Search results for: STS benchmark dataset

Authors: Ritika Jain, Rajnish Kumar

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The Department of Public Enterprises had made submissions of Self Evaluation Reports, for the purpose of corporate governance, mandatory for all central government owned enterprises. Despite this, an alarming 40% of the enterprises did not do so. This study examines the impact of external policy tools and internal firm-specific factors on corporate governance of central public sector enterprises (CPSEs). We use a dataset of all manufacturing and non-financial services owned by the central government of India for the year 2010-11. Using probit, ordered logit and Heckman’s sample selection models, the study finds that the probability and quality of corporate governance is positively influenced by the CPSE getting into a Memorandum of Understanding (MoU) with the central government of India, and hence, enjoying more autonomy in terms of day to day operations. Besides these, internal factors, including bigger size and lower debt size contribute significantly to better corporate governance.

Keywords: corporate governance, central public sector enterprises (CPSEs), sample selection, Memorandum of Understanding (MoU), ordered logit, disinvestment

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Po-Chun Wang, Yan-Ru Lai, Sophia I. C. Lin, Alvin W. Y. Su

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The Generative Theory of Tonal Music (GTTM) provides systematic approaches to recognizing local boundaries of music. The rules have been implemented in some automated melody segmentation algorithms. Besides, there are also deep learning methods with GTTM features applied to boundary detection tasks. However, these studies might face constraints such as a lack of or inconsistent label data. The GTTM database is currently the most widely used GTTM database, which includes manually labeled GTTM rules and local boundaries. Even so, we found some problems with these labels. They are sometimes discrepancies with GTTM rules. In addition, since it is labeled at different times by multiple musicians, they are not within the same scope in some cases. Therefore, in this paper, we examine this database with musicians from the aspect of classical music and relabel the scores. The relabeled database - GTTM Database v2.0 - will be released for academic research usage. Despite the experimental and statistical results showing that the relabeled database is more consistent, the improvement in boundary detection is not substantial. It seems that we need more clues than GTTM rules for boundary detection in the future.

Keywords: dataset, GTTM, local boundary, neural network

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Authors: Haoqi Gao, Koichi Ogawara

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Retinal vascular segmentation of color fundus is the basis of ophthalmic computer-aided diagnosis and large-scale disease screening systems. Early screening of fundus diseases has great value for clinical medical diagnosis. The traditional methods depend on the experience of the doctor, which is time-consuming, labor-intensive, and inefficient. Furthermore, medical images are scarce and fraught with legal concerns regarding patient privacy. In this paper, we propose a new Generative Adversarial Network based on CycleGAN for retinal fundus images. This method can generate not only synthetic fundus images but also generate corresponding segmentation masks, which has certain application value and challenge in computer vision and computer graphics. In the results, we evaluate our proposed method from both quantitative and qualitative. For generated segmented images, our method achieves dice coefficient of 0.81 and PR of 0.89 on DRIVE dataset. For generated synthetic fundus images, we use ”Toy Experiment” to verify the state-of-the-art performance of our method.

Keywords: retinal vascular segmentations, generative ad-versarial network, cyclegan, fundus images

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Authors: Annop Jaewisorn

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This study explores the relationship between natural disasters and inequalities -both income and expenditure inequality- at a micro-level of Thailand as the first study of this nature for this country. The analysis uses a unique panel and remote-sensing dataset constructed for the purpose of this research. It contains provincial inequality measures and other economic and social indicators based on the Thailand Household Survey during the period between 1992 and 2019. Meanwhile, the data on natural disasters, which are remote-sensing data, are received from several official geophysical or meteorological databases. Employing a panel fixed effects, the results show that natural disasters significantly reduce household income and expenditure inequality as measured by the Gini index, implying that rich people in Thailand bear a higher cost of natural disasters when compared to poor people. The effect on income inequality is mainly driven by droughts, while the effect on expenditure inequality is mainly driven by flood events. The results are robust across heterogeneity of the samples, lagged effects, outliers, and an alternative inequality measure.

