Search results for: modulation recognition

Authors: Sonali Banrjee, Swarup Kumar Mitra, Aritra Acharyya

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A piano note recognition method has been proposed by the authors in this paper. The authors have used a comprehensive method for onset detection of each note present in a piano piece followed by segmented short-time Fourier transform (STFT) for the identification of piano notes. The performance evaluation of the proposed method has been carried out in different harsh noisy environments by adding different levels of additive white Gaussian noise (AWGN) having different signal-to-noise ratio (SNR) in the original signal and evaluating the note detection error rate (NDER) of different piano pieces consisting of different number of notes at different SNR levels. The NDER is found to be remained within 15% for all piano pieces under consideration when the SNR is kept above 8 dB.

Keywords: AWGN, onset detection, piano note, STFT

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Authors: Denise Schrama, Claudia Raposo, Pedro Rodrigues

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Background: Food allergies are conducted by a hypersensitive response of the immune system. These allergies are a global concern for the public health. Consumption of fish is increasing worldwide as it is a healthy meat with high nutritional value. Unfortunately, fish can cause adverse immune-mediate reactions, affecting part of the population with higher incidence in children. β-parvalbumin, a small, highly conserved stable, calcium or magnesium binding muscle protein is the main fish allergen. In fish-allergic patients, cross-reactivity between different fish species exist due to recognition of highly identical protein regions. Enolases, aldolases, or fish gelatin are other identified fish allergens in some fish species. With no available cure for fish allergies, clinical management is only based on an avoidance diet aiming at the total exclusion of offending food. Methods: Mediterranean fish (S. aurata and D. labrax) were fed specifically designed diets, enriched in components that target the expression or inactivation of parvalbumin (creatine and EDTA, respectively). After 90 days fish were sampled and biological tissues were excised. Proteomics was used to access fish allergens characterization and expression in muscle while IgE assays to confirm the lower allergenic potential are conducted in patients with history of fish allergies. Fish welfare and quality of flesh were established with biochemical, texture and sensorial analysis. Results: Fish welfare shows no major impact between diets. In case of creatine supplementation in D. labrax proteomic analysis show a slight decrease in parvalbumin expression. No accumulation of this compound was found in muscle. For EDTA supplementation in S. aurata IgE assay show a slight decrease in allergenicity when using sera of fish allergic patients. Conclusion: Supplementation with these two compounds seems to change slightly the allergenicity of the two mean Mediterranean species.

Keywords: fish allergies, fish nutrition, proteomics, aquaculture

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Authors: Sara Hasani

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This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce.

Keywords: disaster management, natural disaster, pattern recognition, prediction

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Authors: Hong Im Shin

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Previous research demonstrated that self-objectification (women view themselves as objects for use) is related to system-justification. Three studies investigated whether cultural disposition as its system-justifying function could have an impact on self-objectification and dehumanization of sexualized women and men. Study 1 (N = 91) employed a survey methodology to examine the relationship between cultural disposition (collectivism vs. individualism), trait of system-justification, and self-objectification. The results showed that the higher tendency of collectivism was related to stronger system-justification and self-objectification. Study 2 (N = 60 females) introduced a single category implicit association task (SC-IAT) to assess the extent to which sexually objectified women were associated with uniquely human attributes (i.e., culture) compared to animal-related attributes (i.e., nature). According to results, female participants associated sexually objectified female targets less with human attributes compared to animal-related attributes. Study 3 (N = 46) investigated whether priming to individualism or collectivism was associated to system justification and sexual objectification of men and women with the use of a recognition task involving upright and inverted pictures of sexualized women and men. The results indicated that the female participants primed to individualism showed an inversion effect for sexualized women and men (person-like recognition), whereas there was no inversion effect for sexualized women in the priming condition of collectivism (object-like recognition). This implies that cultural disposition plays a mediating role for rationalizing the gender status, implicit dehumanization of sexualized females and self-objectification. Future research directions are discussed.

