Search results for: voice recognition

Authors: Joe Voshall, Leigha Shoup

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In 2022, the Ohio Peace Officer Training Council and the Attorney General required officers to complete a minimum of 24 hours of continued professional training for the year. Much of the training was based on Mental Health or similarly related topics. This includes Officer Wellness and Officer Mental Health. It is becoming clearer that the stigma of Officer / First Responder Mental Health is a topic that is becoming more prevalently faced. To assist officers and first responders in facing mental health issues, we are developing new training. This training will aid in recognizing mental health-related issues in officers/first responders and citizens, as well as further using the same information to better respond and interact with one another and the public. In general, society has many varying views of mental health, much of which is largely over-sensationalized by television, movies, and other forms of entertainment. There has also been a stigma in law enforcement / first responders related to mental health and being weak as a result of on-the-job-related trauma-induced struggles. It is our hope this new training will assist officers and first responders in not only positively facing and addressing their mental health but using their own experience and education to recognize signs and symptoms of mental health within individuals in the community. Further, we hope that through this recognition, officers and first responders can use their experiences and more in-depth understanding to better interact within the field and with the public. Through recognition and better understanding of mental health issues and more positive interaction with the public, additional achievements are likely to result. This includes in the removal of bias and stigma for everyone.

Keywords: law enforcement, mental health, officer related mental health, trauma

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Authors: Prabhvir Singh Marway, Kai Lok Chan, Maria-Ruxandra Jinga, Rachel Bok Ying Lee, Matthew Bok Kit Lee, Krishan Nandapalan, Sze Yi Beh, Harry Carr, Christopher Kui

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We piloted a structured series of online workshops on the 3D segmentation of anatomical structures from CT scans. 33 participants were recruited from four UK universities for two-day workshops between 2020 and 2021. Open-source software (3D-Slicer) was used. We hypothesized that active participation via real-time screen-sharing and voice-communication via Discord would enable improved engagement and learning, despite national lockdowns. Written feedback indicated positive learning experiences, with subjective measures of anatomical understanding and software confidence improving.

Keywords: medical education, workshop, segmentation, anatomy

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Authors: Kirsten A. Bartels

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There are a number of similarities between survivor literature and Holocaust fiction for children and young adults. The paper explores three facets of the parallels of recognition found specifically between Livia Bitton-Jackson’s memoir of her experience during the Holocaust as an inmate in Auschwitz, I Have Lived a Thousand Years (1999) and Morris Glietzman series of Holocaust fiction. While Bitton-Jackson reflects on her past and Glietzman designs a fictive character, both are judicious with what they are willing to impart, only providing information about their appearance or themselves when it impacts others or when it serves a necessary purpose to the story. Another similarity lies in another critical aspect of many works of Holocaust literature – the idea of being ‘representatively Jewish’. The authors come to this idea from different angles, perhaps best explained as the difference between showing and telling, for Bitton-Jackson provides personal details, and Gleitzman constructed Felix arguably with this idea in mind. Interwoven through their journeys is a shift in perspectives on being recognized -- from wanting to be seen as individuals to being seen as Jew. With this, being Jewish takes on different meaning, both youths struggle with being labeled as something they do not truly understand, and may have not truly identified with, from a label, to a death warrant. With survivor literature viewed as the most credible and worthwhile type of Holocaust literature and Holocaust fiction is often seen as the least (with children’s and young-adult being the lowest form) the similarities in approaches to telling the stories may go overlooked or be undervalued. This paper serves as an exploration in the some of parallel messages shared between the two.

Keywords: holocaust fiction, Holocaust literature, representatively Jewish, survivor literature

