Search results for: spatiotemporal features

Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Hironori Karachi, Haruka Yamashita

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Recently, the system of quick response (QR) code is getting popular. Many companies introduce new QR code payment services and the services are competing with each other to increase the number of users. For increasing the number of users, we should grasp the difference of feature of the demographic information, usage information, and value of users between services. In this study, we conduct an analysis of real-world data provided by Nomura Research Institute including the demographic data of users and information of users’ usages of two services; LINE Pay, and PayPay. For analyzing such data and interpret the feature of them, Nonnegative Matrix Factorization (NMF) is widely used; however, in case of the target data, there is a problem of the missing data. EM-algorithm NMF (EMNMF) to complete unknown values for understanding the feature of the given data presented by matrix shape. Moreover, for comparing the result of the NMF analysis of two matrices, there is Discriminant NMF (DNMF) shows the difference of users features between two matrices. In this study, we combine EMNMF and DNMF and also analyze the target data. As the interpretation, we show the difference of the features of users between LINE Pay and Paypay.

Keywords: data science, non-negative matrix factorization, missing data, quality of services

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Authors: Ning Kang, Marius Doornenbal

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Funding is a very important – if not crucial – resource for research projects. Usually, funding organizations will publish a description of the funded research to describe the scope of the funding award. Logically, we would expect research outcomes to align with this funding award. For that reason, we might be able to predict future research topics based on present funding award data. That said, it remains to be shown if and how future research topics can be predicted by using the funding information. In this paper, we extract funding project information and their generated paper abstracts from the Gateway to Research database as a group, and use the papers from the same domains and publication years in the Scopus database as a baseline comparison group. We annotate both the project awards and the papers resulting from the funded projects with linguistic features (noun phrases), and then calculate tf-idf and cosine similarity between these two set of features. We show that the cosine similarity between the project-generated papers group is bigger than the project-baseline group, and also that these two groups of similarities are significantly different. Based on this result, we conclude that the funding information actually correlates with the content of future research output for the funded project on the topical level. How funding really changes the course of science or of scientific careers remains an elusive question.

Keywords: natural language processing, noun phrase, tf-idf, cosine similarity

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Authors: Ting-Yu Hsu, Shyu-Yu Wu, Shieh-Kung Huang, Hung-Wei Chiang, Kung-Chun Lu, Pei-Yang Lin, Kuo-Liang Wen

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Regional earthquake early warning (EEW) systems are not suitable for Taiwan, as most destructive seismic hazards arise due to in-land earthquakes. These likely cause the lead-time provided by regional EEW systems before a destructive earthquake wave arrives to become null. On the other hand, an on-site EEW system can provide more lead-time at a region closer to an epicenter, since only seismic information of the target site is required. Instead of leveraging the information of several stations, the on-site system extracts some P-wave features from the first few seconds of vertical ground acceleration of a single station and performs a prediction of the oncoming earthquake intensity at the same station according to these features. Since seismometers could be triggered by non-earthquake events such as a passing of a truck or other human activities, to reduce the likelihood of false alarms, a seismometer was installed at three different locations on the same site and the performance of the EEW system for these three sensor locations were discussed. The results show that the location on the ground of the first floor of a school building maybe a good choice, since the false alarms could be reduced and the cost for installation and maintenance is the lowest.

Keywords: earthquake early warning, on-site, seismometer location, support vector machine

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Authors: Ghada Badr, Arwa Alturki

