Search results for: dual task

Authors: B. McGovern, J. F. Luck, A. Gade, I. V. Lake, D. O’Connell, H. C. Cutcliffe, K. P. Shah, E. E. Ginalis, C. M. Lambert, N. Christian, J. R. Kait, A. W. Yu, C. P. Eckersley, C. R. Bass

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Mild traumatic brain injury (mTBI) in the form of post-concussive injuries and attention deficit / hyperactivity disorder (ADHD) share similar cognitive impairments, including impaired working memory and executive function. The memory-guided oculomotor task separates working memory and inhibitory components to provide further information on the nature of these deficits in each pathology. Eleven subjects with ADHD, fifteen control subjects, and ten subjects with recent concussive injury were matched on age, gender, and education (all high school-age males). Eye movements were recorded during memory-guided oculomotor tasks with varying delays using EyeLink 1000 (SR Research). The percentage of premature saccades and the latency of correct response are the analyzed measures for response inhibition and working memory, respectively. No significant differences were found in latencies between controls subjects and subjects with ADHD or post-concussive injuries, in accordance with previous studies. Subjects with ADHD and post-concussive injuries both demonstrated a trend of increased percentages of premature saccades compared to control subjects in the same oculomotor task. This trend reached statistical significance between the post-concussive and control groups (p < 0.05). These findings support the primary nature of the executive function deficits in response inhibition in ADHD and mTBI. The interpretation of results is limited by the small sample size and the exploratory nature of the study. Further investigation into oculomotor performance differences in mTBI and ADHD may help in differentiating these pathologies in consequent diagnoses and provide insight into the interaction of these deficits in mTBI.

Keywords: attention deficit / hyperactivity disorder (ADHD), concussion, diagnosis, oculomotor, pediatrics

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Authors: Nurjannatul Naim Kamaruddin, Tengku Sifziuzl Tengku Muhammad, Aina Farahiyah Abdul Manan, Habsah Mohamad

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Atherosclerosis which leads to cardiovascular diseases such as myocardial infarction, unstable angina (ischemic heart pain), sudden cardiac death and stroke is the principal cause of death worldwide. It has been a very critical issue as current common drug treatment, statin therapy has left bad side effects like rhabdomyolysis, atrial fibrillation, liver disease, abdominal and chest pain. Interestingly, the discoveries of proprotein convertase subtilisin-kexin type 9 have paved a new way in the treatment of atherosclerosis. This serine protease is believed to involve in the regulation of LDL- uptake by LDL-receptor. Therefore, this study was conducted to evaluate the potential of Acanthaster plancii fractions to reduce the transcriptional activity of the PCSK9 promoter. In this study, the marine organism which is Acanthaster plancii has been used as the source for marine compounds in inhibiting PCSK9. The cytotoxicity activity of ten fractions from the methanol extracts of Acanthaster plancii was investigated on HepG2 cell lines using MTS assay and dual glo luciferase assay was carried out later to analyses the effects of the samples in reducing the transcriptional activity of the PCSK9 promoter. Both assays used fractions with five different concentrations, 3.13µg/mL, 6.25µg/mL, 12.5µg/mL, 25µg/mL, and 50µg/mL. MTS assay indicated that the fractions are non-cytotoxic towards HepG2 cell lines as their IC50 value is greater than 30µg/mL. Whilst, for the dual glo luciferase assay, among all the fractions, Enhance Fraction 2 (EF2) showed the best potential in reducing the transcriptional activity of the PCSK9 promoter. The results indicated that this EF2 gave the lowest PCSK9 promoter expression at low concentration which is 0.2 fold change at 6.25µg/mL. This finding suggested that further analysis should be done to validate the potential of Acanthaster plancii as the source of anti-atherosclerotic agent.