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Authors: Shoury Gupta

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Chatbots are a rapidly growing technological trend that has revolutionized the way businesses interact with their customers. With the advancements in artificial intelligence, chatbots can now mimic human-like conversations and provide instant and efficient responses to customer inquiries. In this research paper, we aim to explore the implications of chatbots on the future of globalization for both businesses and consumers. The paper begins by providing an overview of the current state of chatbots in the global market and their growth potential in the future. The focus is on how chatbots have become a valuable tool for businesses looking to expand their global reach, especially in areas with high population density and language barriers. With chatbots, businesses can engage with customers in different languages and provide 24/7 customer service support, creating a more accessible and convenient customer experience. The paper then examines the impact of chatbots on cross-cultural communication and how they can help bridge communication gaps between businesses and consumers from different cultural backgrounds. Chatbots can potentially facilitate cross-cultural communication by offering real-time translations, voice recognition, and other innovative features that can help users communicate effectively across different languages and cultures. By providing more accessible and inclusive communication channels, chatbots can help businesses reach new markets and expand their customer base, making them more competitive in the global market. However, the paper also acknowledges that there are potential drawbacks associated with chatbots. For instance, chatbots may not be able to address complex customer inquiries that require human input. Additionally, chatbots may perpetuate biases if they are programmed with certain stereotypes or assumptions about different cultures. These drawbacks may have significant implications for businesses and consumers alike. To explore the implications of chatbots on the future of globalization in greater detail, the paper provides a thorough review of existing literature and case studies. The review covers topics such as the benefits of chatbots for businesses and consumers, the potential drawbacks of chatbots, and how businesses can mitigate any risks associated with chatbot use. The paper also discusses the ethical considerations associated with chatbot use, such as privacy concerns and the need to ensure that chatbots do not discriminate against certain groups of people. The ethical implications of chatbots are particularly important given the potential for chatbots to be used in sensitive areas such as healthcare and financial services. Overall, this research paper provides a comprehensive analysis of chatbots and their implications for the future of globalization. By exploring both the potential benefits and drawbacks of chatbot use, the paper aims to provide insights into how businesses and consumers can leverage this technology to achieve greater global reach and improve cross-cultural communication. Ultimately, the paper concludes that chatbots have the potential to be a powerful tool for businesses looking to expand their global footprint and improve their customer experience, but that care must be taken to mitigate any risks associated with their use.

Keywords: chatbots, conversational AI, globalization, businesses

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Vladimir Kulyukin, Tanwir Zaman, Sarat Kiran Andhavarapu

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Research on nutrition label use identifies four factors that impede comprehension and retention of nutrition information by consumers: label’s location on the package, presentation of information within the label, label’s surface size, and surrounding visual clutter. In this paper, a system is presented that makes nutrition label use more effective for nutrition information comprehension and retention. The system’s front end is a smartphone application. The system’s back end is a four node Linux cluster for image recognition and data storage. Image frames captured on the smartphone are sent to the back end for skewed or aligned barcode recognition. When barcodes are recognized, corresponding nutrition labels are retrieved from a cloud database and presented to the user on the smartphone’s touchscreen. Each displayed nutrition label is positioned centrally on the touchscreen with no surrounding visual clutter. Wikipedia links to important nutrition terms are embedded to improve comprehension and retention of nutrition information. Standard touch gestures (e.g., zoom in/out) available on mainstream smartphones are used to manipulate the label’s surface size. The nutrition label database currently includes 200,000 nutrition labels compiled from public web sites by a custom crawler. Stress test experiments with the node cluster are presented. Implications for proactive nutrition management and food policy are discussed.

Keywords: mobile computing, cloud computing, nutrition label use, nutrition management, barcode scanning

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Authors: Xiang Shijie, Zhou Dong, Tian Dan

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This paper focuses on the application of real-time semantic segmentation technology in complex road condition recognition, aiming to address the critical issue of how to improve segmentation accuracy while ensuring real-time performance. Semantic segmentation technology has broad application prospects in fields such as autonomous vehicle navigation and remote sensing image recognition. However, current real-time semantic segmentation networks face significant technical challenges and optimization gaps in balancing speed and accuracy. To tackle this problem, this paper conducts an in-depth study and proposes an innovative Guided Image Reconstruction Module. By resampling high-resolution images into a set of low-resolution images, this module effectively reduces computational complexity, allowing the network to more efficiently extract features within limited resources, thereby improving the performance of real-time segmentation tasks. In addition, a dual-branch network structure is designed in this paper to fully leverage the advantages of different feature layers. A novel Hybrid Attention Mechanism is also introduced, which can dynamically capture multi-scale contextual information and effectively enhance the focus on important features, thus improving the segmentation accuracy of the network in complex road condition. Compared with traditional methods, the proposed model achieves a better balance between accuracy and real-time performance and demonstrates competitive results in road condition segmentation tasks, showcasing its superiority. Experimental results show that this method not only significantly improves segmentation accuracy while maintaining real-time performance, but also remains stable across diverse and complex road conditions, making it highly applicable in practical scenarios. By incorporating the Guided Image Reconstruction Module, dual-branch structure, and Hybrid Attention Mechanism, this paper presents a novel approach to real-time semantic segmentation tasks, which is expected to further advance the development of this field.

