Search results for: emotion detection in the text

Authors: Giya Erina

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Linguists have analyzed the rhetorical structure of various forensic genres, but only a few have investigated the complete structure of death row statements. Unlike other forensic text types, such as suicide or ransom notes, the focus of death row statement analysis is not the authenticity or falsity of the text, but its intended meaning and its communicative purpose. As it constitutes their last statement before their execution, there are probably many things that inmates would like to express. This study mainly examines the rhetorical moves of 200 death row statements from the Texas Department of Criminal Justice website using rhetorical move analysis. The rhetorical moves identified in the statements will be classified based on their communicative purpose, and they will be grouped into moves and steps. A move structure will finally be suggested from the most common or characteristic moves and steps, as well as some sub-moves. However, because of some statements’ atypicality, some moves may appear in different parts of the texts or not at all.

Keywords: Death row statements, forensic linguistics, genre analysis, move analysis

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Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Mulugeta K. Tefera, Xiaolong Yang, Jian Liu

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Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used.

Keywords: background modeling, Gaussian mixture model, inter-frame difference, object detection and tracking, video surveillance

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Authors: Kevin Smith

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Publicly traded company in the US are legally obligated to host earnings calls that discuss their most recent financial reports. During these calls, investors are able to ask these companies questions about these financial reports and on the future direction of the company. This paper examines whether common institutional owners use these calls as a way to indirectly signal to companies in their portfolio to not take actions that could hurt the common owner's interests. This paper uses transcripts taken from the earnings calls of the six largest health insurance companies in the US from 2014 to 2019. This data is analyzed using text analysis and sentiment analysis to look for patterns in the statements made by common owners. The analysis found that common owners where more likely to recommend against direct price competition and instead redirect the insurance companies towards more passive actions, like investing in new technologies. This result indicates a mechanism that common owners use to reduce competition in the health insurance market.

Keywords: common ownership, text analysis, sentiment analysis, machine learning

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Authors: Moquan Gong

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In recent years, with the rapid development of deep learning, pre-training technology for natural language processing has made great progress. The early field of natural language processing has long used word vector methods such as Word2Vec to encode text. These word vector methods can also be regarded as static pre-training techniques. However, this context-free text representation brings very limited improvement to subsequent natural language processing tasks and cannot solve the problem of word polysemy. ELMo proposes a context-sensitive text representation method that can effectively handle polysemy problems. Since then, pre-training language models such as GPT and BERT have been proposed one after another. Among them, the BERT model has significantly improved its performance on many typical downstream tasks, greatly promoting the technological development in the field of natural language processing, and has since entered the field of natural language processing. The era of dynamic pre-training technology. Since then, a large number of pre-trained language models based on BERT and XLNet have continued to emerge, and pre-training technology has become an indispensable mainstream technology in the field of natural language processing. This article first gives an overview of pre-training technology and its development history, and introduces in detail the classic pre-training technology in the field of natural language processing, including early static pre-training technology and classic dynamic pre-training technology; and then briefly sorts out a series of enlightening technologies. Pre-training technology, including improved models based on BERT and XLNet; on this basis, analyze the problems faced by current pre-training technology research; finally, look forward to the future development trend of pre-training technology.

Keywords: natural language processing, pre-training, language model, word vectors

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Authors: YingWei Tan, XueFeng Ding

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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Michael Leontiev, Danil Zhilikov, Dmitry Lobanov, Lenar Klimov, Vyacheslav Chertan, Daniel Bobrov, Vladislav Maslov, Vasilii Vologdin, Ksenia Balabaeva

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Pharmaceutical manufacturing is a complex process, where each stage requires a high level of safety and sterility. Personal Protective Equipment (PPE) is used for this purpose. Despite all the measures of control, the human factor (improper PPE wearing) causes numerous losses to human health and material property. This research proposes a solid computer vision system for ensuring safety in pharmaceutical laboratories. For this, we have tested a wide range of state-of-the-art object detection methods. Composing previously obtained results in this sphere with our own approach to this problem, we have reached a high accuracy ([email protected]) ranging from 0.77 up to 0.98 in detecting all the elements of a common set of PPE used in pharmaceutical laboratories. Our system is a step towards safe medicine production.

