Search results for: text classification

Authors: Michal Krecichwost, Zauzanna Miodonska, Pawel Badura

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The diagnosis of sigmatism is mostly based on the observation of articulatory organs. It is, however, not always possible to precisely observe the vocal apparatus, in particular in the oral cavity of the patient. Speech processing can allow to objectify the therapy and simplify the verification of its progress. In the described study the methodology for classification of incorrectly pronounced phoneme [s] is proposed. The recordings come from adults. They were registered with the speech recorder at the sampling rate of 44.1 kHz and the resolution of 16 bit. The database of pathological and normative speech has been collected for the study including reference assessments provided by the speech therapy experts. Ten adult subjects were asked to simulate a certain type of stigmatism under the speech therapy expert supervision. In the recordings, the analyzed phone [s] was surrounded by vowels, viz: ASA, ESE, ISI, SPA, USU, YSY. Thirteen MFCC (mel-frequency cepstral coefficients) and RMS (root mean square) values are calculated within each frame being a part of the analyzed phoneme. Additionally, 3 fricative formants along with corresponding amplitudes are determined for the entire segment. In order to aggregate the information within the segment, the average value of each MFCC coefficient is calculated. All features of other types are aggregated by means of their 75th percentile. The proposed method of features aggregation reduces the size of the feature vector used in the classification. Binary SVM (support vector machine) classifier is employed at the phoneme recognition stage. The first group consists of pathological phones, while the other of the normative ones. The proposed feature vector yields classification sensitivity and specificity measures above 90% level in case of individual logo phones. The employment of a fricative formants-based information improves the sole-MFCC classification results average of 5 percentage points. The study shows that the employment of specific parameters for the selected phones improves the efficiency of pathology detection referred to the traditional methods of speech signal parameterization.

Keywords: computer-aided pronunciation evaluation, sibilants, sigmatism diagnosis, speech processing

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Authors: Andrzej Kesiak

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Contemporary theology is often accused of answering questions that nobody is asking, and of employing hermetic language that has lost its communication capacity. There is also a question that theology is asking itself: how theological discourse can still be influential on other disciplines and, how to overcome the separation of theology and belief. Undoubtedly, in the wider spectrum, the theological discourse has been and will be needed. The difficulty is how to find the right model of it, the model that would help theology to enter in dialogue with culture, art, science, and politics. Presumably, there is no only one such model, theology constantly needs to seek such models, and this is probably a never-ending journey; in other words, theology should adopt a profile of ‘a restless being’ if it wants to remain influential. Music, on the other hand, has always been very close to theology; in fact, a huge part of classical music is either sacred or religious. Many composers sought inspiration in religion, liturgy, religious painting and sacred texts. This paper will argue that despite all that it seems that a proper and factual dialogue is still in a starting phase. Such a thing as a reciprocal relationship between theology and music definitely exists, but it has not yet been theoretically developed enough. Correlation between musical and theological disciplines constitutes a very broad and complex discourse. Therefore this study would rather narrow the subject and put it in a specific context: Theology and Music in the XXI. Century. This paper is a text-based study; therefore it will be based on textual-analysis with elements of the text hermeneutics.

Keywords: music, theology, reciprocal relationship between theology and music, XXI Century

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Authors: Paweł Konieczka, Lech Raczyński, Wojciech Wiślicki, Oleksandr Fedoruk, Konrad Klimaszewski, Przemysław Kopka, Wojciech Krzemień, Roman Shopa, Jakub Baran, Aurélien Coussat, Neha Chug, Catalina Curceanu, Eryk Czerwiński, Meysam Dadgar, Kamil Dulski, Aleksander Gajos, Beatrix C. Hiesmayr, Krzysztof Kacprzak, łukasz Kapłon, Grzegorz Korcyl, Tomasz Kozik, Deepak Kumar, Szymon Niedźwiecki, Dominik Panek, Szymon Parzych, Elena Pérez Del Río, Sushil Sharma, Shivani Shivani, Magdalena Skurzok, Ewa łucja Stępień, Faranak Tayefi, Paweł Moskal