Keywords: inequality, natural disasters, remote-sensing data, Thailand

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Authors: Lilla Dorina Habsz, Marta Rado

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Popularity is affected by a variety of factors in the primary school such as academic achievement and ethnicity. The main goal of our study was to analyse whether acting white exists in Hungarian elementary schools. In other words, we observed whether Roma students penalize those in-group members who obtain the high academic achievement. Furthermore, to show how popularity is influenced by changes in academic achievement in inter-ethnic relations. The empirical basis of our research was the 'competition and negative networks' longitudinal dataset, which was collected by the MTA TK 'Lendület' RECENS research group. This research followed 11 and 12-year old students for a two-year period. The survey was analysed using fixed and random effect models. Overall, we found a positive correlation between grades and popularity, but no evidence for the acting white effect. However, better grades were more positively evaluated within the majority group than within the minority group, which may further increase inequalities.

Keywords: academic achievement, elementary school, ethnicity, popularity

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Leander Van Neste, Kirk Wojno

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The innate immune system reacts to foreign insult in several unique ways, one of which is phagocytosis of perceived threats such as cancer, bacteria, and viruses. The goal of this study was to look for evidence of phagocytosed RNA from tumor cells in circulating monocytes. While all monocytes possess phagocytic capabilities, the non-classical CD14+/FCGR3A+ monocytes and the intermediate CD14++/FCGR3A+ monocytes most actively remove threatening ‘external’ cellular materials. Purified CD14-positive monocyte samples from fourteen patients recently diagnosed with clinically localized prostate cancer (PCa) were investigated by single-cell RNA sequencing using the 10X Genomics protocol followed by paired-end sequencing on Illumina’s NovaSeq. Similarly, samples were processed and used as controls, i.e., one patient underwent biopsy but was found not to harbor prostate cancer (benign), three young, healthy men, and three men previously diagnosed with prostate cancer that recently underwent (curative) radical prostatectomy (post-RP). Sequencing data were mapped using 10X Genomics’ CellRanger software and viable cells were subsequently identified using CellBender, removing technical artifacts such as doublets and non-cellular RNA. Next, data analysis was performed in R, using the Seurat package. Because the main goal was to identify differences between PCa patients and ‘control’ patients, rather than exploring differences between individual subjects, the individual Seurat objects of all 21 patients were merged into one Seurat object per Seurat’s recommendation. Finally, the single-cell dataset was normalized as a whole prior to further analysis. Cell identity was assessed using the SingleR and cell dex packages. The Monaco Immune Data was selected as the reference dataset, consisting of bulk RNA-seq data of sorted human immune cells. The Monaco classification was supplemented with normalized PCa data obtained from The Cancer Genome Atlas (TCGA), which consists of bulk RNA sequencing data from 499 prostate tumor tissues (including 1 metastatic) and 52 (adjacent) normal prostate tissues. SingleR was subsequently run on the combined immune cell and PCa datasets. As expected, the vast majority of cells were labeled as having a monocytic origin (~90%), with the most noticeable difference being the larger number of intermediate monocytes in the PCa patients (13.6% versus 7.1%; p<.001). In men harboring PCa, 0.60% of all purified monocytes were classified as harboring PCa signals when the TCGA data were included. This was 3-fold, 7.5-fold, and 4-fold higher compared to post-RP, benign, and young men, respectively (all p<.001). In addition, with 7.91%, the number of unclassified cells, i.e., cells with pruned labels due to high uncertainty of the assigned label, was also highest in men with PCa, compared to 3.51%, 2.67%, and 5.51% of cells in post-RP, benign, and young men, respectively (all p<.001). It can be postulated that actively phagocytosing cells are hardest to classify due to their dual immune cell and foreign cell nature. Hence, the higher number of unclassified cells and intermediate monocytes in PCa patients might reflect higher phagocytic activity due to tumor burden. This also illustrates that small numbers (~1%) of circulating peripheral blood monocytes that have interacted with tumor cells might still possess detectable phagocytosed tumor RNA.