Keywords: sterility and safety in pharmaceutical development, personal protective equipment, computer vision, object detection, monitoring in pharmaceutical development, PPE

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Christopher Dick

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While the translation of 'literal language' poses numerous challenges for the translator, the translation of 'figurative language' creates even more complicated issues. It has been only in the last several decades that scholars have attempted to propose theories of figurative language translation, including metaphor translation. Even less work has applied these theories to metaphoric translation in literary texts. And almost no work has linked an analysis of metaphors in translation with the recent scholarship on conceptual metaphors. A study of literature in translation must not only examine the inevitable shifts that occur as specific metaphors move from source language to target language but also analyze the ways in which these shifts impact conceptual metaphors and, ultimately, the text as a whole. Doing so contributes to on-going efforts to bridge the sometimes wide gulf between considerations of content and form in literary studies. This paper attempts to add to the body of scholarly literature on metaphor translation and the function of metaphor in a literary text. Specifically, the study examines the metaphoric expressions in Hemingway’s The Sun Also Rises. First, the issue of Hemingway and metaphor is addressed. Next, the study examines the specific metaphors in the original novel in English and the German translations, first in Annemarie Horschitz’s 1928 German version and then in the recent Werner Schmitz 2013 translation. Hemingway’s metaphors, far from being random occurrences of figurative language, are linguistic manifestations of deeper conceptual metaphors that are central to an interpretation of the text. By examining the modifications that are made to these original metaphoric expressions as they are translated into German, one can begin to appreciate the shifts involved with metaphor translation. The translation of Hemingway’s metaphors into German represents significant metaphoric loss and shifting that subsequently shakes the important conceptual metaphors in the novel.

Keywords: Hemingway, Conceptual Metaphor, Translation, Stylistics

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

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Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

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Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

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Authors: Damien Mouratille, Antonio R. Hidalgo-Muñoz, Nadine Matton, Yves Rouillard, Mickael Causse, Radouane El Yagoubi

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Background: The monitoring of the physiological activity related to mental workload (MW) on pilots will be useful to improve aviation safety by anticipating human performance degradation. The electrocardiogram (ECG) can reveal MW fluctuations due to either cognitive workload or/and emotional state since this measure exhibits autonomic nervous system modulations. Arguably, heart rate (HR) is one of its most intuitive and reliable parameters. It would be particularly interesting to analyze the interaction between cognitive requirements and emotion in ecologic sets such as a flight simulator. This study aims to explore by means of HR the relation between cognitive demands and emotional activation. Presumably, the effects of cognition and emotion overloads are not necessarily cumulative. Methodology: Eight healthy volunteers in possession of the Private Pilot License were recruited (male; 20.8±3.2 years). ECG signal was recorded along the whole experiment by placing two electrodes on the clavicle and left pectoral of the participants. The HR was computed within 4 minutes segments. NASA-TLX and Big Five inventories were used to assess subjective workload and to consider the influence of individual personality differences. The experiment consisted in completing two dual-tasks of approximately 30 minutes of duration into a flight simulator AL50. Each dual-task required the simultaneous accomplishment of both a pre-established flight plan and an additional task based on target stimulus discrimination inserted between Air Traffic Control instructions. This secondary task allowed us to vary the cognitive workload from low (LC) to high (HC) levels, by combining auditory and visual numerical stimuli to respond to meeting specific criteria. Regarding emotional condition, the two dual-tasks were designed to assure analogous difficulty in terms of solicited cognitive demands. The former was realized by the pilot alone, i.e. Low Arousal (LA) condition. In contrast, the latter generates a high arousal (HA), since the pilot was supervised by two evaluators, filmed and involved into a mock competition with the rest of the participants. Results: Performance for the secondary task showed significant faster reaction times (RT) for HA compared to LA condition (p=.003). Moreover, faster RT was found for LC compared to HC (p < .001) condition. No interaction was found. Concerning HR measure, despite the lack of main effects an interaction between emotion and cognition is evidenced (p=.028). Post hoc analysis showed smaller HR for HA compared to LA condition only for LC (p=.049). Conclusion. The control of an aircraft is a very complex task including strong cognitive demands and depends on the emotional state of pilots. According to the behavioral data, the experimental set has permitted to generate satisfactorily different emotional and cognitive levels. As suggested by the interaction found in HR measure, these two factors do not seem to have a cumulative impact on the sympathetic nervous system. Apparently, low cognitive workload makes pilots more sensitive to emotional variations. These results hint the independency between data processing and emotional regulation. Further physiological data are necessary to confirm and disentangle this relation. This procedure may be useful for monitoring objectively pilot’s mental workload.