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This paper develops the transformation of non-image data into 2-dimensional matrices, as a preparation stage for classification based on convolutional neural networks (CNNs). In positron emission tomography (PET) studies, CNN may be applied directly to the reconstructed distribution of radioactive tracers injected into the patient's body, as a pattern recognition tool. Nonetheless, much PET data still exists in non-image format and this fact opens a question on whether they can be used for training CNN. In this contribution, the main focus of this paper is the problem of processing vectors with a small number of features in comparison to the number of pixels in the output images. The proposed methodology was applied to the classification of PET coincidence events.

Keywords: convolutional neural network, kernel principal component analysis, medical imaging, positron emission tomography

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Authors: María José Núñez-Ruiz, Luis Álvarez-González, Cristian Olivares-Rodriguez, Benjamin Lazo-Letelier

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This paper begins with some concept of formative assessment and the relationship with learning objective: contents objectives, processes objectives, and metacognitive objectives. Two methodologies are describes Evidence-Based teaching and Question Drive Instruction. To do formative assessments in larges classes a Classroom Response System (CRS) is needed. But most of CRS use only Multiple Choice Questions (MCQ), True/False question, or text entry; however, this is insufficient to formative assessment. To do that a new CRS, call FAMA was developed. FAMA support six types of questions: Choice, Order, Inline choice, Text entry, Associated, and Slider. An experiment participated in 149 students from four engineering careers. For results, Kendall's Range Correlation Analysis and descriptive analysis was done. In conclusion, there is a strong relation between contents question, process questions (ask in formative assessment without a score) and metacognitive questions, asked in summative assessment. As future work, the lecturer can do personalized teaching, because knows the behavior of all students in each formative assessment

Keywords: Python language, formative assessment, classroom response systems, evidence-Based teaching, question drive instruction

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Authors: Saima Murtaza

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Ethics and aesthetics have invariably remained the two closely integrated artistic appurtenances for the production of any work of art. These artistic devices configure themselves into a complex synthesis in our contemporary literature. The labyrinthine integration of ethics and aesthetics, operating in the lives of human characters, to the extent of transcending all limits has resulted in an artistic puzzle for the readers. Art, no doubt, is an extrinsic expression of the intrinsic life of man. The use of aesthetics in literature pertaining to human existence; aesthetic solipsism, has resulted in the artistic objectification of these characters. The practice of the like aestheticism deprives the characters of their souls, rendering them as mere objects of aesthetic gaze at the hands of their artists-creators. Artists orchestrate their lives founding it on a plot which deviates from normal social and ethical standards. Their perverse attitude can be seen in dealing with characters, their feelings and the incidents of their lives. Morality is made to appear not as a religious construct but as an individual’s private affair. Furthermore, the idea of beauty incarnated, in other words hedonistic aesthetic does not placate a true aesthete. Ethics and aesthetics are the two most recurring motifs of our contemporary literature, especially of Nabokov’s world. The purpose of this study is to peruse these aforementioned motifs in Nabokov’s most enigmatic novel Lolita, a story of pedophilia, which is in fact reflective of our complex individual psychic and societal patterns. The narrative subverts all the traditional and hitherto known notions of aesthetics and ethics. When applied to literature, aesthetic does not simply mean ‘beautiful’ in the text. It refers to an intricate relationship between feelings and perception and also incorporates within its range wide-ranging emotional reactions to text. The term aesthetics in literature is connected with the readers whose critical responses to the text determine the merit of any work to be really a piece of art. Aestheticism is the child of ethics. Morality sets the grounds for the production of any work and the idea of aesthetics gives it transcendence.