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The biological function of an RNA molecule depends on its structure. The objective of the alignment is finding the homology between two or more RNA secondary structures. Knowing the common functionalities between two RNA structures allows a better understanding and a discovery of other relationships between them. Besides, identifying non-coding RNAs -that is not translated into a protein- is a popular application in which RNA structural alignment is the first step A few methods for RNA structure-to-structure alignment have been developed. Most of these methods are partial structure-to-structure, sequence-to-structure, or structure-to-sequence alignment. Less attention is given in the literature to the use of efficient RNA structure representation and the structure-to-structure alignment methods are lacking. In this paper, we introduce an O(N2) Component-based Pairwise RNA Structure Alignment (CompPSA) algorithm, where structures are given as a component-based representation and where N is the maximum number of components in the two structures. The proposed algorithm compares the two RNA secondary structures based on their weighted component features rather than on their base-pair details. Extensive experiments are conducted illustrating the efficiency of the CompPSA algorithm when compared to other approaches and on different real and simulated datasets. The CompPSA algorithm shows an accurate similarity measure between components. The algorithm gives the flexibility for the user to align the two RNA structures based on their weighted features (position, full length, and/or stem length). Moreover, the algorithm proves scalability and efficiency in time and memory performance.

Keywords: alignment, RNA secondary structure, pairwise, component-based, data mining

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Authors: Mariana Panayotova-Pencheva, Anetka Trifonova, Vassilena Dakova

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790 lungs from brown hares (Lepus europeus L.) from different regions of Bulgaria were investigated during the period 2009-2017. The parasitological status and pathomorphological features in the lungs were recorded. The following parasite species were established: one nematode - Protostrongylus tauricus (7.59% prevalence), one tapeworm – larva of Taenia pisiformis – Cysticercus pisiformis (3.04% prevalence) and one arthropod – larva of Linguatula serrata – Pentastomum dentatum (0.89% prevalence). Macroscopic lesions in the lungs were different depending on the causative agents. The infections with C. pisiformis and P. dentatum were attended with small, mainly superficial changes in the lungs. Protostrongylid infections were connected with different in appearance and burden macroscopic changes. In 77.7%, they were nodular, and in the rest of cases, they diffuse. The consistency of the lesions was compact. In most of the cases, alterations were grey in colour, rarely were dark-red or marble-like. In 91.7% of these cases, they were spread on the apical parts of large lung lobes. In 36.7% middle parts of the large lung lobes, and, in 26.7% small lung lobes, were also affected. The small lung lobes were never independently infected.

Keywords: Cysticercus pisiformis, Lepus europeus, lung lesions, Pentastomum dentatum, Protostrongylus tauricus

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Authors: Mounir Azzam, Valerie Graw, Andreas Rienow

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The Syrian war, which commenced in 2011, has resulted in significant changes in the real estate market in the Damascus metropolitan area, with rising levels of insecurity and disputes over tenure rights. The quest for spatial justice is, therefore, imperative, and this study performs a spatiotemporal analysis to investigate the impact of the war on real estate differentiation. Using the hedonic price models including 2,411 housing transactions over the period 2010-2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation and depreciation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus city resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. We monitored 250 samples from hedonic datasets within three different years of the Syrian war (2015, 2019, and 2022). Our findings show that correlation values were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. The findings have valuable implications, not only for investment prospects in setting up a successful reconstruction strategy but also for spatial justice of property rights in strongly encouraging sustainable real estate development.

Keywords: hedonic price, real estate differentiation, reconstruction strategy, spatial justice, urban texture analysis

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Michael N. O'Sullivan, Con Sheahan

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Mass customization is the idea of offering custom products or services to satisfy the needs of each individual customer while maintaining the efficiency of mass production. Technologies like 3D printing and artificial intelligence have many start-ups hoping to capitalize on this dream of creating personalized products at an affordable price, and well established companies scrambling to innovate and maintain their market share. However, the majority of them are failing as they struggle to understand one key question – where does customization make sense? Customization and personalization only make sense where the value of the perceived benefit outweighs the cost to implement it. In other words, will people pay for it? Looking at the Kano Model makes it clear that it depends on the product. In products where customization is an inherent need, like prosthetics, mass customization technologies can be highly beneficial. However, for products that already sell as a standard, like headphones, offering customization is likely only an added bonus, and so the product development team must figure out if the customers’ perception of the added value of this feature will outweigh its premium price tag. This can be done through the use of a ‘serious game,’ whereby potential customers are given a limited budget to collaboratively buy and bid on potential features of the product before it is developed. If the group choose to buy customization over other features, then the product development team should implement it into their design. If not, the team should prioritize the features on which the customers have spent their budget. The level of customization purchased can also be translated to an appropriate production method, for example, the most expensive type of customization would likely be free-form design and could be achieved through digital fabrication, while a lower level could be achieved through short batch production. Twenty-five teams of final year students from design, engineering, construction and technology tested this methodology when bringing a product from concept through to production specification, and found that it allowed them to confidently decide what level of customization, if any, would be worth offering for their product, and what would be the best method of producing it. They also found that the discussion and negotiations between players during the game led to invaluable insights, and often decided to play a second game where they offered customers the option to buy the various customization ideas that had been discussed during the first game.