Keywords: cognitive demands, emotion, flight simulator, heart rate, mental workload

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Authors: Luís Miguel Cabeça Marques, Alberto Manuel Martinho Vale, José Pedro Miragaia Trancoso Vaz, Ana Sofia Baptista Fernandes, Rui Alexandre de Barros Coito, Tiago Miguel Prates da Costa

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The detection and localization of hidden radioactive sources are of significant importance in countering the illicit traffic of Special Nuclear Materials and other radioactive sources and materials. Radiation portal monitors are commonly used at airports, seaports, and international land borders for inspecting cargo and vehicles. However, these equipment can be expensive and are not available at all checkpoints. Consequently, the localization of SNM and other radioactive sources often relies on handheld equipment, which can be time-consuming. The current study presents the advantages of real-time analysis of gamma-ray count rate data from a mobile radiation detection system based on simulated data and field tests. The incorporation of profit functions and decision criteria to optimize the detection system's path significantly enhances the radiation field information and reduces survey time during cargo inspection. For source position estimation, a maximum likelihood estimation algorithm is employed, and confidence intervals are derived using the Fisher information. The study also explores the impact of uncertainties, baselines, and thresholds on the performance of the profit function. The proposed detection system, utilizing a plastic scintillator with silicon photomultiplier sensors, boasts several benefits, including cost-effectiveness, high geometric efficiency, compactness, and lightweight design. This versatility allows for seamless integration into any mobile platform, be it air, land, maritime, or hybrid, and it can also serve as a handheld device. Furthermore, integration of the detection system into drones, particularly multirotors, and its affordability enable the automation of source search and substantial reduction in survey time, particularly when deploying a fleet of drones. While the primary focus is on inspecting maritime container cargo, the methodologies explored in this research can be applied to the inspection of other infrastructures, such as nuclear facilities or vehicles.

Keywords: plastic scintillators, profit functions, path planning, gamma-ray detection, source localization, mobile radiation detection system, security scenario

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Authors: Jeewan Rai, Kinzang, Yeshi Jigme Choden

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Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy they produce. While various techniques exist for detecting dust to schedule cleaning, many of these methods use MATLAB image processing tools and other licensed software, which can be financially burdensome. This study will investigate the efficiency of a free open-source computer vision library using the YOLO algorithm. The proposed approach has been tested on images of solar panels with varying dust levels through an experiment setup. The experimental findings illustrated the effectiveness of using the YOLO-based image classification method and the overall dust detection approach with an accuracy of 90% in distinguishing between clean and dusty panels. This open-source solution provides a cost effective and accessible alternative to commercial image processing tools, offering solutions for optimizing solar panel maintenance and enhancing energy production.