Keywords: cultural disposition, dehumanization, implicit test, self-objectification

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Hadri Hussain, M.Nasir Ibrahim, Chee-Ming Ting, Mariani Idroas, Fuad Numan, Alias Mohd Noor

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A biometric security system has become an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioural or physiological information for authentication purposes. The behavioural biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram (ECG) and phonocardiogram or heart sound (HS)). The speech signal is commonly used in a recognition system in biometric, while the ECG and the HS have been used to identify a person’s diseases uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack that will affect the performance of the system. Therefore, a multimodal biometric security system is developed, which is based on biometric signal of ECG, HS, and speech. The biosignal data involved in the biometric system is initially segmented, with each segment Mel Frequency Cepstral Coefficients (MFCC) method is exploited for extracting the feature. The Hidden Markov Model (HMM) is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as client identification (CID). In this project, twenty clients are tested with the developed system. The best overall performance at 44 kHz was 93.92% for ECG and the worst overall performance was ECG at 88.47%. The results were compared to the best overall performance at 44 kHz for (20clients) to increment of clients, which was 90.00% for HS and the worst overall performance falls at ECG at 79.91%. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher frequency sampling, the performance still decreased slightly as predicted.

Keywords: electrocardiogram, phonocardiogram, hidden markov model, mel frequency cepstral coeffiecients, client identification

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Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

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Authors: Saad Alkentar, Abdulkareem Assalem

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Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points).

Keywords: small targets, drones, trajectory information, TBD, multivariate time series

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Authors: LuoJiaoyang, Yu Hongyang

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In this paper, we experimented with a new approach to multimodal identification using RGB, RGB-D and voice data. The multimodal combination of RGB and voice data has been applied in tasks such as emotion recognition and has shown good results and stability, and it is also the same in identity recognition tasks. We believe that the data of different modalities can enhance the effect of the model through mutual reinforcement. We try to increase the three modalities on the basis of the dual modalities and try to improve the effectiveness of the network by increasing the number of modalities. We also implemented the single-modal identification system separately, tested the data of these different modalities under clean and noisy conditions, and compared the performance with the multimodal model. In the process of designing the multimodal model, we tried a variety of different fusion strategies and finally chose the fusion method with the best performance. The experimental results show that the performance of the multimodal system is better than that of the single modality, especially in dealing with noise, and the multimodal system can achieve an average improvement of 5%.

Keywords: multimodal, three modalities, RGB-D, identity verification

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Authors: Agoston Restas

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Introduction: In case of managing disasters decision makers can face many times such a special situation where any pre-sign of the drastically change is missing therefore the improvised decision making can be required. The complexity, ambiguity, uncertainty or the volatility of the situation can require many times the improvisation as decision making. It can be taken at any level of the management (strategic, operational and tactical) but at tactical level the main reason of the improvisation is surely time pressure. It is certainly the biggest problem during the management. Methods: The author used different tools and methods to achieve his goals; one of them was the study of the relevant literature, the other one was his own experience as a firefighting manager. Other results come from two surveys that are referred to; one of them was an essay analysis, the second one was a word association test, specially created for the research. Results and discussion: This article proves that, in certain situations, the multi-criteria, evaluating decision-making processes simply cannot be used or only in a limited manner. However, it can be seen that managers, directors or commanders are many times in situations that simply cannot be ignored when making decisions which should be made in a short time. The functional background of decisions made in a short time, their mechanism, which is different from the conventional, was studied lately and this special decision procedure was given the name recognition-primed decision. In the article, author illustrates the limits of the possibilities of analytical decision-making, presents the general operating mechanism of recognition-primed decision-making, elaborates on its special model relevant to managers at tactical level, as well as explore and systemize the factors that facilitate (catalyze) the processes with an example with fire managers.