Keywords: Acanthaster plancii, atherosclerosis, luciferase assay, PCSK9

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Authors: Camellia Al-Ibrahim, Jenny Yiend, Sukhwinder S. Shergill

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Background: Attention bias to threat play a role in the development, maintenance, and exacerbation of delusional beliefs in schizophrenia in which patients emphasize the threatening characteristics of stimuli and prioritise them for processing. Cognitive control deficits arise when task-irrelevant emotional information elicits attentional bias and obstruct optimal performance. This study is investigating neural correlates of interference effect of linguistic threat and whether these effects are independent of delusional severity. Methods: Using an event-related functional magnetic resonance imaging (fMRI), neural correlates of interference effect of linguistic threat during the emotional Stroop task were investigated and compared patients with schizophrenia with high (N=17) and low (N=16) paranoid symptoms and healthy controls (N=20). Participants were instructed to identify the font colour of each word presented on the screen as quickly and accurately as possible. Stimuli types vary between threat-relevant, positive and neutral words. Results: Group differences in whole brain effects indicate decreased amygdala activity in patients with high paranoid symptoms compared with low paranoid patients and healthy controls. Regions of interest analysis (ROI) validated our results within the amygdala and investigated changes within the striatum showing a pattern of reduced activation within the clinical group compared to healthy controls. Delusional severity was associated with significant decreased neural activity in the striatum within the clinical group. Conclusion: Our findings suggest that the emotional interference mediated by the amygdala and striatum may reduce responsiveness to threat-related stimuli in schizophrenia and that attenuation of fMRI Blood-oxygen-level dependent (BOLD) signal within these areas might be influenced by the severity of delusional symptoms.

Keywords: attention bias, fMRI, Schizophrenia, Stroop

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Sven Tackenberg, Sönke Duckwitz, Andreas Petz, Christopher M. Schlick

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This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to complex maintenance problems. The problem is to assign technicians to a team which has to process several tasks with multi-level skill requirements during a work shift. Here, several alternative activities for a task allow both, the temporal shift of activities or the reallocation of technicians and tools. As a result, switches from one valid work process variant to another can be considered and may be selected by the developed evolutionary algorithm based on the present skill level of technicians or the available tools. An additional complication of the observed scheduling problem is that the locations of the construction sites are only temporarily accessible during a day. Due to intensive rail traffic, the available time slots for maintenance and repair works are extremely short and are often distributed throughout the day. To identify efficient working periods, a first concept of a Bayesian network is introduced and is integrated into the extended RCPSP with pre-emptive and non-pre-emptive tasks. Thereby, the Bayesian network is used to calculate the probability of a maintenance task to be processed during a specific period of the shift. Focusing on the domain of maintenance of the railway infrastructure in metropolitan areas as the most unproductive implementation process at construction site, the paper illustrates how the extended RCPSP can be applied for maintenance planning support. A multi-criteria evolutionary algorithm with a problem representation is introduced which is capable of revising technician-task allocations, whereas the duration of the task may be stochastic. The approach uses a novel activity list representation to ensure easily describable and modifiable elements which can be converted into detailed shift schedules. Thereby, the main objective is to develop a shift plan which maximizes the utilization of each technician due to a minimization of the waiting times caused by rail traffic. The results of the already implemented core algorithm illustrate a fast convergence towards an optimal team composition for a shift, an efficient sequence of tasks and a high probability of the subsequent implementation due to the stochastic durations of the tasks. In the paper, the algorithm for the extended RCPSP is analyzed in experimental evaluation using real-world example problems with various size, resource complexity, tightness and so forth.

Keywords: maintenance management, scheduling, resource constrained project scheduling problem, genetic algorithms

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Authors: Hercules Argyropoulos, Thomas F. Johnson, Nigel B Jackson, Kalliopi Zourna, Daniel G. Bracewell