Keywords: YOLO, openCV, dust detection, solar panels, computer vision, image processing

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Authors: Arnab Hazra

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Breath analysis is a quick, noninvasive and inexpensive technique for disease diagnosis can be used on people of all ages without any risk. Only a limited number of volatile organic compounds (VOCs) can be associated with the occurrence of specific diseases. These VOCs can be considered as disease markers or breath markers. Selective detection with specific concentration of breath marker in exhaled human breath is required to detect a particular disease. For example, acetone (C₃H₆O), ethanol (C₂H₅OH), ethane (C₂H₆) etc. are the breath markers and abnormal concentrations of these VOCs in exhaled human breath indicates the diseases like diabetes mellitus, renal failure, breast cancer respectively. Nanomaterial-based vapor sensors are inexpensive, small and potential candidate for the detection of breath markers. In practical measurement, selectivity is the most crucial issue where trace detection of breath marker is needed to identify accurately in the presence of several interfering vapors and gases. Current article concerns a novel technique for selective and lower ppb level detection of breath markers at very low temperature based on TiO₂ nanotube array based vapor sensor devices. Highly ordered and oriented TiO₂ nanotube array was synthesized by electrochemical anodization of high purity tatinium (Ti) foil. 0.5 wt% NH₄F, ethylene glycol and 10 vol% H₂O was used as the electrolyte and anodization was carried out for 90 min with 40 V DC potential. Au/TiO₂ Nanotube/Ti, sandwich type sensor device was fabricated for the selective detection of VOCs in low concentration range. Initially, sensor was characterized where resistive and capacitive change of the sensor was recorded within the valid concentration range for individual breath markers (or organic vapors). Sensor resistance was decreased and sensor capacitance was increased with the increase of vapor concentration. Now, the ratio of resistive slope (mR) and capacitive slope (mC) provided a concentration independent constant term (M) for a particular vapor. For the detection of unknown vapor, ratio of resistive change and capacitive change at any concentration was same to the previously calculated constant term (M). After successful identification of the target vapor, concentration was calculated from the straight line behavior of resistance as a function of concentration. Current technique is suitable for the detection of particular vapor from a mixture of other interfering vapors.

Keywords: breath marker, vapor sensors, selective detection, TiO₂ nanotube array

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing

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Authors: Shahrokh Barati, Reza Ramezani

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Due to the countries growing attention to the renewable energy producing, the demand for energy from renewable energy has gone up among the renewable energy sources; wind energy is the fastest growth in recent years. In this regard, in order to increase the availability of wind turbines, using of Fault Detection and Isolation (FDI) system is necessary. Wind turbines include of various faults such as sensors fault, actuator faults, network connection fault, mechanical faults and faults in the generator subsystem. Although, sensors and actuators have a large number of faults in wind turbine but have discussed fewer in the literature. Therefore, in this work, we focus our attention to design a sensor and actuator fault detection and isolation algorithm and Fault-tolerant control systems (FTCS) for Wind Turbine. The aim of this research is to propose a comprehensive fault detection and isolation system for sensors and actuators of wind turbine based on data-driven approaches. To achieve this goal, the features of measurable signals in real wind turbine extract in any condition. The next step is the feature selection among the extract in any condition. The next step is the feature selection among the extracted features. Features are selected that led to maximum separation networks that implemented in parallel and results of classifiers fused together. In order to maximize the reliability of decision on fault, the property of fault repeatability is used.

Keywords: FDI, wind turbines, sensors and actuators faults, renewable energy

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Robin Gengan

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The film industry is one of the most impactful creative sectors in modern social influence. It has relational effects on knowledge and psychological impact on the youth. Much focus in current filmmaking is either in fictional drama or documentary. The purpose of this article is to combine the two into a third genre; edutainment in which film is approached as a visual educational text. Similar to language text, cinema can be applied to semiotic reading. Film interpretation is a phenomenological order, unique to each viewer. There are cultural norms and tropes that are more universal between the practice of semiotic reading, symbolism and interpretation. Film semiotics and narration are a juxtaposition of moving visual texts and sound to create meaning through film codes and social conventions to form an educational narrative that makes the medium effective for learning and teaching. The aim of this article is to explore and set precedence for more creative building-blocks into future research on edutainment cinema. This will further stimulate and benefit innovative entrepreneurial filmmaking and future academic research.