Keywords: decision making, disaster managers, recognition primed decision, model for making decisions in emergencies

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Manutchanok Jongprasithporn, Rawiphorn Srivilai, Paweena Pongsopha

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Fall is the most serious accident leading to increased unintentional injuries and mortality. Falls are not only the cause of suffering and functional impairments to the individuals, but also the cause of increasing medical cost and days away from work. The early detection of falls could be an advantage to reduce fall-related injuries and consequences of falls. Smartphones, embedded accelerometer, have become a common device in everyday life due to decreasing technology cost. This paper explores a physical activity monitoring and fall detection application in smartphones which is a non-invasive biomedical device to determine physical activities and fall event. The combination of application and sensors could perform as a biomedical sensor to monitor physical activities and recognize a fall. We have chosen Android-based smartphone in this study since android operating system is an open-source and no cost. Moreover, android phone users become a majority of Thai’s smartphone users. We developed Thai 3 Axis (TH3AX) as a physical activities and fall detection application which included command, manual, results in Thai language. The smartphone was attached to right hip of 10 young, healthy adult subjects (5 males, 5 females; aged< 35y) to collect accelerometer and gyroscope data during performing physical activities (e.g., walking, running, sitting, and lying down) and falling to determine threshold for each activity. Dependent variables are including accelerometer data (acceleration, peak acceleration, average resultant acceleration, and time between peak acceleration). A repeated measures ANOVA was performed to test whether there are any differences between DVs’ means. Statistical analyses were considered significant at p<0.05. After finding threshold, the results were used as training data for a predictive model of activity recognition. In the future, accuracies of activity recognition will be performed to assess the overall performance of the classifier. Moreover, to help improve the quality of life, our system will be implemented with patients and elderly people who need intensive care in hospitals and nursing homes in Thailand.

Keywords: activity recognition, accelerometer, fall, gyroscope, smartphone

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Daniel Botero, Oscar Rubiano, Pedro P. Barragan, Jaime Baron, Leonardo Rodriguez Perdomo, Jaime Rodriguez

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In the last decade interval training (IT) has gained importance when is compare with strength training (ST). However, there are few studies analyzing the impact of these training over the autonomic nervous system (ANS). This work has aimed to compare the activity of the autonomic nervous system, when is expose to an IT or ST indoor cycling mode. After approval by the ethics committee, a cross-over clinical trial with 22 healthy participants (age 21 ± 3 years) was implemented. The selection of participants for the groups with sequence force-interval (F-I) and interval-force (I-F) was made randomly with assignation of 11 participants for each group. The temporal series of heart rate was obtained before and after each training using the POLAR TEAM® heart monitor. The evaluation of the ANS was performed with spectral analysis of the heart rate variability (HRV) using the fast Fourier transform (Kubios software). A training of 8 weeks in each sequence (4 weeks with each training) with an intermediate period of two weeks of washout was implemented for each group. The power parameter of the HRV in the low frequency band (LF = 0.04-0.15Hz related to the sympathetic nervous system), high frequency (HF = 0.15-0.4Hz, related to the parasympathetic) and LF/HF (with reference to a modulation of parasympathetic over the sympathetic), were calculated. Afterward, the difference between the parameters before and after was realized. Then, to evaluate statistical differences between each training was implemented the method of Wellek (Wellek and Blettner, 2012, Medicine, 109 (15), 276-81). To determine the difference of effect over parasympathetic when FT and IT are used, the T test is implemented obtaining a T value of 0.73 with p-value ≤ 0.1. For the sympathetic was obtained a T of 0.33 with p ≤ 0.1 and for LF/HF the T was 1.44 with a p ≥ 0.1. Then, the carry over effect was evaluated and was not present. Significant changes over autonomic activity with strength or interval training were not observed. However, a modulation of the parasympathetic over the sympathetic can be observed. Probably, these findings should be explained because the sample is little and/or the time of training was insufficient to generate changes.