Keywords: Kano model, mass customization, new product development, serious game

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Authors: Ebru Turgal, Beyza Doganay Erdogan

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Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Forouzan Salehi Fergeni

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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Zelalem Fantahun

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Part-of-speech tagging is the process of assigning a part-of-speech or other lexical class marker to each word into naturally occurring text. Part-of-speech tagging is the most fundamental and basic task almost in all natural language processing. In natural language processing, the problem of providing large amount of manually annotated data is a knowledge acquisition bottleneck. Since, Amharic is one of under-resourced language, the availability of tagged corpus is the bottleneck problem for natural language processing especially for POS tagging. A promising direction to tackle this problem is to provide a system that does not require manually tagged data. In unsupervised learning, the learner is not provided with classifications. Unsupervised algorithms seek out similarity between pieces of data in order to determine whether they can be characterized as forming a group. This paper explicates the development of unsupervised part-of-speech tagger using K-Means clustering for Amharic language since large amount of data is produced in day-to-day activities. In the development of the tagger, the following procedures are followed. First, the unlabeled data (raw text) is divided into 10 folds and tokenization phase takes place; at this level, the raw text is chunked at sentence level and then into words. The second phase is feature extraction which includes word frequency, syntactic and morphological features of a word. The third phase is clustering. Among different clustering algorithms, K-means is selected and implemented in this study that brings group of similar words together. The fourth phase is mapping, which deals with looking at each cluster carefully and the most common tag is assigned to a group. This study finds out two features that are capable of distinguishing one part-of-speech from others these are morphological feature and positional information and show that it is possible to use unsupervised learning for Amharic POS tagging. In order to increase performance of the unsupervised part-of-speech tagger, there is a need to incorporate other features that are not included in this study, such as semantic related information. Finally, based on experimental result, the performance of the system achieves a maximum of 81% accuracy.

Keywords: POS tagging, Amharic, unsupervised learning, k-means

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Davoud Maleki, Neda Zamani

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The present study aims at exploring the effective accountability model of Islamic Azad University using a qualitative approach of grounded theory. The data of this study were obtained from semi-structured interviews with 25 professors and scholars in Islamic Azad University of Tehran who were selected by theoretical sampling method. In the data analysis, the stepwise method and Strauss and Corbin analytical methods (1992) were used. After identification of the main component (balanced response to stakeholders’ needs) and using it to bring the categories together, expressions and ideas representing the relationships between the main and subcomponents, and finally, the revealed components were categorized into six dimensions of the paradigm model, with the relationships among them, including causal conditions (7 components), main component (balanced response to stakeholders’ needs), strategies (5 components), environmental conditions (5 components), intervention features (4 components), and consequences (3 components). Research findings show an exploratory model for describing the relationships between causal conditions, main components, accountability strategies, environmental conditions, university environmental features, and that consequences.