Keywords: cinema, edutainment, epistemology, multimodality, semiotics, structuralism

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Authors: M. Mansouri, P. Jaklitsch, E. Teiniker

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SQL injection on web applications is a very popular kind of attack. There are mechanisms such as intrusion detection systems in order to detect this attack. These strategies often rely on techniques implemented at high layers of the application but do not consider the low level of system calls. The problem of only considering the high level perspective is that an attacker can circumvent the detection tools using certain techniques such as URL encoding. One technique currently used for detecting low-level attacks on privileged processes is the tracing of system calls. System calls act as a single gate to the Operating System (OS) kernel; they allow catching the critical data at an appropriate level of detail. Our basic assumption is that any type of application, be it a system service, utility program or Web application, “speaks” the language of system calls when having a conversation with the OS kernel. At this level we can see the actual attack while it is happening. We conduct an experiment in order to demonstrate the suitability of system call analysis for detecting SQL injection. We are able to detect the attack. Therefore we conclude that system calls are not only powerful in detecting low-level attacks but that they also enable us to detect high-level attacks such as SQL injection.

Keywords: Linux system calls, web attack detection, interception, SQL

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Authors: Juan C. Torrado, Javier Gomez, Guadalupe Montero, German Montoro, M. Dolores Villalba

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One of the most challenging aspects of the executive dysfunction of people with Autism Spectrum Disorders is the behavior control. This is related to a deficit in their ability to regulate, recognize and manage their own emotions. Some researchers have developed applications for tablets and smartphones to practice strategies of relaxation and emotion recognition. However, they cannot be applied to the very moment of temper outbursts, anger episodes or anxiety, since they require to carry the device, start the application and be helped by caretakers. Also, some of these systems are developed for either obsolete technologies (old versions of tablet devices, PDAs, outdated operative systems of smartphones) or specific devices (self-developed or proprietary ones) that create differentiation between the users and the rest of the individuals in their context. For this project we selected smartwatches. Focusing on emergent technologies ensures a wide lifespan of the developed products, because the derived products are intended to be available in the same moment the very technology gets popularized, not later. We also focused our research in commercial versions of smartwatches, since this way differentiation is easily avoided, so the users’ abandonment rate lowers. We have developed a smartwatch system along with a smartphone authoring tool to display self-regulation strategies. These micro-prompting strategies are conformed of pictograms, animations and temporizers, and they are designed by means of the authoring tool: When both devices synchronize their data, the smartwatch holds the self-regulation strategies, which are triggered when the smartwatch sensors detect a remarkable rise of heart rate and movement. The system is being currently tested in an educational center of people with ASD of Madrid, Spain.

Keywords: assistive technologies, emotion regulation, human-computer interaction, smartwatches

Procedia PDF Downloads 291

Authors: Ritu Sharma

Abstract:

In order to be objective, critics must examine the dynamic relationships between the author, the reader, the text, and the outside world. However, it is also crucial to recognize that because the language was created by God, meaning is ingrained in it. Meaning is located in and discovered through literature rather than being limited to the author, reader, text, or the outside world. The link between the author, the reader, and the text is crucial because literature unites an author and a reader through the use of language. Literature is a potent kind of communication, and Ethan Frome's audience is forever changed as a result of the book's language and the language its characters use. The narrative of Ethan Frome and his wife Zeena is presented in Ethan Frome. Ethan's story is told throughout the course of the book, revealed through the eyes of the narrator, an outsider passing through Starkfield, as well as through the insight that the narrator gains from the townspeople and his stay on the Frome farm. The story is set in the rural New England community of Starkfield, Massachusetts. The weather provides the ideal setting for Ethan and the narrator to get to know one another as the narrator gets preoccupied with unraveling the narrative that underlies Ethan's physical anomalies. In addition to telling a gripping tale and capturing human nature as it is, Ethan Frome uses its storyline to achieve something more significant. The book by Edith Wharton supports language. Zeena's deliberate and convincing language challenges relativity and meaninglessness. Ethan and Mattie's effort to effectively use words reflects the complexity of language, and their battle illustrates the influence that language may have if and when it is used. Ethan Frome defends the written word, the foundation upon which it is constructed, as a literary work. Communication is based on language, and as the characters respond to and get involved in disputes throughout the book, Zeena, Ethan, and Mattie, each reflects particular theories of communication that help define their uses of communication within the broader context of language.