Keywords: autonomic nervous, force training, indoor cycling, interval training

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Authors: Insiya Bhalloo

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It is quite common in the Muslim faith for non-Arabic speakers to be able to convert written Arabic, especially Quranic Arabic, into a phonological code without significant semantic or syntactic knowledge. This is due to prior experience learning to read the Quran (a religious text written in Classical Arabic), from a very young age such as via enrolment in Quranic Arabic classes. As compared to native speakers of Arabic, these Arabic readers do not have a comprehensive morpho-syntactic knowledge of the Arabic language, nor can understand, or engage in Arabic conversation. The study seeks to investigate whether mere phonological experience (as indicated by the Arabic readers’ experience with Arabic phonology and the sound-system) is sufficient to cause phonological-interference during word recognition of previously-heard words, despite the participants’ non-native status. Both native speakers of Arabic and non-native speakers of Arabic, i.e., those individuals that learned to read the Quran from a young age, will be recruited. Each experimental session will include two phases: An exposure phase and a test phase. During the exposure phase, participants will be presented with Arabic words (n=40) on a computer screen. Half of these words will be common words found in the Quran while the other half will be words commonly found in Modern Standard Arabic (MSA) but either non-existent or prevalent at a significantly lower frequency within the Quran. During the test phase, participants will then be presented with both familiar (n = 20; i.e., those words presented during the exposure phase) and novel Arabic words (n = 20; i.e., words not presented during the exposure phase. ½ of these presented words will be common Quranic Arabic words and the other ½ will be common MSA words but not Quranic words. Moreover, ½ the Quranic Arabic and MSA words presented will be comprised of nouns, while ½ the Quranic Arabic and MSA will be comprised of verbs, thereby eliminating word-processing issues affected by lexical category. Participants will then determine if they had seen that word during the exposure phase. This study seeks to investigate whether long-term phonological memory, such as via childhood exposure to Quranic Arabic orthography, has a differential effect on the word-recognition capacities of native Arabic speakers and Arabic readers; we seek to compare the effects of long-term phonological memory in comparison to short-term phonological exposure (as indicated by the presentation of familiar words from the exposure phase). The researcher’s hypothesis is that, despite the lack of lexical knowledge, early experience with converting written Quranic Arabic text into a phonological code will help participants recall the familiar Quranic words that appeared during the exposure phase more accurately than those that were not presented during the exposure phase. Moreover, it is anticipated that the non-native Arabic readers will also report more false alarms to the unfamiliar Quranic words, due to early childhood phonological exposure to Quranic Arabic script - thereby causing false phonological facilitatory effects.

Keywords: modern standard arabic, phonological facilitation, phonological memory, Quranic arabic, word recognition

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Thauane Silva, Gabrielle do Vale, Andre Ferreira, Marilda Siqueira, Thiago Moreno L. Souza, Milene D. Miranda