Keywords: accountability, effectiveness, Islamic Azad University, grounded theory

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Authors: Manuela Nayantara Jeyaraj, Senuri Sucharitharathna, Chathurika Senarath, Yasanthy Kanagaraj, Indraka Udayakumara

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PostgreSQL is an Object Relational Database Management System (ORDBMS) that has been in existence for a while. Despite the superior features that it wraps and packages to manage database and data, the database community has not fully realized the importance and advantages of PostgreSQL. Hence, this research tends to focus on provisioning a better environment of development for PostgreSQL in order to induce the utilization and elucidate the importance of PostgreSQL. PostgreSQL is also known to be the world’s most elementary SQL-compliant open source ORDBMS. But, users have not yet resolved to PostgreSQL due to the facts that it is still under the layers and the complexity of its persistent textual environment for an introductory user. Simply stating this, there is a dire need to explicate an easy way of making the users comprehend the procedure and standards with which databases are created, tables and the relationships among them, manipulating queries and their flow based on conditions in PostgreSQL to help the community resolve to PostgreSQL at an augmented rate. Hence, this research under development within the context tends to initially identify the dominant features provided by PostgreSQL over its competitors. Following the identified merits, an analysis on why the database community holds a hesitance in migrating to PostgreSQL’s environment will be carried out. These will be modulated and tailored based on the scope and the constraints discovered. The resultant of the research proposes a system that will serve as a designing platform as well as a learning tool that will provide an interactive method of learning via a visual editor mode and incorporate a textual editor for well-versed users. The study is based on conjuring viable solutions that analyze a user’s cognitive perception in comprehending human computer interfaces and the behavioural processing of design elements. By providing a visually draggable and manipulative environment to work with Postgresql databases and table queries, it is expected to highlight the elementary features displayed by Postgresql over any other existent systems in order to grasp and disseminate the importance and simplicity offered by this to a hesitant user.

Keywords: cognition, database, PostgreSQL, text-editor, visual-editor

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Authors: Farzam Fatolazadeh, Kalifa Goita, Mehdi Eshagh, Shusen Wang

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The Gravity Recovery and Climate Experiment (GRACE) is a satellite mission with twin satellites for the precise determination of spatial and temporal variations in the Earth’s gravity field. The products of this mission are monthly global gravity models containing the spherical harmonic coefficients and their errors. These GRACE models can be used for estimating terrestrial water storage (TWS) variations across the globe at large scales, thereby offering an opportunity for surface and groundwater storage (GWS) assessments. Yet, the ability of GRACE to monitor changes at smaller scales is too limited for local water management authorities. This is largely due to the low spatial and temporal resolutions of its models (~200,000 km2 and one month, respectively). High-resolution GRACE data products would substantially enrich the information that is needed by local-scale decision-makers while offering the data for the regions that lack adequate in situ monitoring networks, including northern parts of Canada. Such products could eventually be obtained through downscaling. In this study, we extended the spectral combination theory to simultaneously downscale spatiotemporally the 3o spatial coarse resolution of GRACE to 0.25o degrees resolution and monthly coarse resolution to daily resolution. This method combines the monthly gravity field solution of GRACE and daily hydrological model products in the form of both low and high-frequency signals to produce high spatiotemporal resolution TWSA and GWSA products. The main contribution and originality of this study are to comprehensively and simultaneously consider GRACE and hydrological variables and their uncertainties to form the estimator in the spectral domain. Therefore, it is predicted that we reach downscale products with an acceptable accuracy.

Keywords: GRACE satellite, groundwater storage, spectral combination, terrestrial water storage

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Authors: Adrian-Gabriel Chifu, Sebastien Fournier

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One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.

Keywords: sentiment analysis, difficulty, classification, machine learning

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Authors: Prashansa Singh, Manjot Kaur, Rohit Bajaj

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The proliferation of wireless sensor networks and Internet of Things (IoT) devices in the quickly changing digital landscape has highlighted the urgent need for strong security solutions that can handle these systems’ limited resources. A key solution to this problem is the emergence of ultralightweight security protocols, which provide strong security features while respecting the strict computational, energy, and memory constraints imposed on these kinds of devices. This in-depth analysis explores the field of ultralightweight security protocols, offering a thorough examination of their evolution, salient features, and the particular security issues they resolve. We carefully examine and contrast different protocols, pointing out their advantages and disadvantages as well as the compromises between resource limitations and security resilience. We also study these protocols’ application domains, including the Internet of Things, RFID systems, and wireless sensor networks, to name a few. In addition, the review highlights recent developments and advancements in the field, pointing out new trends and possible avenues for future research. This paper aims to be a useful resource for researchers, practitioners, and developers, guiding the design and implementation of safe, effective, and scalable systems in the Internet of Things era by providing a comprehensive overview of ultralightweight security protocols.