Keywords: dynamic relationships, potent, communication, complexity

Procedia PDF Downloads 88

Authors: Ulrica Pettersson

Abstract:

Collection of information on incidents is regularly done through pre-printed incident report forms. These tend to be incomplete and frequently lack essential information. ne consequence is that reports with inadequate information, that do not fulfil analysts’ requirements, are transferred into the analysis process. To improve an incident reporting form, theory in design science, witness psychology and interview and questionnaire research has been used. Previously three experiments have been conducted to evaluate the form and shown significant improved results. The form has proved to capture knowledge, regardless of the incidents’ character or context. The aim in this paper is to describe how design science, in more detail a design hierarchy can be used to construct a collection form for improvements in safety science.

Keywords: data collection, design science, incident reports, safety science

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Authors: Gong Zhilin, Jing Yang, Jian Yin

Abstract:

The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: A. T. Kuda, J. J. Dayya, A. Jimoh

Abstract:

This paper investigates the fault detection and Isolation technique of measurement data sets from a three tank system using analytical model-based temporal redundancy which is based on residual generation using parity equations/space approach. It further briefly outlines other approaches of model-based residual generation. The basic idea of parity space residual generation in temporal redundancy is dynamic relationship between sensor outputs and actuator inputs (input-output model). These residuals where then used to detect whether or not the system is faulty and indicate the location of the fault when it is faulty. The method obtains good results by detecting and isolating faults from the considered data sets measurements generated from the system.

Keywords: fault detection, fault isolation, disturbing influences, system failure, parity equation/relation, structured parity equations

Procedia PDF Downloads 297

Authors: Binh T. T. Nguyen, Zhenyu Li, Eric Yap, Yi Zhang, Ai-Qun Liu

Abstract:

Silicon-photonic-sensor system is an emerging class of analytical technologies that use evanescent field wave to sensitively measure the slight difference in the surrounding environment. The wavelength shift induced by local refractive index change is used as an indicator in the system. These devices can be served as sensors for a wide variety of chemical or biomolecular detection in clinical and environmental fields. In our study, a system including a silicon-based micro-ring resonator, microfluidic channel, and optical processing is designed, fabricated for biomolecule detection. The system is demonstrated to detect Clostridium botulinum type A neurotoxin (BoNT) in different water sources. BoNT is one of the most toxic substances known and relatively easily obtained from a cultured bacteria source. The toxin is extremely lethal with LD50 of about 0.1µg/70kg intravenously, 1µg/ 70 kg by inhalation, and 70µg/kg orally. These factors make botulinum neurotoxins primary candidates as bioterrorism or biothreat agents. It is required to have a sensing system which can detect BoNT in a short time, high sensitive and automatic. For BoNT detection, silicon-based micro-ring resonator is modified with a linker for the immobilization of the anti-botulinum capture antibody. The enzymatic reaction is employed to increase the signal hence gains sensitivity. As a result, a detection limit to 30 pg/mL is achieved by our silicon-photonic sensor within a short period of 80 min. The sensor also shows high specificity versus the other type of botulinum. In the future, by designing the multifunctional waveguide array with fully automatic control system, it is simple to simultaneously detect multi-biomaterials at a low concentration within a short period. The system has a great potential to apply for online, real-time and high sensitivity for the label-free bimolecular rapid detection.

Keywords: biotoxin, photonic, ring resonator, sensor

Procedia PDF Downloads 113