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The exposure of A(H1N1)pdm09-infected epithelial cells (HeLa) to HIV-1 viral particles, or its gp120, enhanced interferon-induced transmembrane protein (IFITM3) content, a viral restriction factor (RF), resulting in a decrease in influenza replication. The gp120 binds to CCR5 (R5) or CXCR4 (X4) cell receptors during HIV-1 infection. Then, it is possible that the endogenous ligands of these receptors also modulate the expression of IFITM3 and other cellular factors that restrict influenza virus replication. Thus, the aim of this study is to analyze the role of cellular receptors R5 and X4 in modulating RFs in order to inhibit the replication of the influenza virus. A549 cells were treated with 2x effective dose (ED50) of endogenous R5 or X4 receptor agonists, CCL3 (20 ng/ml), CCL4 (10 ng/ml), CCL5 (10 ng/ml) and CXCL12 (100 ng/mL) or exogenous agonists, gp120 Bal-R5, gp120 IIIB-X4 and its mutants (5 µg/mL). The interferon α (10 ng/mL) and oseltamivir (60 nM) were used as a control. After 24 h post agonists exposure, the cells were infected with virus influenza A(H3N2) at 2 MOI (multiplicity of infection) for 1 h. Then, 24 h post infection, the supernatant was harvested and, the viral titre was evaluated by qRT-PCR. To evaluate IFITM3 and SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1) protein levels, A549 were exposed to agonists for 24 h, and the monolayer was lysed with Laemmli buffer for western blot (WB) assay or fixed for indirect immunofluorescence (IFI) assay. In addition to this, we analyzed other RFs modulation in A549, after 24 h post agonists exposure by customized RT² Profiler Polymerase Chain Reaction Array. We also performed a functional assay in which SAMHD1-knocked-down, by single-stranded RNA (siRNA), A549 cells were infected with A(H3N2). In addition, the cells were treated with guanosine to assess the regulatory role of dNTPs by SAMHD1. We found that R5 and X4 agonists inhibited influenza replication in 54 ± 9%. We observed a four-fold increase in SAMHD1 transcripts by RFs mRNA quantification panel. After 24 h post agonists exposure, we did not observe an increase in IFITM3 protein levels through WB or IFI assays, but we observed an upregulation up to three-fold in the protein content of SAMHD1, in A549 exposed to agonists. Besides this, influenza replication enhanced in 20% in cell cultures that SAMDH1 was knockdown. Guanosine treatment in cells exposed to R5 ligands further inhibited influenza virus replication, suggesting that the inhibitory mechanism may involve the activation of the SAMHD1 deoxynucleotide triphosphohydrolase activity. Thus, our data show for the first time a direct relationship of SAMHD1 and inhibition of influenza replication, and provides perspectives for new studies on the signaling modulation, through cellular receptors, to induce proteins of great importance in the control of relevant infections for public health.

Keywords: chemokine receptors, gp120, influenza, virus restriction factors

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Authors: Takanori Tanaka, Daisuke Kitao, Daisuke Ikeda

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The aurora is an attractive phenomenon but it is difficult to understand the whole mechanism of it. An approach of data-intensive science might be an effective approach to elucidate such a difficult phenomenon. To do that we need labeled data, which shows when and what types of auroras, have appeared. In this paper, we propose an image retrieval system for auroral all-sky images, some of which include discrete and diffuse aurora, and the other do not any aurora. The proposed system retrieves images which are similar to the query image by using a popular image recognition method. Using 300 all-sky images obtained at Tromso Norway, we evaluate two methods of image recognition methods with or without our original color filtering method. The best performance is achieved when SIFT with the color filtering is used and its accuracy is 81.7% for discrete auroras and 86.7% for diffuse auroras.

Keywords: data-intensive science, image classification, content-based image retrieval, aurora

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Authors: Javier Comes Fayos, Isabel RodríGuez Moreno, Sara Bressanutti, Marisol Lila, Angel Romero MartíNez, Luis Moya Albiol

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Given the great impact produced by gender-based violence, its comprehensive approach seems essential. Consequently, research has focused on risk factors for violent behaviour, linking various psychosocial variables, as well as cognitive and neuropsychological deficits with the aggressors. However, studies on affective processing are scarce, so the present study investigates possible emotional alterations in men convicted of gender violence. The participants were 51 aggressors, who attended the CONTEXTO program with sentences of less than two years, and 47 men with no history of violence. The sample did not differ in age, socioeconomic level, education, or alcohol and other substances consumption. Anger, alexithymia and facial recognition of other people´s emotions were assessed through the State-Trait Anger Expression Inventory (STAXI-2), the Toronto Alexithymia Scale (TAS-20) and Reading the mind in the eyes (REM), respectively. Men convicted of gender-based violence showed higher scores on the anger trait and temperament dimensions, as well as on the anger expression index. They also scored higher on alexithymia and in the identification and emotional expression subscales. In addition, they showed greater difficulties in the facial recognition of emotions by having a lower score in the REM. These results seem to show difficulties in different affective areas in men condemned for gender violence. The deficits are reflected in greater difficulty in identifying and expressing emotions, in processing anger and in recognizing the emotions of others. All these difficulties have been related to the use of violent behavior. Consequently, it is essential and necessary to include emotional regulation in intervention programs for men who have been convicted of gender-based violence.