Keywords: wireless sensor network, machine-to-machine, MQTT broker, server, ultralightweight, TCP/IP

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Authors: M. S. Belashov, A. V. Benin, Lin Hong, Sh. Sh. Nazarova, O. B. Sabirova, A. M. Uzdin, Lin Hong

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The features of calculating structures for the action of weak earthquakes are analyzed. Earthquakes with a recurrence of 30 years and 50 years are considered. In the first case, the structure is to operate normally without damage after the earthquake. In the second case, damages are allowed that do not affect the possibility of the structure operation. Three issues are emphasized: setting elastic and damping characteristics of reinforced concrete, formalization of limit states, and combinations of loads. The dependence of damping on the reinforcement coefficient is estimated. When evaluating limit states, in addition to calculations for crack resistance and strength, a human factor, i.e., the possibility of panic among people, was considered. To avoid it, it is proposed to limit a floor-by-floor speed level in certain octave ranges. Proposals have been developed for estimating the coefficients of the combination of various loads with the seismic one. As an example, coefficients of combinations of seismic and ice loads are estimated. It is shown that for strong actions, the combination coefficients for different regions turn out to be close, while for weak actions, they may differ.

Keywords: weak earthquake, frequent earthquake, damage, limit state, reinforcement, crack resistance, strength resistance, a floor-by-floor velocity, combination coefficients

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Authors: Chang Hsueh-Sheng, Chen Tzu-Ling

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Earthquake is an unpredictable natural disaster and intensive earthquakes have caused serious impacts on social-economic system, environmental and social resilience. Conventional ways to mitigate earthquake disaster are to enhance building codes and advance structural engineering measures. However, earthquake-induced ground damage such as liquefaction, land subsidence, landslide happen on places nearby earthquake prone or poor soil condition areas. Therefore, this study uses spatial statistical analysis to explore the spatial pattern of damaged buildings. Afterwards, principle components analysis (PCA) is applied to categorize the similar features in different kinds of clustered patterns. The results show that serious landslide prone area, close to fault, vegetated ground surface and mudslide prone area are common in those highly damaged buildings. In addition, the oldest building might not be directly referred to the most vulnerable one. In fact, it seems that buildings built between 1974 and 1989 become more fragile during the earthquake. The incorporation of both spatial statistical analyses and PCA can provide more accurate information to subsidize retrofit programs to enhance earthquake resistance in particular areas.

Keywords: earthquake disaster, spatial statistic analysis, principle components analysis (pca), clustered patterns

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Hasan Mahmud Reza

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Extensive studies of the human umbilical cord, both basic and translational, over the last three decades have unveiled a plethora of information. The cord lining harbors at least two phenotypically different multipotent stem cells: mesenchymal stem cells (MSCs) and cord lining epithelial stem cells (CLECs). These cells exhibit a mixed genetic profiling of both embryonic and adult stem cells, hence display a broader stem features than cells from other sources. We have observed that umbilical cord-derived cells are immunologically privileged and non-tumorigenic by animal study. These cells are ethically acceptable, thus provides a significant advantage over other stem cells. The high proliferative capacity, viability, differentiation potential, and superior harvest of these cells have made them better candidates in comparison to contemporary adult stem cells. Following 30 replication cycles, these cells have been observed to retain their stemness, with their phenotype and karyotype intact. Transplantation of bioengineered CLEC sheets in limbal stem cell-deficient rabbit eyes resulted in regeneration of clear cornea with phenotypic expression of the normal cornea-specific epithelial cytokeratin markers. The striking features of low immunogenicity protecting self along with co-transplanted allografts from rejection largely define the transplantation potential of umbilical cord-derived stem cells.