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Lipid encapsulation has become essential in drug delivery, notably for mRNA vaccines during the COVID-19 pandemic. However, their sterile filtration poses challenges due to the risk of deformation, filter fouling and product loss from adsorption onto the membrane. Choosing the right filtration membrane is crucial to maintain sterility and integrity while minimizing product loss. The objective of this study is to develop a rigorous analytical framework utilizing confocal microscopy and filtration blocking models to elucidate the fouling mechanisms of liposomes as a model system for this class of delivery vehicle during sterile filtration, particularly in response to variations in transmembrane pressure (TMP) during the filtration process. Experiments were conducted using fluorescent Lipoid S100 PC liposomes formulated by micro fluidization and characterized by Multi-Angle Dynamic Light Scattering. Dual-layer PES/PES and PES/PVDF membranes with 0.2 μm pores were used for filtration under constant pressure, cycling from 30 psi to 5 psi and back to 30 psi, with 5, 6, and 5-minute intervals. Cross-sectional membrane samples were prepared by microtome slicing and analyzed with confocal microscopy. Liposome characterization revealed a particle size range of 100-140 nm and an average concentration of 2.93x10¹¹ particles/mL. Goodness-of-fit analysis of flux decline data at varying TMPs identified the intermediate blocking model as most accurate at 30 psi and the cake filtration model at 5 psi. Membrane resistance analysis showed atypical behavior compared to therapeutic proteins, with resistance remaining below 1.38×10¹¹ m⁻¹ at 30 psi, increasing over fourfold at 5 psi, and then decreasing to 1-1.3-fold when pressure was returned to 30 psi. This suggests that increased flow/shear deforms liposomes enabling them to more effectively navigate membrane pores. Confocal microscopy indicated that liposome fouling mainly occurred in the upper parts of the dual-layer membrane.

Keywords: sterile filtration, membrane resistance, microfluidization, confocal microscopy, liposomes, filtration blocking models

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Authors: Anne Lehner, Mostafa Salari Rad, Jeremy Ginges

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Many of life’s privileges (and burdens) are thrust on us at birth. Someone born white or male in the United States is also born with a set of advantages over someone born non-white or female. One aspect of privileges conferred by birth is that they are so entrenched in social institutions and social norms that until they are robustly challenged, they can be seen as a moral good. While American society increasingly confronts privileges based on gender and race, other types of privileges, like one's nationality, see less attention. The nationality one is born into can have enormous effects on one’s personal life, work opportunities, and health outcomes. Yet, we predicted that although most Americans would regard it as absurd to think that white people have a right to protect their privileges and 'way of life', they would regard it as obvious that Americans have a right to protect the American way of life and associated privileges. In a preregistered study we presented 300 Americans randomly with one out of three 'privilege scales' in order to assess their agreement with certain statements. The domains for the privilege scales were nationality, race, and gender. Next, all participants completed the switched-at-birth task assessing ones tendency to essentialize nationality. We found that Americans are more approving of privilege based on nationality than of privilege based on gender and race. In addition, we found an interaction of condition with ideology, showing that conservatives are in general more approving of the privilege of any kind than liberals are, and they especially approve of privilege based on nationality. For the switched-at-birth task, we found that both, liberals as well as conservatives are equally willing to grant the child 100% American nationality. Whether or not one chose 100% is unrelated to the expressed approval of privilege based on nationality. One might hesitate to fully grant the child 100% American nationality in the task, yet disapprove of privilege based on nationality. This shows that as much as we see beholders of privilege being oblivious to their statuses within other social categories, like gender or race, we seem to detect the same blindness for the privilege based on nationality. Liberals showing relatively fewer support for privilege based on nationality compared to conservatives still refused to acknowledge the child as having become 100% American and thereby denying the privileges it potentially bestows upon them.

Keywords: thought experiment, anti-immigrant attitudes, privilege of nationality, immigration, moral circles, psychology

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Authors: Terry Teo, Sun Woh Lye, Yufei Li, Zainuddin Zakaria

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Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is therefore expected to increase. Despite the growing number of EEG research into brain functioning activities of a person, key challenges remain in adopting EEG for real-time operations. These include limited portability, long preparation time, high number of channel dimensionality, intrusiveness, as well as level of accuracy in acquiring neurological data. This paper proposes an approach using a 4-6 EEG channels to determine the cognitive states of a subject when undertaking a set of passive and active monitoring tasks of a subject. Air traffic controller (ATC) dynamic-tasks are used as a proxy. The work found that when using the channel reduction and identifier algorithm, good trend adherence of 89.1% can be obtained between a commercially available BCI 14 channel Emotiv EPOC+ EEG headset and that of a carefully selected set of reduced 4-6 channels. The approach can also identify different levels of engagement activities ranging from general monitoring ad hoc and repeated active monitoring activities involving information search, extraction, and memory activities.

