases or decreases the rate of gentrification in Spanish Harlem, but rather to study dynamics present in the construction of Art space PS 109 as a case study considered against the broader landscape of gentrification in New York, particularly with respect to the impact of artist communities on housing supply. In the end, what Art space PS 109 most valuably offers us is a reference point for a comparative analysis of affordable housing strategies currently being pursued within municipal government. Our study of Art space PS 109 has allowed us to examine a microcosm of the city’s response and evaluate its overall strategy accordingly. As a base line, the city must aggressively pursue an affordability strategy specifically suited to the needs of each of its neighborhoods. It must also conduct this in such a way so as not to undermine its own efforts by rendering them susceptible to the exploitative involvement of real estate developers seeking to identify successive waves of trendy neighborhoods. Though Art space PS 109 offers an invaluable resource for the city’s legitimate aim of preserving its artist communities, with such a high inclusion rate of artists from outside of the community the project risks additional displacement, strongly suggesting the need for further study of the implications of sites of cultural capital for neighborhood planning.

Keywords: artist housing, displacement, east Harlem, urban planning

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Authors: Mehdi Seifbarghy, Zahra Mansouri

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In this paper a few location-allocation models are developed in a multi-echelon supply chain including suppliers, manufacturers, distributors and retailers. The objectives are maximizing demand coverage, minimizing the total distance of distributors from suppliers, minimizing some facility establishment costs and minimizing the environmental effects. Since nature of the given models is multi-objective, we suggest a number of goal-based solution techniques such L-P metric, goal programming, multi-choice goal programming and goal attainment in order to solve the problems.

Keywords: location, multi-echelon supply chain, covering, goal programming

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