Keywords: ethics, aesthetics and hedonistic aesthetic, nymphet syndrome, pedophilia

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Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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Authors: Ghada Ahmed

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Feminist translation is one of the strategies adopted in the field of translation studies when a gendered content is being rendered from one language to another, and this strategy has been examined in previous studies on written texts. This research, however, addresses the practice of feminist translation in audiovisual texts that are concerned with the screen, dialogue, image and visual aspects. In this thesis, the objectives are studying feminist translation and its adaptation in subtitling from Arabic into English. It addresses the connections between gender and translation as one domain and feminist translation practices with particular consideration of feminist translation strategies in English subtitles. It examines the visibility of the translator throughout the process, assuming that feminist translation is a product directed by the translator’s feminist position, culture, and ideology as a means of helping unshadow women. It also discusses how subtitling constraints impact feminist translation and how the image that has a narrative value can be integrated into the content of the English subtitles. The reasons for conducting this research project are to study language sexism in English and look into Arabic into English gendered content, taking into consideration the Arabic cultural concepts that may lose their connotations when they are translated into English. This research is also analysing the image in an audiovisual text and its contribution to the written dialogue in subtitling. Thus, this research attempts to answer the following questions: To what extent is there a form of affinity between a gendered content and translation? Is feminist translation an act of merely working on a feminist text or feminising the language of any text, by incorporating the translator’s ideology? How can feminist translation practices be applied in an audiovisual text? How likely is it to adapt feminist translation looking into visual components as well as subtitling constraints? Moreover, the paper searches into the fields of gender and translation; feminist translation, language sexism, media studies, and the gap in the literature related to feminist translation practice in visual texts. For my case study, the "Speed Sisters" film has been chosen so as to analyze its English subtitles for my research. The film is a documentary that was produced in 2015 and directed by Amber Fares. It is about five Palestinian women who try to break the stereotypes about women, and have taken their passion about car-racing forward to be the first all-women car-racing driving team in the Middle East. It tackles the issue of gender in both content and language and this is reflected in the translation. As the research topic is semiotic-channelled, the choice for the theoretical approaches varies and combines between translation studies, audiovisual translation, gender studies, and media studies. Each of which will contribute to understanding a specific field of the research and the results will eventually be integrated to achieve the intended objectives in a way that demonstrates rendering a gendered content in one of the audiovisual translation modes from a language into another.

Keywords: audiovisual translation, feminist translation, films gendered content, subtitling conventions and constraints

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Authors: Leesa Mohanty, Ashok Banerjee

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Better audit quality signifies the financial statements of the auditee firm reflect true and fair view of their actual state of affairs, which reduces information asymmetry between management and shareholders, as a result, helps protect interests of shareholders. This study examines the impact of joint audit on audit quality. It is motivated by the ongoing debate where The Institute of Chartered Accountants of India (ICAI), the regulatory body governing auditors, has advocated the finance ministry and the Reserve Bank of India (RBI) for the mandatory use of joint audit in private banks to enhance the quality of audit. Earlier, the Government of India had rejected the plea by ICAI for mandatory joint audits in large companies stating it is not a viable option for promoting domestic firms. We introduce a new measure of audit quality. Drawing from the domain of text analytics, we use relevant phrases in audit reports to gauge audit quality and demonstrate that joint audit improves audit quality. We also, for robustness, use prevalent proxy for audit quality (Big N Auditor, ratio of audit fees to total fees) and find negative effect of joint audit on audit quality. We, therefore highlight that different proxy for audit quality show opposite effect of joint audit.