Keywords: assessment, neurophysiology, monitoring, EEG

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Authors: Germán Gálvez-García, Marta Lavin, Javiera Peña, Javier Albayay, Claudio Bascour, Jesus Fernandez-Gomez, Alicia Pérez-Gálvez

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We studied the effectiveness of response inhibition in movements with different degrees of motor complexity when they were executed in isolation and alternately. Sixteen participants performed the Stroop task which was used as a measure of response inhibition. Participants responded by lifting the index finger and reaching the screen with the same finger. Both actions were performed separately and alternately in different experimental blocks. Repeated measures ANOVAs were used to compare reaction time, movement time, kinematic errors and Movement errors across conditions (experimental block, movement, and congruency). Delta plots were constructed to perform distributional analyses of response inhibition and accuracy rate. The effectiveness of response inhibition did not show difference when the movements were performed in separated blocks. Nevertheless, it showed differences when they were performed alternately in the same experimental block, being more effective for the lifting action. This could be due to a competition of the available resources during a more complex scenario which also demands to adopt some strategy to avoid errors.

Keywords: response inhibition, motor complexity, Stroop task, delta plots

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Authors: Heng Chia Ying, Charmaine Tan Chai Nie, Burra Venkata Durga Kumar

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Process scheduling is the method of process management that determines which process the CPU will proceed with for the next task and how long it takes. Some issues were found in process management, particularly for Priority Scheduling (PS) and Round Robin Scheduling (RR). The proposed recommendations made for IPRRS are to combine the strengths of both into a combining algorithm while they draw on others to compensate for each weakness. A significant improvement on the combining technique of scheduler, Incorporating Priority Round-Robin Scheduler (IPRRS) address an algorithm for both high and low priority task to sustain the indefinite blocking issue faced in the priority scheduling algorithm and minimize the average turnaround time (ATT) and average waiting time (AWT) in RR scheduling algorithm. This paper will delve into the simple rules introduced by IPRRS and enhancements that both PS and RR bring to the execution of processes in the operating system. Furthermore, it incorporates the best aspects of each algorithm to build the optimum algorithm for a certain case in terms of prioritized processes, ATT, and AWT.

Keywords: round Robin scheduling, priority scheduling, indefinite blocking, process management, sustain, turnaround time

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Authors: Hala Zaghloul, Taymoor Nazmy

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One of the great challenges of the 21st century is to build a robot that can perceive and act within its environment and communicate with people, while also exhibiting the cognitive capabilities that lead to performance like that of people. The Pulse Coupled Neural Network, PCNN, is a relative new ANN model that derived from a neural mammal model with a great potential in the area of image processing as well as target recognition, feature extraction, speech recognition, combinatorial optimization, compressed encoding. PCNN has unique feature among other types of neural network, which make it a candid to be an important approach for perceiving in cognitive systems. This work show and emphasis on the potentials of PCNN to perform different tasks related to image processing. The main drawback or the obstacle that prevent the direct implementation of such technique, is the need to find away to control the PCNN parameters toward perform a specific task. This paper will evaluate the performance of PCNN standard model for processing images with different properties, and select the important parameters that give a significant result, also, the approaches towards find a way for the adaptation of the PCNN parameters to perform a specific task.

Keywords: cognitive system, image processing, segmentation, PCNN kernels

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Authors: Fu-Chien Hung, Chi‐Wen Liang