Keywords: alexithymia, anger, emotional processing, emotional recognition, empathy, intimate partner violence

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Authors: Lívia Pimentel Sant'ana Dourado, Raquel Das Neves Almeida, Luís Henrique Costa Corrêa Neto, Nayara Soares, Kelly Grace Magalhães

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Introduction: Obesity and high-fat diet intake have a crucial impact on immune cells and inflammatory profile, highlighting an emerging realization that obesity is an inflammatory disease. In the present work, we aimed to characterize the role of caspase-1/11 and NLRP3 inflammasome in the establishment of mice obesity and modulation of inflammatory lipid metabolism induced by high fat diet intake. Methods and results: Wild type, caspase-1/11 and NLRP3 knockout mice were fed with standard fat diet (SFD) or high fat diet (HFD) for 90 days. The weight of animals was measured weekly to monitor the weight gain. After 90 days, the blood, peritoneal lavage cells, heart and liver were collected from mice studied here. Cytokines were measured in serum by ELISA and analyzed in spectrophotometry. Lipid antigen presentation molecule CD1d expression, reactive oxygen species (ROS) generation and lipid droplets biogenesis were analyzed in cells from mice peritoneal cavity by flow cytometry. Liver histopathology was performed for morphological evaluation of the organ. The absence of caspase-1/11, but not NLRP3, in mice fed with HFD favored the mice weight gain, increased liver size, induced development of hepatic steatosis and IL-12 secretion in mice compared to mice fed with SFD. In addition, caspase-1/11 knockout mice fed with HFD presented an increased CD1d molecule expression, as well as higher levels of lipid droplets biogenesis and ROS generation compared to wild type mice also fed with HFD. Conclusion: Our data suggest that caspase-1/11 knockout mice have greater susceptibility to obesity as well as increased activation of lipid metabolism and inflammatory markers.

Keywords: caspase 1, caspase 11, inflamassome, obesity, lipids

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Authors: Komal Ghafoor, Tauqir Ahmad, Murtaza Hanif, Hira Zaheer

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Assistive technology has been developed to support visually impaired people in their social interactions. Conversation assistive technology is designed to enhance communication skills, facilitate social interaction, and improve the quality of life of visually impaired individuals. This technology includes speech recognition, text-to-speech features, and other communication devices that enable users to communicate with others in real time. The technology uses natural language processing and machine learning algorithms to analyze spoken language and provide appropriate responses. It also includes features such as voice commands and audio feedback to provide users with a more immersive experience. These technologies have been shown to increase the confidence and independence of visually impaired individuals in social situations and have the potential to improve their social skills and relationships with others. Overall, conversation-assistive technology is a promising tool for empowering visually impaired people and improving their social interactions. One of the key benefits of conversation-assistive technology is that it allows visually impaired individuals to overcome communication barriers that they may face in social situations. It can help them to communicate more effectively with friends, family, and colleagues, as well as strangers in public spaces. By providing a more seamless and natural way to communicate, this technology can help to reduce feelings of isolation and improve overall quality of life. The main objective of this research is to give blind users the capability to move around in unfamiliar environments through a user-friendly device by face, object, and activity recognition system. This model evaluates the accuracy of activity recognition. This device captures the front view of the blind, detects the objects, recognizes the activities, and answers the blind query. It is implemented using the front view of the camera. The local dataset is collected that includes different 1st-person human activities. The results obtained are the identification of the activities that the VGG-16 model was trained on, where Hugging, Shaking Hands, Talking, Walking, Waving video, etc.