Keywords: audit fees, audit quality, Big N. Auditor, joint audit

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Authors: Ramon Santana

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The use of biometric identifiers in the field of information security, access control to resources, authentication in ATMs and banking among others, are of great concern because of the safety of biometric data. In the general architecture of a biometric system have been detected eight vulnerabilities, six of them allow obtaining minutiae template in plain text. The main consequence of obtaining minutia templates is the loss of biometric identifier for life. To mitigate these vulnerabilities several models to protect minutiae templates have been proposed. Several vulnerabilities in the cryptographic security of these models allow to obtain biometric data in plain text. In order to increase the cryptographic security and ease of reversibility, a minutiae templates protection model is proposed. The model aims to make the cryptographic protection and facilitate the reversibility of data using two levels of security. The first level of security is the data transformation level. In this level generates invariant data to rotation and translation, further transformation is irreversible. The second level of security is the evaluation level, where the encryption key is generated and data is evaluated using a defined evaluation function. The model is aimed at mitigating known vulnerabilities of the proposed models, basing its security on the impossibility of the polynomial reconstruction.

Keywords: fingerprint, template protection, bio-cryptography, minutiae protection

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Authors: Mary Cruz

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Founded from 1496 to 1502, Santo Domingo is the oldest European settlement in the Americas, inhabited without any discontinuity and was the first seat of Spanish power in the new world. Capital of the country since 1932.In this text, we discover Santo Domingo as an international tourist center, Urban Structure, Eco-tourism, Contamination and other issues related to tourism and development of this city. Founded from 1496 to 1502, Santo Domingo is the oldest European settlement in the Americas, inhabited without any discontinuity and was the first seat of Spanish power in the new world. Capital of the country since 1932. Encouraged by the United Nations and the World Bank, many Caribbean governments have encouraged tourism from the 1950s to boost their Third World economies. In this text, we discover Santo Domingo as an international tourist center, Urban Structure, Eco-tourism, Contamination and challenges of the first tourist destination in the Caribbean.

Keywords: eco-tourism, urban structure, contamination, development

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Authors: Devendra Kr Tayal, Vrinda Gupta, Aastha Bansal, Khushi Singh, Sristi Sharma, Hunny Gaur

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In today's world, suicide is a serious problem. In order to save lives, early suicide attempt detection and prevention should be addressed. A good number of at-risk people utilize social media platforms to talk about their issues or find knowledge on related chores. Twitter and Reddit are two of the most common platforms that are used for expressing oneself. Extensive research has already been done in this field. Through supervised classification techniques like Nave Bayes, Bernoulli Nave Bayes, and Multiple Layer Perceptron on a Reddit dataset, we demonstrate the early recognition of suicidal ideation. We also performed comparative analysis on these approaches and used accuracy, recall score, F1 score, and precision score for analysis.

Keywords: machine learning, suicide ideation detection, supervised classification, natural language processing

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Ali Akbar

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The importance of advancing women’s rights is closely intertwined with the development of civil society and the institutionalization of democracy in Middle Eastern countries. There is indeed an intimate relationship between the process of democratization and promoting gender equality, since democracy necessitates equality between men and women. In order to advance the issue of gender equality, what is required is a solid theoretical framework which has its roots in the reexamination of pre-modern interpretation of certain Qurʾānic passages that seem to have given men more rights than it gives women. This paper suggests that those Muslim scholars who adopt a contextualist approach to the Qurʾānic text and its interpretation provide a solid theoretical background for improving women’s rights. Indeed, the aim of the paper is to discuss how the contextualist approach to the Qurʾānic text and its interpretation given by a number of prominent scholars is capable of promoting the issue of gender equality. The paper concludes that since (1) much of the gender inequality found in the primary sources of Islam as well as pre-modern Muslim writings is rooted in the natural cultural norms and standards of early Islamic societies and (2) since the context of today’s world is so different from that of the pre-modern era, the proposed models provide a solid theoretical framework for promoting women’s rights and gender equality.