Keywords: cord lining epithelial stem cells, mesenchymal stem cell, regenerative medicine, umbilical cord

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

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Authors: Stephen Anim, Emmanuel O. Sarpong, Daniel Apaak

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This study investigates the relationship between morphological predictors and running ability, emphasizing gender-specific variations among male and female athletes in Cape Coast Metropolis (CCM), Ghana. The dynamic interplay between an athlete's physique and their performance capabilities holds particular relevance in the realm of sports science, influencing training methodologies and talent identification processes. The research aims to contribute comprehensive insights into the morphological determinants of running proficiency, with a specific focus on the local athletic community in Cape Coast Metropolis. Utilizing a correlational research design, a thorough analysis of morphological features, encompassing 22 morphological features including body weight, 6 measurements related to body length, 7 body girth, and knee diameter, and 7 skinfold measurements against 50m dash, among male and female athletes, was conducted. The study involved 420 athletes both male (N=210) and female (N=210) aged 16-22 from 10 Senior High Schools (SHS) in the Cape Coast Metropolis, providing a representative sample of the local athletic community. The collected data were statistically analysed using means and standard deviation, and stepwise multiple regression to determine how morphological variables contribute to and predict running proficiency outcomes. The investigation revealed that athletes from Senior High Schools (SHS) in Cape Coast Metropolis (CCM) exhibit well-developed physiques and sufficient fitness levels suitable for overall athletic performance, taking into account gender differences. Moreover, the findings suggested that approximately 77% of running ability could be attributed to morphological factors, leading to diverse predictive models for male and female athletes within SHS in CCM, Ghana. Consequently, these formulated equations hold promise for predicting running ability among young athletes, particularly in the context of SHS environments.

Keywords: body fat, body girth, body length, morphological features, running ability, senior high school

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Authors: Joud Basyouni, Rama Zagzoog, Mashael Al Faleh, Jana Alireza

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AI is changing many fields and speeding up tasks that used to take a long time. It used to take too long to edit photos. However, many AI-powered apps make photo editing, automatic effects, and animations much easier than other manual editing apps with no AI. The mobile app Photoleap edits photos and creates digital art using AI. Editing photos with text prompts is also becoming a standard these days with the help of apps like Photoleap. Now, users can change backgrounds, add animations, turn text into images, and create scenes with AI. This project report discusses the photo editing app's history and popularity. Photoleap resembles Photoshop, Canva, Photos, and Pixlr. The report includes survey questions to assess Photoleap user satisfaction. The report describes Photoleap's features and functions with screenshots. Photoleap uses AI well. Charts and graphs show Photoleap user ratings and comments from the survey. This project found that most Photoleap users liked how well it worked, was made, and was easy to use. People liked changing photos and adding backgrounds. Users can create stunning photo animations. A few users dislike the app's animations, AI art, and photo effects. The project report discusses the app's pros and cons and offers improvements.

Keywords: artificial intelligence, photoleap, images, background, photo editing

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Authors: Huang Bai-Cheng

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When deploying the Oriented Fast and Rotated Binary Robust Independent Elementary Feature (BRIEF) (ORB) extraction algorithm on field-programmable gate arrays (FPGA), the access of global storage for 31×31 pixel patches of the features has become the bottleneck of the system efficiency. Therefore, a feature zone strategy has been proposed. Zones are searched as features are detected. Pixels around the feature zones are extracted from global memory and distributed into patches corresponding to feature coordinates. The proposed FPGA structure is targeted on a Xilinx FPGA development board of Zynq UltraScale+ series, and multiple datasets are tested. Compared with the streaming pixel patch extraction method, the proposed architecture obtains at least two times acceleration consuming extra 3.82% Flip-Flops (FFs) and 7.78% Look-Up Tables (LUTs). Compared with the non-streaming one, the proposed architecture saves 22.3% LUT and 1.82% FF, causing a latency of only 0.2ms and a drop in frame rate for 1. Compared with the related works, the proposed strategy and hardware architecture have the superiority of keeping a balance between FPGA resources and performance.