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Background: People who suffer from chronic fatigue syndrome (CFS) frequently complain about continuous tiredness, weakness or lack of strength, but without apparent organic etiology. The prevalence rate of the CFS is nearly from 3% to 20%, yet more than 80% go undiagnosed or misdiagnosed as depression. The biopsychosocial model has suggested the associations among the CFS, depressive syndrome, and stress. This study aimed to investigate the difference between individuals with the CFS and with the depressive syndrome on psychophysiological responses under stress. Method: There were 23 participants in the CFS group, 14 participants in the depression group, and 23 participants in the healthy control group. All of the participants first completed the measures of demographic data, CFS-related symptoms, daily life functioning, and depressive symptoms. The participants were then asked to perform a stressful cognitive task. The participants’ psychophysiological responses including the HR, BVP and SC were measured during the task. These indexes were used to assess the reactivity and recovery rates of the automatic nervous system. Results: The stress reactivity of the CFS and depression groups was not different from that of the healthy control group. However, the stress recovery rate of the CFS group was worse than that of the healthy control group. Conclusion: The results from this study suggest that the CFS is a syndrome which can be independent from the depressive syndrome, although the depressive syndrome may include fatigue syndrome.

Keywords: chronic fatigue syndrome, depression, stress response, misdiagnosis

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Authors: Zhou Ziyi, Chen Qiuxiao, Wu Shuang

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Population diversification is very common in villages located in the developed coastal areas of China. Based on the analyses of the characteristics of the traditional rural society and its landscape, also in consideration of the diversified landscape demand due to the population diversification of the village, with the dual ideas of heritage and innovation, the ideas and methods of rural landscape design were explored by taking Duxuao Village in Zhejiang Province of China as an example.

Keywords: rural landscape, population diversification, landscape design, architecture

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Authors: Diane Prorak, Perri Moreno

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University students may have been born in the digital age, but they need to be taught the critical thinking skills to detect misinformation and social media manipulation online. In recent years, librarians have been active in designing instructional methods to help students learn information evaluation skills. At the University of Idaho Library (USA), librarians have developed new teaching methods for these skills. Last academic year, when classes were taught via Zoom, librarians taught these skills to an online session of each first-year rhetoric and composition course. In the Zoom sessions, students were placed in breakout groups where they practiced using an evaluation method known as lateral reading. Online collaborative software was used to give each group an evaluative task and break the task into steps. Groups reported back to the full class. Students learned to look at an information source, then search outside the source to find information about the organization, publisher or author, before evaluating the source itself. Class level pre-and post-test comparison results showed students learned better techniques for evaluation than they knew before instruction.

Keywords: critical thinking, information evaluation, information literacy instruction, lateral reading.

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Authors: Srushti Bhatt, M. B. Kiran

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Two-sided assembly line balancing problem is yet to be addressed simply to compete for the global market for manufacturers. The task assigned in an ordered sequence to get optimum performance of the system is known as assembly line balancing problem mainly classified as single and two sided. It is very challenging in manufacturing industries to balance two-sided assembly line, wherein the set of sequential workstations the task operations are performed in two sides of the line. The conflicting major objective in two-sided assembly line balancing problem is either to maximize /minimize the performance parameters. The present study emphases on combining different evolutionary algorithm; ant colony, Tabu search and petri net method; and compares their results of an algorithm for solving two-sided assembly line balancing problem. The concept of multi objective optimization of performance parameters is now a day adopted to make a decision involving more than one objective function to be simultaneously optimized. The optimum result can be expected among the selected methods using multi-objective optimization. The performance parameters considered in the present study are a number of workstation, slickness and smoothness index. The simulation of the assembly line balancing problem provides optimal results of classical and practical problems.

Keywords: Ant colony, petri net, tabu search, two sided ALBP

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Authors: Jugal Bhandari, K. Hari Priya

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The ECG signal provides important clinical information which could be used to pretend the diseases related to heart. Accordingly, delineation of ECG signal is an important task. Whereas delineation of P and T waves is a complex task. This paper deals with the Study of ECG signal and analysis of signal by means of Verilog Design of efficient filters and MATLAB tool effectively. It includes generation and simulation of ECG signal, by means of real time ECG data, ECG signal filtering and processing by analysis of different algorithms and techniques. In this paper, we design a basic particle filter which generates a dynamic model depending on the present and past input samples and then produces the desired output. Afterwards, the output will be processed by MATLAB to get the actual shape and accurate values of the ranges of P-wave and T-wave of ECG signal. In this paper, Questasim is a tool of mentor graphics which is being used for simulation and functional verification. The same design is again verified using Xilinx ISE which will be also used for synthesis, mapping and bit file generation. Xilinx FPGA board will be used for implementation of system. The final results of FPGA shall be verified with ChipScope Pro where the output data can be observed.