Keywords: hybrid attention mechanism, image reconstruction, real-time, road status recognition

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Authors: Besma Khalfoun

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In many current Human Activity Recognition (HAR) applications, users' data is frequently shared and centrally stored by third parties, posing a significant privacy risk. This practice makes these entities attractive targets for extracting sensitive information about users, including their identity, health status, and location, thereby directly violating users' privacy. To tackle the issue of centralized data storage, a relatively recent paradigm known as federated learning has emerged. In this approach, users' raw data remains on their smartphones, where they train the HAR model locally. However, users still share updates of their local models originating from raw data. These updates are vulnerable to several attacks designed to extract sensitive information, such as determining whether a data sample is used in the training process, recovering the training data with inversion attacks, or inferring a specific attribute or property from the training data. In this paper, we first introduce PUR-Attack, a parameter-based user re-identification attack developed for HAR applications within a federated learning setting. It involves associating anonymous model updates (i.e., local models' weights or parameters) with the originating user's identity using background knowledge. PUR-Attack relies on a simple yet effective machine learning classifier and produces promising results. Specifically, we have found that by considering the weights of a given layer in a HAR model, we can uniquely re-identify users with an attack success rate of almost 100%. This result holds when considering a small attack training set and various data splitting strategies in the HAR model training. Thus, it is crucial to investigate protection methods to mitigate this privacy threat. Along this path, we propose SAFER, a privacy-preserving mechanism based on adaptive local differential privacy. Before sharing the model updates with the FL server, SAFER adds the optimal noise based on the re-identification risk assessment. Our approach can achieve a promising tradeoff between privacy, in terms of reducing re-identification risk, and utility, in terms of maintaining acceptable accuracy for the HAR model.

Keywords: federated learning, privacy risk assessment, re-identification risk, privacy preserving mechanisms, local differential privacy, human activity recognition

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Noorlizawati Abd Rahim, Zainai Mohamed, Zaidatun Tasir, Astuty Amrin, Haliyana Khalid, Nina Diana Nawi

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Entrepreneurial leaders have been sought as in-demand talents to lead profit-driven organizations during turbulent and unprecedented times. However, research regarding the pertinence of their roles in the public sector has been limited. This paper examined the characteristics of the challenging experiences encountered by senior leaders in public universities that require them to embrace entrepreneurialism in their leadership. Through a focus group interview with five Malaysian university top senior leaders with experience being Vice-Chancellor, we explored and developed a framework of institutional adversity characteristics and exemplary entrepreneurial leadership competency in the face of adversity. Complexity of diverse stakeholders, multiplicity of academic disciplines, unfamiliarity to lead different and broader roles, leading new directions, and creating change in high velocity and uncertain environment are among the dimensions that characterise institutional adversities. Our findings revealed that learning agility, opportunity recognition capacity, and bridging capability are among the characteristics of entrepreneurial university leaders. The findings reinforced that the presence of specific attributes in institutional adversity and experiences in overcoming those challenges may contribute to the development of entrepreneurial leadership capabilities.

Keywords: bridging capability, entrepreneurial leadership, leadership development, learning agility, opportunity recognition, university leaders

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Authors: Mina Dabirinezhad, Mohsen Bayat Pour, Amin Dabirinejad

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This paper is intended to introduce the technology to solve some of the deficiencies of the direct digital radiology. Nowadays, digital radiology is the latest progression in dental imaging, which has become an essential part of dentistry. There are two main parts of the direct digital radiology comprised of an intraoral X-ray machine and a sensor (digital image receptor). The dentists and the dental nurses experience afflictions during the taking image process by the direct digital X-ray machine. For instance, sometimes they need to readjust the sensor in the mouth of the patient to take the X-ray image again due to the low quality of that. Another problem is, the position of the sensor may move in the mouth of the patient and it triggers off an inappropriate image for the dentists. It means that it is a time-consuming process for dentists or dental nurses. On the other hand, taking several the X-ray images brings some problems for the patient such as being harmful to their health and feeling pain in their mouth due to the pressure of the sensor to the jaw. The author provides a technology to solve the above-mentioned issues that is called “Self-Recognition Direct Digital Radiology” (SDDR). This technology is based on the principle that the intraoral X-ray machine is capable to diagnose the location of the sensor in the mouth of the patient automatically. In addition, to solve the aforementioned problems, SDDR technology brings out fewer environmental impacts in comparison to the previous version.