Keywords: feature extraction, real-time, ORB, FPGA implementation

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Authors: Hossein Arfaeinia, Ahmad Jonidi Jafari, Sina Dobaradaran, Sadegh Niazi, Mojtaba Ehsanifar, Amir Zahedi

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Street dust and surface soil samples were collected from very heavy, heavy, medium and low traffic areas and natural site in Tehran, Iran. These samples were analyzed for some physical–chemical features, total and chemical speciation of selected metals and metalloids (Zn, Al, Sr, Pb, Cu, Cr, Cd, Co, Ni, and V) to study the effect of traffic on their mobility and accumulation in the environment. The pH, electrical conductivity (EC), carbonates and organic carbon (OC) values were similar in soil and dust samples from similar traffic areas. The traffic increases EC contents in dust/soil matrixes but has no effect on concentrations of metals and metalloids in soil samples. Rises in metal and metalloids levels with traffic were found in dust samples. Moreover, the traffic increases the percentage of acid soluble fraction and Fe and Mn oxides associated fractions of Pb and Zn. The mobilization of Cu, Zn, Pb, Cr in dust samples was easier than in soil. The speciation of metals and metalloids except Cd is mainly affected by physicochemical features in soil, although total metals and metalloids affected the speciation in dust samples (except chromium and nickel).

Keywords: street dust, surface soil, traffic, metals, metalloids, chemical speciation

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: D. Scarpanti, M. Brondino, M. Pasini

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Background: the present study explores the role of light and restorativeness on work. According with the Attention Restoration Theory (ART) and a Model of Work Environment, the main idea is that some features of environment, i.e., lighting, influences the direct attention, and so, the performance. Restorativeness refers to the presence/absence level of all the characteristics of physical environment that help to regenerate direct attention. Specifically, lighting can affect level of fascination and attention in one hand; and in other hand promotes several biological functions via pineal gland. Different reviews on this topic show controversial results. In order to bring light on this topic, the hypotheses of this study are that lighting can affect the construct of restorativeness and, in the second time, the restorativeness can affect the performance. Method: the participants are 30 workers of a mechatronic company in the North Italy. Every subject answered to a questionnaire valuing their subjective perceptions of environment in a different way: some objective features of environment, like lighting, temperature and air quality; some subjective perceptions of this environment; finally, the participants answered about their perceived performance. The main attention is on the features of light and his components: visual comfort, general preferences and pleasantness; and the dimensions of the construct of restorativeness; fascination, coherence and being away. The construct of performance per se is conceptualized in three level: individual, team membership and organizational membership; and in three different components: proficiency, adaptability, and proactivity, for a total of 9 subcomponents. Findings: path analysis showed that some characteristics of lighting respectively affected the dimension of fascination; and, as expected, the dimension of fascination affected work performance. Conclusions: The present study is a first pilot step of a wide research. These first results can be summarized with the statement that lighting and restorativeness contribute to explain work performance variability: in details perceptions of visual comfort, satisfaction and pleasantness, and fascination respectively. Results related to fascination are particularly interesting because fascination is conceptualized as the opposite of the construct of direct attention. The main idea is, in order to regenerate attentional capacity, it’s necessary to provide a lacking of attention (fascination). The sample size did not permit to test simultaneously the role of the perceived characteristics of light to see how they differently contribute to predict fascination of the work environment. However, the results highlighted the important role that light could have in predicting restorativeness dimensions and probably with a larger sample we could find larger effects also on work performance. Furthermore, longitudinal data will contribute to better analyze the causal model along time. Applicative implications: the present pilot study highlights the relevant role of lighting and perceived restorativeness in the work environment and the importance to focus attention on light features and the restorative characteristics in the design of work environments.

Keywords: lighting, performance, restorativeness, workplace

Procedia PDF Downloads 156