Keywords: contextualism, gender equality, Islam, the rights of women

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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: Aliya Grig, Konstantin Sokolov, Igor Shatalin

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The report describes the methodology for collecting data and building an ML model for determining the personality psychotype using profiling and personality traits methods based on several short messages of a user communicating on an arbitrary topic with a chitchat bot. In the course of the experiments, the minimum amount of text was revealed to confidently determine aspects of personality. Model accuracy - 85%. Users' language of communication is English. AI for a personalized communication with a user based on his mood, personality, and current emotional state. Features investigated during the research: personalized communication; providing empathy; adaptation to a user; predictive analytics. In the report, we describe the processes that captures both structured and unstructured data pertaining to a user in large quantities and diverse forms. This data is then effectively processed through ML tools to construct a knowledge graph and draw inferences regarding users of text messages in a comprehensive manner. Specifically, the system analyzes users' behavioral patterns and predicts future scenarios based on this analysis. As a result of the experiments, we provide for further research on training AI models to be empathetic, creating personalized communication for a user

Keywords: AI, empathetic, chatbot, AI models

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Authors: Shubhakar Gupta, Dhruv Prakash, Apoorv Mehta

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In this project, we design a modular robot capable of using and switching between multiple methods of propulsion and classifying terrain, based on an Inertial Measurement Unit (IMU) input. We wanted to make a robot that is not only intelligent in its functioning but also versatile in its physical design. The advantage of a modular robot is that it can be designed to hold several movement-apparatuses, such as wheels, legs for a hexapod or a quadpod setup, propellers for underwater locomotion, and any other solution that may be needed. The robot takes roughness input from a gyroscope and an accelerometer in the IMU, and based on the terrain classification from an artificial neural network; it decides which method of propulsion would best optimize its movement. This provides the bot with adaptability over a set of terrains, which means it can optimize its locomotion on a terrain based on its roughness. A feature like this would be a great asset to have in autonomous exploration or research drones.

Keywords: modular robotics, terrain detection, terrain classification, neural network

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Authors: Victoria Legkikh

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To create a new Orthodox service of Jerusalem rite and to make it possible for a performance, one had to use several types of books. These are menaions and triodion, cleargy service book, stichirarion and typikon. These books keep a part of the information about the service, which a medieval copyist had to put together like a puzzle. But an abundance of necessary books and their variety created a lot of problems in copying services. The main problem was the difference of text in notated and not notated manuscripts (they were corrected at a different time) and lack of information in typikon, which provided only a type of hymns and their mode. After all, a copyist could have both corrected and not corrected manuscripts which also provided a different type of service. It brings us to the situation when we hardly have a couple of manuscripts containing the same service, and it is difficult to understand which changes were made voluntarily and which ones were provided by different types of available manuscripts. A recent paper proposes an analysis of every type of liturgical book and a way of using them in copying and correcting a service so we can divide voluntary changes and changes due to various types of books. The paper also proposes an index showing the “material” life of hymns in different types of manuscripts and the changes of its version and place in the same type of manuscript. This type of index can help in reconstructing the way of creation/copying service and can be useful for publication of the services providing necessary information of every hymn in every used manuscript.

Keywords: orthodox church music, creation, manuscripts, liturgical books

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

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Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall.

Keywords: sarcasm recognition, tone-word spotting, natural language processing, pitch analyzer

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Authors: Ekaterina Lukianchenko

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The present article addresses the nuances of teaching English vocabulary to Russian-speaking students. The experiment involving 39 participants aged 17 to 21 proves that the key to understanding spoken coinages is not only the knowledge of their constituents, but rather the understanding of the context and co-text. The volunteers who took part knew the constituents, but did not know the meaning of the words. The assumption of the authors consists in the fact that the structure of the concept has a direct relation with the form of the particular vocabulary unit, but its form is secondary to its meaning, if the word is a spoken coinage, which is partly proved by the fact that in modern slang words have multiple meanings, as well as one notion can have various embodiments that have virtually nothing in common. The choice of vocabulary items that youngsters use is not exactly arbitrary, but, even if complex nominals are taken into consideration, whose meaning seems clear, as it looks like a sum of their constituents’ meanings, they are still impossible to understand without any context or co-text, as a lot of them are idiomatic, non-transparent. It is further explained what methods might be effective in teaching students how to deal with new words they encounter in real-life situations and how student’s knowledge of vocabulary might be enhanced.