Keywords: Dental direct digital imaging, digital image receptor, digital x-ray machine, and environmental impacts

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Authors: Sandra Tapin-Lingua, Katia Ruel, Jean-Paul Joseleau, Daouia Messaoudi, Olivier Fahy, Michel Petit-Conil

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The preservative efficacy of organic biocides is strongly related to their capacity of penetration and retention within wood tissues. The specific detection of the pyrethroid insecticide is currently obtained after extraction followed by chemical analysis by chromatography techniques. However visualizing the insecticide molecule within the wood structure requires specific probes together with microscopy techniques. Therefore, the aim of the present work was to apply a new methodology based on antibody-antigen recognition and electronic microscopy to visualize directly pyrethroids in the wood material. A polyclonal antibody directed against cypermethrin was developed and implement it on Pinus sylvestris wood samples coated with technical cypermethrin. The antibody was tested on impregnated wood and the specific recognition of the insecticide was visualized in transmission electron microscopy (TEM). The immunogold-TEM assay evidenced the capacity of the synthetic biocide to penetrate in the wood. The depth of penetration was measured on sections taken at increasing distances from the coated surface of the wood. Such results correlated with chemical analyzes carried out by GC-ECD after extraction. In addition, the immuno-TEM investigation allowed visualizing, for the first time at the ultrastructure scale of resolution, that cypermethrin was able to diffuse within the secondary wood cell walls.

Keywords: cypermethrin, insecticide, wood penetration, wood retention, immuno-transmission electron microscopy, polyclonal antibody

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Authors: Wasantha Pushpakumara Hitihami Mudiyanselage

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Since recent past, Sri Lanka is highly vulnerable to reoccurring climate shocks that severely impact the food security, loss of human & animal lives, destructions of human settlements, displacement of people and damaging properties. Hence, the Government of Sri Lanka has taken important steps towards strengthening legal and institutional arrangements for Disaster Risks management in the country in May 2005. Puttalam administrative district is one of the disaster prone districts in Sri Lanka which constantly face the devastating consequences of the increasing natural disasters annually. Therefore disaster risk management will be a timely intervention in the area to minimize the adverse impacts of the disasters. The few functioning Disaster Risk management networks do not take children’s specific needs and vulnerabilities during emergencies into account. The most affected children and their families were evacuated to the government schools and temples and it was observed that children were left to roaming around as their parents were busy queuing up for relief goods and other priorities. In this sense, VOICE understands that the community has vital role that has to be played in facing challenges of disaster management in the area. During and after the disaster, it was viewed that some children were having psychological disorders which could be impacted negatively to children well–being. Need of child friendly space at emergency is a must action in the area to turn away negative impact coming from the hazards. VOICE with the support of national & international communities have established safer places for the children (Child Centered Spaces – CCS) and their families at emergencies. Village religious venues and schools were selected and equipped with necessary materials to be used for the children at emergency. Materials such as tools, stationeries, play materials, which couldn’t be easily found in surrounding environment, were provided for CCS centers. Village animators, youth and elders were given comprehensive training on Disaster management and their role at CCS. They did the facilitation in keeping children without fear and stress at flooding occurred in 2015 as well as they were able to improve their skills when working with children. Flooding in 2016, the government agencies have taken service of these village animators at early stage of flooding to make all disaster-related recovery actions productively & efficiently. This mechanism is sustained at village level that can be used for disaster events.