Keywords: dataset, visually impaired person, natural language process, human activity recognition

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Authors: Rodrigo C. Belleza, Jr., Roselyn A. Maaño, Karl Patrick E. Camota, Darwin Kim Q. Bulawan

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Eyes are an essential and conspicuous organ of the human body. Human eyes are outward and inward portals of the body that allows to see the outside world and provides glimpses into ones inner thoughts and feelings. Inevitable blindness and visual impairments may result from eye-related disease, trauma, or congenital or degenerative conditions that cannot be corrected by conventional means. The study emphasizes innovative tools that will serve as an aid to the blind and visually impaired (VI) individuals. The researchers fabricated a prototype that utilizes the Microsoft Kinect for Windows and Arduino microcontroller board. The prototype facilitates advanced gesture recognition, voice recognition, obstacle detection and indoor environment navigation. Open Computer Vision (OpenCV) performs image analysis, and gesture tracking to transform Kinect data to the desired output. A computer vision technology device provides greater accessibility for those with vision impairments.

Keywords: algorithms, blind, computer vision, embedded systems, image analysis

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Authors: Juliana Barcelos Cordeiro, Khashayar Mahani, Farbod Farzan, Mohsen A. Jafari

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Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Keywords: energy consumption forecasting, energy efficiency, load disaggregation, pattern recognition approach

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Authors: Matthias Nnadi

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Using samples of 250 large listed firms by market capitalization in China and Hong Kong, we conducted empirical test to determine the impact of regulatory environment on reporting quality following IFRS convergence using three financial reporting measures; earning management, timely loss recognition and value relevance. Our results indicate that accounting data are more value relevant for Hong Kong listed firms than the Chinese A-share firms. The empirical results for timely loss recognition further reveal that there is a larger coefficient estimate on bad news earnings, which suggests that Chines A-share firms are more likely to report losses in a timely manner. The results support the evidence that substantial convergence of IFRS can improve financial reporting quality in a regulated environment such as China. This further supports the expectation that IFRS are relevant to China and has positive effect on its accounting practice and quality.

Keywords: reporting, quality, earning, loss, relevance, financial, China, Hong Kong

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Authors: Nazif Fuat Turkmen

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Domestic violence is the most common form of violence that risks the health and psychological well-being of victims and its witnesses. Psychology as a scientific field has made contributions in research, exploration, assessment, intervention, and prevention of domestic violence. The present study will be exploring the level of recognition of Turkish Cypriots on domestic violence and their understanding about it in general terms. While discussing the level of awareness of Turkish Cypriots on domestic violence and the effects of this level of awareness on the general well-being of the members of the society, the most common types of domestic violence as well as how Turkish Cypriots recognize and interpret these different types will be explored. The participants consisted of 224 Turkish Cypriots; 48.4% (n= 109) were female, 51.1% (n=115) were male. For the purpose of the study, a 28-item questionnaire was prepared and used for data collection. According to the results, there is a strong relationship between the education level of the respondents and their awareness on domestic violence. The study shows that cultural approaches on child rearing effect people’s recognition of violence in general and awareness on domestic violence in particular.

Keywords: culture, domestic violence, health psychology, public health, Turkish Cypriots, violence

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Authors: Haiqiao Du, Qian Wang, Shuwei Wang, Jianan Li