Keywords: spoken language, cognitive linguistics, complex nominals, nominals with the incorporated object, concept, EFL, communicative language teaching

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Authors: Suraj Gururaj, Sumantha Udupa U.

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Browsing the Web has emerged as the de facto activity performed on the Internet. Although browsing gets tracked, the manageability aspect of Web browsing history is very poor. In this paper, we have a workable solution implemented by using machine learning and natural language processing techniques for efficient manageability of user’s browsing history. The significance of adding such a capability to a Web browser is that it ensures efficient and quick information retrieval from browsing history, which currently is very challenging. Our solution guarantees that any important websites visited in the past can be easily accessible because of the intelligent and automatic classification. In a nutshell, our solution-based paper provides an implementation as a browser extension by intelligently classifying the browsing history into most relevant category automatically without any user’s intervention. This guarantees no information is lost and increases productivity by saving time spent revisiting websites that were of much importance.

Keywords: adhoc retrieval, Chrome extension, supervised learning, tile, Web personalization

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Authors: Shreyansh Jain Jeetmal, Shobith P. Chadaga, Shreyas H. Srinivas

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Internet of Things (IoT) is seen as the next major step in the ongoing revolution in the Information Age. It is predicted that in the near future billions of embedded devices will be communicating with each other to perform a plethora of tasks with or without human intervention. One of the major ongoing hotbed of research activity in IoT is Human Computer Interaction (HCI). HCI is used to facilitate communication between an intelligent system and a user. An intelligent system typically comprises of a system consisting of various sensors, actuators and embedded controllers which communicate with each other to monitor data collected from the environment. Communication by the user to the system is typically done using voice. One of the major ongoing applications of HCI is in home automation as a personal assistant. The prime objective of our project is to implement a use case of HCI for home automation. Our system is designed to detect and recognize the users and personalize the appliances in the house according to their individual preferences. Our HCI system is also capable of speaking with the user when certain commands are spoken such as searching on the web for information and controlling appliances. Our system can also monitor the environment in the house such as air quality and gas leakages for added safety.

Keywords: human computer interaction, internet of things, computer vision, sensor networks, speech to text, text to speech, android

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Authors: Irfan Mohiuddin, Ahmad Al Mogren

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In recent years, cloud computing has earned substantial attention in the Oil and Gas Industry and provides services in all the phases of the industry lifecycle. Oil and gas supply infrastructure, in particular, is more vulnerable to accidental, natural and intentional threats because of its widespread distribution. Numerous surveys have been conducted on cloud security and privacy. However, to the best of our knowledge, hardly any survey is carried out that reviews cyber security in all phases with a cloud computing perspective. Moreover, a distinctive classification is performed for all the cloud-based cyber security measures based on the cloud component in use. The classification approach will enable researchers to identify the required technique used to enhance the security in specific cloud components. Also, the limitation of each component will allow the researchers to design optimal algorithms. Lastly, future directions are given to point out the imminent challenges that can pave the way for researchers to further enhance the resilience to cyber security threats in the oil and gas industry.

Keywords: cyber security, cloud computing, safety and security, oil and gas industry, security threats, oil and gas pipelines

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Authors: Xiang Shijie, Zhou Dong, Tian Dan

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Speaker recognition is a crucial task in the field of speech processing, aimed at identifying individuals based on their vocal characteristics. However, existing speaker recognition methods face numerous challenges. Traditional methods primarily rely on audio signals, which often suffer from limitations in noisy environments, variations in speaking style, and insufficient sample sizes. Additionally, relying solely on audio features can sometimes fail to capture the unique identity of the speaker comprehensively, impacting recognition accuracy. To address these issues, we propose a multi-modal network architecture that simultaneously processes both audio and text signals. By gradually integrating audio and text features, we leverage the strengths of both modalities to enhance the robustness and accuracy of speaker recognition. Our experiments demonstrate significant improvements with this multi-modal approach, particularly in complex environments, where recognition performance has been notably enhanced. Our research not only highlights the limitations of current speaker recognition methods but also showcases the effectiveness of multi-modal fusion techniques in overcoming these limitations, providing valuable insights for future research.