Keywords: child centered space, impacts, psychological disorders, village animators

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Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Sonia Bernardo Correia

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This paper is part of ongoing research that aims to understand the role of gender in the composition of the Portuguese contemporary art world and the possibilities and limits to the success of the professional paths of women and men artists. The field of visual arts is gender-sensitive as it differentiates the positions occupied by artists in terms of visibility and recognition. Women artists occupy a peripheral space, which may hinder the progression of their professional careers. Based on the collection of data on the participation of artists in Portuguese exhibitions, art fairs, auctions, and art awards between 2012 and 2019, the goal of this study is to portray female artists’ participation as a condition of professional, social, and cultural visibility. From the analysis of a significant sample of institutions from the artistic field, it was possible to observe that the works of female authors are under exhibited, never exceeding one-third of the total of exhibitions. Male artists also enjoy a comfortable majority as gallery artists (around 70%) and as part of institutional collections (around 80%). However, when analysing the younger age cohorts of artists by gender, it appears that there is representation parity, which may be a good sign of change. The data shows that there are persistent gender inequalities in accessing the artist profession. Women are not yet occupying positions of exposure, recognition, and legitimation in the market similar to those of their male counterparts, suggesting that they may face greater obstacles in experiencing successful professional trajectories.

Keywords: inequalities, invisibility of the woman artist, gender, visual arts

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Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy

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This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.

Keywords: sign language, CNN, HCI, segmentation

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Authors: Christian Arcos, Marley Vellasco, Abraham Alcaim

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In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario.

Keywords: binary labels, local binary patterns, mask, wavelet coefficients, speech enhancement, speech recognition

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Authors: Ankita Wadhawan, Parteek Kumar, Umesh Kumar

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Gesture is any body movement that expresses some attitude or any sentiment. Gestures as a sign language are used by deaf people for conveying messages which helps in eliminating the communication barrier between deaf people and normal persons. Nowadays, everybody is using mobile phone and computer as a very important gadget in their life. But there are some physically challenged people who are blind/deaf and the use of mobile phone or computer like device is very difficult for them. So, there is an immense need of a system which works on body gesture or sign language as input. In this research, Microsoft Kinect Sensor, SDK V2 and Hidden Markov Toolkit (HTK) are used to recognize the object, motion of object and human body joints through Touch less NUI (Natural User Interface) in real-time. The depth data collected from Microsoft Kinect has been used to recognize gestures of Indian Sign Language (ISL). The recorded clips are analyzed using depth, IR and skeletal data at different angles and positions. The proposed system has an average accuracy of 85%. The developed Touch less NUI provides an interface to recognize gestures and controls the cursor and click operation in computer just by waving hand gesture. This research will help deaf people to make use of mobile phones, computers and socialize among other persons in the society.

Keywords: gesture recognition, Indian sign language, Microsoft Kinect, natural user interface, sign language

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Saeed Asiri

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The Novel Automated System (NAS) has the control system of the level of chlorine and acid (i.e. pH level) through a feedback in three forms of synchronous alerts. The feedback is in the form of an alert voice, a visible color, and a message on a digital screen. In addition, NAS contains a slide-in container in which chemicals are used to treat the problems of chlorine and acid levels independently. Moreover, NAS has a net in front of it to clean the pool on the surface of the water from leaves and wastes and so on which is controlled through a remote control. The material used is a lightweight aluminum with mechanical and electric parts integrated with each other. In fact, NAS is qualified to serve as an assistant security guard for swimming pools because it has the characteristics that make it unique and smart.

Keywords: novel automated system, pool safety, maintenance, pH level, digital screen

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Authors: Maroof Sakhi

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This article studies writing style in A Thousand Splendid Suns. It mainly discusses code-switching and usage of Persian words in the novel. Hosseini mainly writes in English; however, constantly he applies Persian words, phrases and syntax. Code-switching is used for different purposes in A Thousand Splendid Suns. It gives a voice to a character from Afghanistan. It is also used to mark a difference between the American and Afghanistan cultures and languages. Furthermore, representation of Persian language can be interpreted as valorization of the author’s mother tongue. In short, the writing style in A Thousand Splendid Suns represents Hosseini’s identity, culture and linguistic background.