Keywords: circulating monocytes, phagocytic cells, prostate cancer, tumor immune response

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Authors: Liuyao Chai, Colin Clark

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Analysis of 574 participants in a simulated two-person distributive negotiation revealed that the first price 245 (42.7%) of these participants actually asked/offered for the item under negotiation (a used car) differed from the first price they previously stated they intended to ask/offer during their negotiation. This discrepancy between a negotiator’s intended first asking/offer price and his/her actual first asking/offer price had a significant and economically consequential impact on both the course and the outcomes of the negotiations studied. Participants whose actual first price remained the same as their intended first price tended to secure better negotiation outcomes. Moreover, participants who changed their intended first price tended to obtain relatively lower outcomes regardless of whether their modified first announced price had created a negotiating position that was ‘stronger’ or ‘weaker’ than if they had opened with their intended first price. Subsequent investigation of over twenty negotiation behaviours and pre-negotiation perceptual variables within this dataset indicated that the three types of first price announcers—i.e. intended first asking/offer price ‘weakeners’, ‘maintainers’ and ‘strengtheners’— comprised persons who tended to have significantly different pre-negotiation perceptions and behaved in systematically different ways during their negotiation. Typically, the most negative, outcome-compromising consequences of changing, weakening or strengthening an intended first price occurred at the very beginning of a negotiation when participants exchanged their actual first asking/offer prices.

Keywords: business communication, negotiation, persuasion, intended first asking/offer prices, bargaining

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Authors: Jitendra Gouda, Chander Shekhar

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With the addition of 27 million elderly in India in past census decade from 2001 to 2011, it is imperative to work towards exploring the issues and concerns of this increasingly aged population. In Indian society, the elderly person is assumed to be looked after by the family members, especially by children but with changing economy, society, and lifestyle, this assumption demands examining. This paper is an attempt to explore the living arrangement of the elderly and their perceptions about this in India. The findings are based on the BKPAI dataset of 2011, which was conducted in seven states – Himachal Pradesh, Kerala, Maharashtra, Odisha, Punjab, Tamil Nadu, and West Bengal. The result shows that three fourth of elderly lives with their children. Having son and staying with children is positively associated among elderly. More than 40 percent as compared to 37 percent of elderly feels comfortable living with sons and daughters respectively. Half of elderly across sexes viewed that sons are the best person to live with. The result of discriminant analysis suggest that health status and living arrangement of elderly are the good discriminators to ensure their importance in the family.

Keywords: discriminant analysis, elderly, India, living arrangment

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Authors: Manasse Elusma, Thung-Hong Lin, Chun-yin Lee

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The relationship between hunger and war or conflict remains to be discussed. Do wars or conflicts cause hunger and food scarcity, or is the reverse relationship is true? As the world becomes more peaceful and wealthier, some countries are still suffered from hunger and food shortage. So, eradicating hunger calls for a more comprehensive understanding of the relationship between conflict and hunger. Several studies are carried out to detect the importance of conflict or war on food security. Most of these studies, however, perform only descriptive analysis and largely use food security indicators instead of the global hunger index. Few studies have employed cross-country panel data to explicitly analyze the association between conflict and chronic hunger, including hidden hunger. Herein, this study addresses this issue and the knowledge gap. We combine global datasets to build a new panel dataset including 143 countries from 1989 to 2020. This study examines the effect of conflict on hunger with fixed effect models, and the results show that the increase of conflict frequency deteriorates hunger. Peacebuilding efforts and war prevention initiative are required to eradicate global hunger.

Keywords: armed conflict, food scarcity, hidden hunger, hunger, malnutrition

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Authors: Gizem Ertürk, Nursena Dinç

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The Aegean area has always been an important transit point for migration; however, the establishment of the European Union gave further impetus to the migration phenomenon and increased the significance of the area within this context. The migration waves have been more visible in the area in recent decades, and particularly after the “2015 Migration Crisis,” this issue has been subject to further securitization in the EU. In this conjuncture, the Frontex, which is an agency set up by the EU in 2005 for the purpose of managing and coordinating the border control efforts, has become more functional in the relevant area, but at the same time, have some questionable actions within the context of human rights. This paper problematizes the rationality behind the existence and practices of such a structure and attempts to make a political and legal analysis of the security practices of the European Union against migration within a framework based on the biopolitics approaches of Michel Foucault and Giorgio Agamben. The dataset of this paper, which focuses on the agency in question by taking it as a case, is formed by making use of the existing literature on the EU’s security policies, the relevant official texts of the Union and Frontex reports on migration practices in and around the Aegean Sea.