Keywords: ECG, MATLAB, Bayesian filtering, particle filter, Verilog hardware descriptive language

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Authors: Tugrul Torun, Ihsan A. Yuksel, Si̇nem On Aktan, Taha K. Vezi̇roglu

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In the quality control processes in some industries, the usage of CMM has increased in recent years. Consequently, the CMMs play important roles in the acceptance or rejection of manufactured parts. For parts, it’s important to be able to make decisions by performing fast measurements. According to related technical drawing and its tolerances, measurement uncertainty should also be considered during assessment. Since uncertainty calculation is difficult and time-consuming, most companies ignore the uncertainty value in their routine inspection method. Although studies on measurement uncertainty have been carried out on CMM’s in recent years, there is still no applicable method for analyzing task-specific measurement uncertainty. There are some standard series for calculating measurement uncertainty (ISO-15530); it is not possible to use it in industrial measurement because it is not a practical method for standard measurement routine. In this study, the inter-laboratory comparison test has been carried out in the ROKETSAN A.Ş. with all dimensional inspection units. The reference part that we used is traceable to the national metrology institute TUBİTAK UME. Each unit has measured reference parts according to related technical drawings, and the task-specific measuring uncertainty has been calculated with related parameters. According to measurement results and uncertainty values, the En values have been calculated.

Keywords: coordinate measurement, CMM, comparison, uncertainty

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Authors: Anes Enakoa, Yawei Liang

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Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

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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: Youru Zhou

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This paper seeks to unveil the quality assurance practices in the Chinese translation industry. Since China’s reform and opening up, the Chinese language service industry has enjoyed impressively rapid growth. However, while still in its early stage of professionalization, the Chinese translation industry is also facing many challenges, such as the lack of clear admission requirements, a powerful regulation authority and a great number of qualified professionals. ‘How quality is assured’ means a great deal to translation in China at this stage. In order to examine the mechanisms in which quality is assured, this paper studied four international and national standards that have gained widespread adoption by Chinese translation companies and examined the content that is relevant to translation quality assurance. Case studies with six selected Chinese translation companies of different sizes were conducted to confirm and exemplify the descriptions on the standards. It has been found that quality in the industry is a relative concept which is mainly determined by the demand of clients. Furthermore, the procedures of translation can vary from task to task dependent on the agreement made between the service provider and clients. Finally, there are companies relying on expert-oriented mechanisms to assure the quality of translation, while other companies and standards insist on process-oriented ones.

Keywords: case study, Chinese translation industry, professional practice, translation quality assurance, translation standards

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Authors: Sarah Mhae Diaz, Erika Anna De Leon, Jacklin Alwil Cartagena, Geordan Caruncong, Micah Riezl Gonzales

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This study examined the intervention of threat and surveillance on the obedience of 100 preschool children through a task variable experiment replicated from the previous studies of Higbee (1979), and Chua, J., Chua, M., & Pico (1983). Nowadays, obedience among Filipino children to authority is disregarded since they are more outspoken and rebel due to social influences. With this, aside from corporal punishment, threat and surveillance became a mean of inducing obedience. Threat, according to the Dissonance Theory, can give attitudinal change. On the other hand, surveillance, according to the Theory of Social Facilitation, can either contribute to the completion or failure to do a task. Through a 2x2 factorial design, results show; (1) threat (F(1,96) = 12.487, p < 0.05) and (2) surveillance (F(1,96)=9.942, p<.05) had a significant main effect on obedience, suggesting that the Dissonance Theory and Theory of Social Facilitation is respectively true in the study. On the other hand, (3) no interaction (F(1,96)=1.303, p > .05) was seen since threat and surveillance both have a main effect that could be positive or negative, or could be because of their complementary property as supported by the post-hoc results. Also, (4) most effective commanding style is threat and surveillance setting (M = 30.04, SD = 7.971) due to the significant main effect of the two variables. With this, in the Filipino Setting, threat and surveillance has proven to be a very effective strategy to discipline and induce obedience from a child.