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Purpose: The aim is to evaluate the effect of cochlear implantation (CI) in patients with unilateral hearing loss, with a view to providing data support for the selection of therapeutic interventions for patients with single-sided deafness (SSD)/asymmetric hearing loss (AHL) and the broadening of the indications for CI. Methods: The study subjects were patients with unilateral hearing loss who underwent cochlear implantation surgery in our hospital in August 2022 and were willing to cooperate with the test and were divided into 2 groups: SSD group and AHL group. The enrolled patients were followed up for hearing level, tinnitus changes, speech recognition ability, sound source localization ability, and quality of life at five-time points: preoperatively, and 1, 3, 6, and 12 months after postoperative start-up. Results: As of June 30, 2024, a total of nine patients completed follow-up, including four in the SSD group and five in the AHL group. The mean postoperative hearing aid thresholds on the CI side were 31.56 dB HL and 34.75 dB HL in the two groups, respectively. Of the four patients with preoperative tinnitus symptoms (three patients in the SSD group and one patient in the AHL group), all showed a degree of reduction in Tinnitus Handicap Inventory (THI) scores, except for one patient who showed no change. In both the SSD and AHL groups, the sound source localization results (expressed as RMS error values, with smaller values indicating better ability) were 66.87° and 77.41° preoperatively and 29.34° and 54.60° 12 months after postoperative start-up, respectively, which showed that the ability to localize the sound source improved significantly with longer implantation time. The level of speech recognition was assessed by 3 test methods: speech recognition rate of monosyllabic words in a quiet environment and speech recognition rate of different sound source directions at 0° and 90° (implantation side) in a noisy environment. The results of the 3 tests were 99.0%, 72.0%, and 36.0% in the preoperative SSD group and 96.0%, 83.6%, and 73.8% in the AHL group, respectively, whereas they fluctuated in the postoperative period 3 months after start-up, and stabilized at 12 months after start-up to 99.0%, 100.0%, and 100.0% in the SSD group and 99.5%, 96.0%, and 99.0%. Quality of life was subjectively evaluated by three tests: the Speech Spatial Quality of Sound Auditory Scale (SSQ-12), the Quality-of-Life Bilateral Listening Questionnaire (QLBHE), and the Nijmegen Cochlear Implantation Inventory (NCIQ). The results of the SSQ-12 (with a 10-point score out of 10) showed that the scores of preoperative and postoperative 12 months after start-up were 6.35 and 6.46 in the SSD group, while they were 5.61 and 9.83 in the AHL group. The QLBHE scores (100 points out of 100) were 61.0 and 76.0 in the SSD group and 53.4 and 63.7 in the AHL group for the preoperative versus the postoperative 12 months after start-up. Conclusion: Patients with unilateral hearing loss can benefit from cochlear implantation: CI implantation is effective in compensating for the hearing on the affected side and reduces the accompanying tinnitus symptoms; there is a significant improvement in sound source localization and speech recognition in the presence of noise; and the quality of life is improved.

Keywords: single-sided deafness, asymmetric hearing loss, cochlear implant, unilateral hearing loss

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Authors: Alexander N. Savostyanov, Tatyana A. Dolgorukova, Elena A. Esipenko, Mikhail S. Zaleshin, Margherita Malanchini, Anna V. Budakova, Alexander E. Saprygin, Yulia V. Kovas

Abstract:

The present study compared spectral-power indexes and cortical topography of brain activity in a sample characterized by different levels of trait and mathematical anxiety. 52 healthy Russian-speakers (age 17-32; 30 males) participated in the study. Participants solved an error recognition task under 3 conditions: A lexical condition (simple sentences in Russian), and two numerical conditions (simple arithmetic and complicated algebraic problems). Trait and mathematical anxiety were measured using self-repot questionnaires. EEG activity was recorded simultaneously during task execution. Event-related spectral perturbations (ERSP) were used to analyze spectral-power changes in brain activity. Additionally, sLORETA was applied in order to localize the sources of brain activity. When exploring EEG activity recorded after tasks onset during lexical conditions, sLORETA revealed increased activation in frontal and left temporal cortical areas, mainly in the alpha/beta frequency ranges. When examining the EEG activity recorded after task onset during arithmetic and algebraic conditions, additional activation in delta/theta band in the right parietal cortex was observed. The ERSP plots reveled alpha/beta desynchronizations within a 500-3000 ms interval after task onset and slow-wave synchronization within an interval of 150-350 ms. Amplitudes of these intervals reflected the accuracy of error recognition, and were differently associated with the three (lexical, arithmetic and algebraic) conditions. The level of trait anxiety was positively correlated with the amplitude of alpha/beta desynchronization. The level of mathematical anxiety was negatively correlated with the amplitude of theta synchronization and of alpha/beta desynchronization. Overall, trait anxiety was related with an increase in brain activation during task execution, whereas mathematical anxiety was associated with increased inhibitory-related activity. We gratefully acknowledge the support from the №11.G34.31.0043 grant from the Government of the Russian Federation.

Keywords: anxiety, EEG, lexical and numerical error-recognition tasks, alpha/beta desynchronization

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