Keywords: migration, biopolitics, Frontex, security, European union, securitization

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Authors: Sanaa Chafik, Imane Daoudi, Mounim A. El Yacoubi, Hamid El Ouardi

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Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. In this paper, we propose a new hashing algorithm to overcome the storage space problem and improve query time, while keeping a good accuracy as similar to that achieved by the original Euclidean LSH. The Experimental results on a real large-scale dataset show that the proposed approach achieves good performances and consumes less memory than the Euclidean LSH.

Keywords: approximate nearest neighbor search, content based image retrieval (CBIR), curse of dimensionality, locality sensitive hashing, multidimensional indexing, scalability

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

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De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

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Authors: Nada Mohammad Eljeshi

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In the United Arab Emirates (UAE), preschoolers can enroll in kindergarten after completing four years old by August 31 of their admission year. This study aims to better understand how Sharjah-based parents’ engagement with preschoolers contributes to their phonological awareness, literacy development, and print knowledge before their kindergarten admission considering cognitive development is addressed in the UAE national child care standards. More specifically, it will discuss the importance of cognitive development activities to preschoolers, the rationale behind defining the admission age to kindergarten and compare and benchmark the policy to other countries. To achieve this study's objectives, an online survey was conducted and distributed. Respondents were asked 13 dichotomous questions related to activities that promote the preschooler’s linguistics literacy and cognitive development. The results suggested parents’ emphasis on phonological awareness, followed by developing their print knowledge. However, the majority of the surveyed parents did not engage in literacy development with their preschoolers. On this basis, it is clear parents’ awareness should occur by introducing various activities such as book reading, that there is a need to introduce and encourage parents to various activities such as reading a printed book and drawings to keep up with their children's cognitive development. The survey results suggested an emphasis on phonological awareness, followed by developing their print knowledge. However, the majority of the surveyed parents did not engage in literacy development with their preschoolers. On this basis, parental awareness of the importance of preschoolers' cognitive development should be developed and engage the parents in understanding their preschooler’s cognitive development before entering kindergarten.

Keywords: preschoolers, cognitive development, parental engagement, Sharjah-based case study

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Authors: M. Macchiaroli, L. Dolores, V. Pellecchia

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With the recent Resolution n. 580/2019/R/idr, the Italian Regulatory Authority for Energy, Networks, and Environment (ARERA) for the Urban Water Management has introduced, for water managements characterized by persistent critical issues regarding the planning and organization of the service and the implementation of the necessary interventions for the improvement of infrastructures and management quality, a new mechanism for determining tariffs: the regulatory scheme of Convergence. The aim of this regulatory scheme is the overcoming of the Water Service Divided in order to improve the stability of the local institutional structures, technical quality, contractual quality, as well as in order to guarantee transparency elements for Users of the Service. Convergence scheme presupposes the identification of the cost items to be considered in the tariff in parametric terms, distinguishing three possible cases according to the type of historical data available to the Manager. The study, in particular, focuses on operations that have neither data on tariff revenues nor data on operating costs. In this case, the Manager's Constraint on Revenues (VRG) is estimated on the basis of a reference benchmark and becomes the starting point for defining the structure of the tariff classes, in compliance with the TICSI provisions (Integrated Text for tariff classes, ARERA's Resolution n. 665/2017/R/idr). The proposed model implements the recent studies on optimization models for the definition of tariff classes in compliance with the constraints dictated by TICSI in the application of the Convergence mechanism, proposing itself as a support tool for the Managers and the local water regulatory Authority in the decision-making process.