Keywords: feature fusion, memory network, multimodal input, speaker recognition

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: Xiaoping Su, Gabriel G. Malouf

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Introduction: Breast cancer is a heterogeneous disease that can be classified in 4 subgroups using transcriptional profiling. The role of lncRNA expression in human breast cancer biology, prognosis, and molecular classification remains unknown. Methods and results: Using an integrative comprehensive analysis of lncRNA, mRNA and DNA methylation in 900 breast cancer patients from The Cancer Genome Atlas (TCGA) project, we unraveled the molecular portraits of 1,700 expressed lncRNA. Some of those lncRNAs (i.e, HOTAIR) are previously reported and others are novel (i.e, HOTAIRM1, MAPT-AS1). The lncRNA classification correlated well with the PAM50 classification for basal-like, Her-2 enriched and luminal B subgroups, in contrast to the luminal A subgroup which behaved differently. Importantly, estrogen receptor (ESR1) expression was associated with distinct lncRNA networks in lncRNA clusters III and IV. Gene set enrichment analysis for cis- and trans-acting lncRNA showed enrichment for breast cancer signatures driven by breast cancer master regulators. Almost two third of those lncRNA were marked by enhancer chromatin modifications (i.e., H3K27ac), suggesting that lncRNA expression may result in increased activity of neighboring genes. Differential analysis of gene expression profiling data showed that lncRNA HOTAIRM1 was significantly down-regulated in basal-like subtype, and DNA methylation profiling data showed that lncRNA HOTAIRM1 was highly methylated in basal-like subtype. Thus, our integrative analysis of gene expression and DNA methylation strongly suggested that lncRNA HOTAIRM1 should be a tumor suppressor in basal-like subtype. Conclusion and significance: Our study depicts the first lncRNA molecular portrait of breast cancer and shows that lncRNA HOTAIRM1 might be a novel tumor suppressor.

Keywords: lncRNA profiling, breast cancer, HOTAIRM1, tumor suppressor

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Authors: Aditya Jayadas

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During a pandemic like the one we are in with COVID-19, older adults, especially those who live in a senior retirement facility, experience even bigger challenges as they are often dependent on other individuals for care. Many older adults are dependent on caregivers to assist with their instrumented activities of daily living (IADL). With travel restrictions imposed during a pandemic, there is a critical need to ensure that older adults who are homebound continue to be able to participate in physical exercise, cognitive exercise, and social interaction programs. The objective of this study was to better understand the challenges that older adults faced during the pandemic and what they were doing specifically to cope with the pandemic physically, mentally, and through social interaction. A focus group was conducted with ten older adults (age: 82.70 ± 7.81 years; nine female and one male) who resided in a senior retirement facility. During the course of one hour, seven open-ended questions were posed to the participants: a) What has changed in your life since the start of the pandemic, b) What has been most challenging for you, c) What are you doing to take care of yourself, d) Are you doing anything specifically as it relates to your physical health, e) Are you doing anything specifically as it relates to your mental health, f) What did you do for social interaction during the pandemic, g) Is there anything else you would like to share as it relates to your experience during the pandemic. The focus group session was audio-taped, and verbatim transcripts were created to evaluate the responses of the participants. The transcript consisted of 4,698 words and 293 lines of text. The data was analyzed using content analysis. The unit of analysis was the text from the audio recordings that were transcribed. From the review of the transcribed text, themes and sub-themes were identified, along with salient quotes under each sub-theme. The major themes that emerged from the data were: having a routine, engaging in activities, attending exercise classes, use of technology, family, community, and prayer. The quotes under the sub-themes provided compelling evidence of how older adults coped during the pandemic while addressing the challenges they faced and developing strategies to address their physical and mental health while interacting with others. Lessons learned from this focus group can be used to develop specific physical exercise, cognitive exercise, and social interaction programs that benefit the health and well-being of older adults.