Keywords: experimental study, obedience, preschool children, surveillance, threat

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Authors: Othmane Bouferroum, Malika Boudraa

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In this study, a dual exponential model for CV formant transition is derived from locus theory of speech perception. Then, an algorithm for automatic vowel and consonant’s target formant frequency detection is developed and tested on real speech. The results show that vowels and consonants are detected through transitions rather than their small stable portions. Also, vowel reduction is clearly observed in our data. These results are confirmed by the observations made in perceptual experiments in the literature.

Keywords: acoustic invariance, coarticulation, formant transition, locus equation

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Authors: John McDowall, Crysta Derham

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Broaden and build theory of emotion describes how experiencing positive emotions, such as happiness, broadens our ‘thought-action repertoire’ leading us to be more likely to go out and act on our positive emotions. This results in the building of new relationships, resources and skills, which we can draw on in times of need throughout life. In contrast, the experience of negative emotion is thought to narrow our ‘thought-action repertoire’, leading to specific actions to aid in survival. Three experiments aimed to explore the effect of briefly presented schematic faces (happy, sad, and neutral) on attentional scope using the flanker task. Based on the broaden and build theory it was hypothesised that there would be an increase in reaction time in trials primed with a happy face due to a broadening of attention, leading to increased flanker interference. A decrease in reaction time was predicted for trials primed with a sad face, due to a narrowing of attention leading to less flanker interference. Results lended partial support to the broaden and build hypothesis, with reaction times being slower following happy primes in incongruent flanker trials. Recent research is discussed in regards to potential mediators of the relationship between emotion and attention.

Keywords: emotion, attention, broaden and build, flanker task

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

Abstract:

It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Cheng Fang, Lingwei Quan, Cunyue Lu

Abstract:

Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.

Keywords: computer vision, pose estimation, pose tracking, Siamese network

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Authors: K. Biswas, U. H. Armin, S. M. J. Prodhan, J. A. Prithul, S. Sarker, F. Afrin

Abstract:

Alzheimer’s disease (AD) (a progressive neurodegenerative disorder) is mostly predominant cause of dementia in the elderly. Prolonging the function of acetylcholine by inhibiting both acetylcholinesterase and butyrylcholinesterase is most effective treatment therapy of AD. Traditionally Pterocarpus santalinus L. is widely known for its medicinal use. In this study, in vitro acetylcholinesterase inhibitory activity was investigated and methanolic extract of the plant showed significant activity. To confirm this activity (in vivo), learning and memory enhancing effects were tested in mice. For the test, memory impairment was induced by scopolamine (cholinergic muscarinic receptor antagonist). Anti-amnesic effect of the extract was investigated by the passive avoidance task in mice. The study also includes brain acetylcholinesterase activity. Results proved that scopolamine induced cognitive dysfunction was significantly decreased by administration of the extract solution, in the passive avoidance task and inhibited brain acetylcholinesterase activity. These results suggest that bark extract of Pterocarpus santalinus can be better option for further studies on AD via their acetylcholinesterase inhibitory actions.

Keywords: Pterocarpus santalinus, cholinesterase inhibitor, passive avoidance, Alzheimer’s disease

Procedia PDF Downloads 249

Authors: Carla Ferreira, Helena Barros

Abstract:

The paper termed “Design of reinforced concrete with Eurocode 2” presents the theory regarding the design of reinforced concrete sections and the development of the tables and abacuses to verify the concrete section to the ultimate limit and service limit states. This paper is a complement of it, showing how to use the previous tools. Different numerical results are shown, proving the capability of the methodology. When a section of a beam is already chosen, the computer program presents the reinforcing steel in many locations along the structure, and it is the engineer´s task to choose the layout available for the construction, considering the maximum regular kind of reinforcing bars. There are many computer programs available for this task, but the interest of the present kind of tools is the fast and easy way of making the design and choose the optimal solution. Another application of these design tools is in the definition of the section dimensions, in a way that when stresses are evaluated, the final design is acceptable. In the design offices, these are considered by the engineers a very quick and useful way of designing reinforced concrete sections, employing variable strength concrete and higher steel classes. Examples of nonlinear analyses and redistribution of the bending moment will be considered, according to the Eurocode 2 recommendations, for sections under bending moment and axial forces. Examples of the evaluation of the service limit state will be presented.