Keywords: decision-making process, economic evaluation of projects, optimizing tools, urban water management, water tariff

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Authors: Marcio Leal, Marta Villamil

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Computacional recognition of sign languages aims to allow a greater social and digital inclusion of deaf people through interpretation of their language by computer. This article presents a model of recognition of two of global parameters from sign languages; hand configurations and hand movements. Hand motion is captured through an infrared technology and its joints are built into a virtual three-dimensional space. A Multilayer Perceptron Neural Network (MLP) was used to classify hand configurations and Dynamic Time Warping (DWT) recognizes hand motion. Beyond of the method of sign recognition, we provide a dataset of hand configurations and motion capture built with help of fluent professionals in sign languages. Despite this technology can be used to translate any sign from any signs dictionary, Brazilian Sign Language (Libras) was used as case study. Finally, the model presented in this paper achieved a recognition rate of 80.4%.

Keywords: artificial neural network, computer vision, dynamic time warping, infrared, sign language recognition

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Eusebio Odiari, Mark Birkin, Susan Grant-Muller, Nicolas Malleson

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Big data typically refers to consumer datasets revealing some detailed heterogeneity in human behavior, which if harnessed appropriately, could potentially revolutionize our understanding of the collective phenomena of the physical world. Inadvertent missing values skew these datasets and compromise the validity of the thesis. Here we discuss a conceptually consistent strategy for identifying other relevant datasets to combine with available big data, to plug the gaps and to create a rich requisite comprehensive dataset for subsequent analysis. Specifically, emphasis is on how these methodologies can for the first time enable the construction of more detailed pictures of passenger demand and drivers of mobility on the railways. These methodologies can predict the influence of changes within the network (like a change in time-table or impact of a new station), explain local phenomena outside the network (like rail-heading) and the other impacts of urban morphology. Our analysis also reveals that our new imputation data model provides for more equitable revenue sharing amongst network operators who manage different parts of the integrated UK railways.

Keywords: big-data, micro-simulation, mobility, ticketing-data, commuters, transport, synthetic, population

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Authors: Nurkhairul Bariyah Baharun, Afzan Adam, Reena Rahayu Md Zin

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Tumor microenvironment (TME) in breast cancer is mainly composed of cancer cells, immune cells, and stromal cells. The interaction between cancer cells and their microenvironment plays an important role in tumor development, progression, and treatment response. The TME in breast cancer includes tumor-infiltrating lymphocytes (TILs) that are implicated in killing tumor cells. TILs can be found in tumor stroma (sTILs) and within the tumor (iTILs). TILs in triple negative breast cancer (TNBC) have been demonstrated to have prognostic and potentially predictive value. The international Immune-Oncology Biomarker Working Group (TIL-WG) had developed a guideline focus on the assessment of sTILs using hematoxylin and eosin (H&E)-stained slides. According to the guideline, the pathologists use “eye balling” method on the H&E stained- slide for sTILs assessment. This method has low precision, poor interobserver reproducibility, and is time-consuming for a comprehensive evaluation, besides only counted sTILs in their assessment. The TIL-WG has therefore recommended that any algorithm for computational assessment of TILs utilizing the guidelines provided to overcome the limitations of manual assessment, thus providing highly accurate and reliable TILs detection and classification for reproducible and quantitative measurement. This study is carried out to develop a TNBC digital whole slide image (WSI) dataset from H&E-stained slides and IHC (CD4+ and CD8+) stained slides. TNBC cases were retrieved from the database of the Department of Pathology, Hospital Canselor Tuanku Muhriz (HCTM). TNBC cases diagnosed between the year 2010 and 2021 with no history of other cancer and available block tissue were included in the study (n=58). Tissue blocks were sectioned approximately 4 µm for H&E and IHC stain. The H&E staining was performed according to a well-established protocol. Indirect IHC stain was also performed on the tissue sections using protocol from Diagnostic BioSystems PolyVue™ Plus Kit, USA. The slides were stained with rabbit monoclonal, CD8 antibody (SP16) and Rabbit monoclonal, CD4 antibody (EP204). The selected and quality-checked slides were then scanned using a high-resolution whole slide scanner (Pannoramic DESK II DW- slide scanner) to digitalize the tissue image with a pixel resolution of 20x magnification. A manual TILs (sTILs and iTILs) assessment was then carried out by the appointed pathologist (2 pathologists) for manual TILs scoring from the digital WSIs following the guideline developed by TIL-WG 2014, and the result displayed as the percentage of sTILs and iTILs per mm² stromal and tumour area on the tissue. Following this, we aimed to develop an automated digital image scoring framework that incorporates key elements of manual guidelines (including both sTILs and iTILs) using manually annotated data for robust and objective quantification of TILs in TNBC. From the study, we have developed a digital dataset of TNBC H&E and IHC (CD4+ and CD8+) stained slides. We hope that an automated based scoring method can provide quantitative and interpretable TILs scoring, which correlates with the manual pathologist-derived sTILs and iTILs scoring and thus has potential prognostic implications.