Keywords: cognitive exercise, pandemic, physical exercise, social interaction

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Authors: Dimiter M. Dimitrov, Abdullah Sadaawi

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The National Assessment for Schools (NAFS) in Saudi Arabia consists of standardized tests in Mathematics, Reading, and Science for school grade levels 3, 6, and 9. One main goal is to classify students into four categories of NAFS performance (minimal, basic, proficient, and advanced) by schools and the entire national sample. The NAFS scoring and equating is performed on a bounded scale (D-scale: ranging from 0 to 1) in the framework of the recently developed “D-scoring method of measurement.” The specificity of the NAFS measurement framework and data complexity presented both challenges and opportunities to (a) the estimation of score reliability for schools, (b) setting cut-scores for the classification of students into categories of performance, and (c) generating plausible values for distributions of student performance on the D-scale. The estimation of score reliability at the school level was performed in the framework of generalizability theory (GT), with students “nested” within schools and test items “nested” within test forms. The GT design was executed via a multilevel modeling syntax code in R. Cut-scores (on the D-scale) for the classification of students into performance categories was derived via a recently developed method of standard setting, referred to as “Response Vector for Mastery” (RVM) method. For each school, the classification of students into categories of NAFS performance was based on distributions of plausible values for the students’ scores on NAFS tests by grade level (3, 6, and 9) and subject (Mathematics, Reading, and Science). Plausible values (on the D-scale) for each individual student were generated via random selection from a statistical logit-normal distribution with parameters derived from the student’s D-score and its conditional standard error, SE(D). All procedures related to D-scoring, equating, generating plausible values, and classification of students into performance levels were executed via a computer program in R developed for the purpose of NAFS data analysis.

Keywords: large-scale assessment, reliability, generalizability theory, plausible values

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: Alina Muradyan, Lina Babayan, Arsen Nanyan, Gohar Galstyan, Vigen Khachatryan

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We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of ∼> 65000 color templates for comparison with observed objects. The method aims for extracting the information content of object colors in a statistically correct way, and performs a classification as well as a redshift estimation for galaxies and quasars in a unified approach based on the same probability density functions. For the redshift estimation, we employ an advanced version of the Minimum Error Variance estimator which determines the redshift error from the redshift dependent probability density function itself. The method was originally developed for the Calar Alto Deep Imaging Survey (CADIS), but is now used in a wide variety of survey projects. We checked its performance by spectroscopy of CADIS objects, where the method provides high reliability (6 errors among 151 objects with R < 24), especially for the quasar selection, and redshifts accurate within σz ≈ 0.03 for galaxies and σz ≈ 0.1 for quasars. For an optimization of future survey efforts, a few model surveys are compared, which are designed to use the same total amount of telescope time but different sets of broad-band and medium-band filters. Their performance is investigated by Monte-Carlo simulations as well as by analytic evaluation in terms of classification and redshift estimation. If photon noise were the only error source, broad-band surveys and medium-band surveys should perform equally well, as long as they provide the same spectral coverage. In practice, medium-band surveys show superior performance due to their higher tolerance for calibration errors and cosmic variance. Finally, we discuss the relevance of color calibration and derive important conclusions for the issues of library design and choice of filters. The calibration accuracy poses strong constraints on an accurate classification, which are most critical for surveys with few, broad and deeply exposed filters, but less severe for surveys with many, narrow and less deep filters.

Keywords: VO, ArVO, DFBS, FITS, image processing, data analysis

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