Keywords: design examples, eurocode 2, reinforced concrete, section design

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Authors: Hanan Al-Jabri

Abstract:

Simultaneous interpreting is deemed to be the most challenging mode of interpreting by many scholars. The special constraints involved in this task including time constraints, different linguistic systems, and stress pose a great challenge to most interpreters. These constraints are likely to maximise when the interpreting task is done live on TV. The TV interpreter is exposed to a wide variety of audiences with different backgrounds and needs and is mostly asked to interpret high profile tasks which raise his/her levels of stress, which further complicate the task. Under these constraints, which require fast and efficient performance, TV interpreters of four TV channels were asked to render Trump's victory speech into Arabic. However, they had also to deal with the burden of rendering English emotive overtones employed by the speaker into a whole different linguistic system. The current study aims at investigating the way TV interpreters, who worked in the simultaneous mode, handled this task; it aims at exploring and evaluating the TV interpreters’ linguistic choices and whether the original emotive effect was maintained, upgraded, downgraded or abandoned in their renditions. It also aims at exploring the possible difficulties and challenges that emerged during this process and might have influenced the interpreters’ linguistic choices. To achieve its aims, the study analysed Trump’s victory speech delivered on November 6, 2016, along with four Arabic simultaneous interpretations produced by four TV channels: Al-Jazeera, RT, CBC News, and France 24. The analysis of the study relied on two frameworks: a macro and a micro framework. The former presents an overview of the wider context of the English speech as well as an overview of the speaker and his political background to help understand the linguistic choices he made in the speech, and the latter framework investigates the linguistic tools which were employed by the speaker to stir people’s emotions. These tools were investigated based on Shamaa’s (1978) classification of emotive meaning according to their linguistic level: phonological, morphological, syntactic, and semantic and lexical levels. Moreover, this level investigates the patterns of rendition which were detected in the Arabic deliveries. The results of the study identified different rendition patterns in the Arabic deliveries, including parallel rendition, approximation, condensation, elaboration, transformation, expansion, generalisation, explicitation, paraphrase, and omission. The emerging patterns, as suggested by the analysis, were influenced by factors such as speedy and continuous delivery of some stretches, and highly-dense segments among other factors. The study aims to contribute to a better understanding of TV simultaneous interpreting between English and Arabic, as well as the practices of TV interpreters when rendering emotiveness especially that little is known about interpreting practices in the field of TV, particularly between Arabic and English.

Keywords: emotive overtones, interpreting strategies, political speeches, TV interpreting

Procedia PDF Downloads 159

Authors: Tian Zhang

Abstract:

Unsupervised object segmentation aims at segmenting objects in sequential images and obtaining the mask of each object without any manual intervention. Unsupervised segmentation remains a challenging task due to the lack of prior knowledge about these objects. Previous methods often require manually specifying the action of each object, which is often difficult to obtain. Instead, this paper does not need action information of objects and automatically learns the actions and relations among objects from the structured environment. To obtain the object segmentation of sequential images, the relationships between objects and images are extracted to infer the action and interaction of objects based on the multi-head attention mechanism. Three types of objects’ relationships in the object segmentation task are proposed: the relationship between objects in the same frame, the relationship between objects in two frames, and the relationship between objects and historical information. Based on these relationships, the proposed model (1) is effective in multiple objects segmentation tasks, (2) just needs images as input, and (3) produces better segmentation results as more relationships are considered. The experimental results on multiple datasets show that this paper’s method achieves state-of-art performance. The quantitative and qualitative analyses of the result are conducted. The proposed method could be easily extended to other similar applications.

Keywords: unsupervised object segmentation, attention mechanism, contrastive learning, structured environment

Procedia PDF Downloads 109