Keywords: automated quantification, digital pathology, triple negative breast cancer, tumour infiltrating lymphocytes

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Authors: Jamal S. M. Sabir, Edward C. Theriot, Schonna R. Manning, Abdulrahman L. Al-Malki, Mohammad, Mumdooh J. Sabir, Dwight K. Romanovicz, Nahid H. Hajrah, Robert K. Jansen, Matt P. Ashworth

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The diatom Phaeodactylum tricornutum has been used as a model for cell biologists and ecologists for over a century. We have incorporated several new raphid pennates into a three-gene phylogenetic dataset (SSU, rbcL, psbC), and recover Gomphonemopsis sp. as sister to P. tricornutum with 100% BS support. This is the first time a close relative has been identified for P. tricornutum with robust statistical support. We test and reject a succession of hypotheses for other relatives. Our molecular data are statistically significantly incongruent with placement of either or both species among the Cymbellales, an order of diatoms with which both have been associated. We believe that further resolution of the phylogenetic position of P. tricornutum will rely more on increased taxon sampling than increased genetic sampling. Gomphonemopsis is a benthic diatom, and its phylogenetic relationship with P. tricornutum is congruent with the hypothesis that P. tricornutum is a benthic diatom with specific adaptations that lead to active recruitment into the plankton. We hypothesize that other benthic diatoms are likely to have similar adaptations and are not merely passively recruited into the plankton.

Keywords: benthic, diatoms; ecology, Phaeodactylum tricornutum, phylogeny, tychoplankton

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

Abstract:

Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: decision tree, feature selection, intrusion detection system, support vector machine

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Authors: Muthukumarasamy Govindarajan

Abstract:

Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Yunwei Zhang, Qiaochu Tang, Yao Zhang, Jiabin Wang, Ulrich Stimming, Alpha Lee

Abstract:

Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS) -- a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis -- with Gaussian process machine learning. We collect over 20,000 EIS spectra of commercial Li-ion batteries at different states of health, states of charge and temperatures -- the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems.

Keywords: battery degradation, machine learning method, electrochemical impedance spectroscopy, battery diagnosis

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Authors: Adarsh Shroff

Abstract:

Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

Procedia PDF Downloads 354

Authors: S. M. Anowarul Haque, Md. Asiful Islam

Abstract:

Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

Abstract:

As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

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Authors: Sangbok Lee

Abstract:

There has been much effort on process improvement for outpatient clinics to provide quality and acute care to patients. One of the efforts is no-show analysis or prediction. This work analyzes patient no-shows along with patient health conditions. The health conditions refer to clinical symptoms that each patient has, out of the followings; hyperlipidemia, diabetes, metastatic solid tumor, dementia, chronic obstructive pulmonary disease, hypertension, coronary artery disease, myocardial infraction, congestive heart failure, atrial fibrillation, stroke, drug dependence abuse, schizophrenia, major depression, and pain. A dataset from a regional hospital is used to find the relationship between the number of the symptoms and no-show probabilities. Additional analysis reveals how each symptom or combination of symptoms affects no-shows. In the above analyses, cross-classification of patients by age and gender is carried out. The findings from the analysis will be used to take extra care to patients with particular health conditions. They will be forced to visit clinics by being informed about their health conditions and possible consequences more clearly. Moreover, this work will be used in the preparation of making institutional guidelines for patient reminder systems.

Keywords: healthcare system, no show analysis, process improvment, statistical data analysis

Procedia PDF Downloads 233