Keywords: code-switching, hybridity, identity, linguistic background, Persian literature

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Authors: Mayoorika Shukla, Pramila Jakhar, Tejendra Dixit, I. A. Palani, Vipul Singh

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Biosensors are analytical devices with wide range of applications in biological, chemical, environmental and clinical analysis. It comprises of bio-recognition layer which has biomolecules (enzymes, antibodies, DNA, etc.) immobilized over it for detection of analyte and transducer which converts the biological signal into the electrical signal. The performance of biosensor primarily the depends on the bio-recognition layer and therefore it has to be chosen wisely. In this regard, nanostructures of metal oxides such as ZnO, SnO2, V2O5, and TiO2, etc. have been explored extensively as bio-recognition layer. Recently, ZnO has the attracted attention of researchers due to its unique properties like high iso-electric point, biocompatibility, stability, high electron mobility and high electron binding energy, etc. Although there have been many reports on usage of ZnO as bio-recognition layer but to the authors’ knowledge, none has ever observed correlation between optical properties like defect suppression and biosensing capability of the sensor. Here, ZnO nanorods (ZNR) have been synthesized by a low cost, simple and low-temperature hydrothermal growth process, over Platinum (Pt) coated glass substrate. The ZNR have been synthesized in two steps viz. initially a seed layer was coated over substrate (Pt coated glass) followed by immersion of it into nutrient solution of Zinc nitrate and Hexamethylenetetramine (HMTA) with in situ addition of KMnO4. The addition of KMnO4 was observed to have a profound effect over the growth rate anisotropy of ZnO nanostructures. Clustered and powdery growth of ZnO was observed without addition of KMnO4, although by addition of it during the growth, uniform and crystalline ZNR were found to be grown over the substrate. Moreover, the same has resulted in suppression of defects as observed by Normalized Photoluminescence (PL) spectra since KMnO4 is a strong oxidizing agent which provides an oxygen rich growth environment. Further, to explore the correlation between defect suppression and biosensing capability of the ZNR Glucose oxidase (Gox) was immobilized over it, using physical adsorption technique followed by drop casting of nafion. Here the main objective of the work was to analyze effect of defect suppression over biosensing capability, and therefore Gox has been chosen as model enzyme, and electrochemical amperometric glucose detection was performed. The incorporation of KMnO4 during growth has resulted in variation of optical and charge transfer properties of ZNR which in turn were observed to have deep impact on biosensor figure of merits. The sensitivity of biosensor was found to increase by 12-18 times, due to variations introduced by addition of KMnO4 during growth. The amperometric detection of glucose in continuously stirred buffer solution was performed. Interestingly, defect suppression has been observed to contribute towards the improvement of biosensor performance. The detailed mechanism of growth of ZNR along with the overall influence of defect suppression on the sensing capabilities of the resulting enzymatic electrochemical biosensor and different figure of merits of the biosensor (Glass/Pt/ZNR/Gox/Nafion) will be discussed during the conference.

Keywords: biosensors, defects, KMnO4, ZnO nanorods

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Authors: Amin Aghatabar Roodbary, Mohammad Hassan Bastani

Abstract:

One of the defects of stepped frequency radar systems is their sensitivity to target motion. In such systems, target motion causes range cell shift, false peaks, Signal to Noise Ratio (SNR) reduction and range profile spreading because of power spectrum interference of each range cell in adjacent range cells which induces distortion in High Resolution Range Profile (HRRP) and disrupt target recognition process. Thus Target Motion Parameters (TMPs) effects compensation should be employed. In this paper, such a method for estimating TMPs (velocity and acceleration) and consequently eliminating or suppressing the unwanted effects on HRRP based on entropy minimization has been proposed. This method is carried out in two major steps: in the first step, a discrete search method has been utilized over the whole acceleration-velocity lattice network, in a specific interval seeking to find a less-accurate minimum point of the entropy function. Then in the second step, a 1-D search over velocity is done in locus of the minimum for several constant acceleration lines, in order to enhance the accuracy of the minimum point found in the first step. The provided simulation results demonstrate the effectiveness of the proposed method.

Keywords: automatic target recognition (ATR), high resolution range profile (HRRP), motion compensation, stepped frequency waveform technique (SFW), target motion parameters (TMPs)

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Authors: Tomas Trainys, Algimantas Venckauskas

Abstract:

Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: S. Aswath, Chinmaya Krishna Tilak, Amal Suresh, Ganesh Udupa

Abstract:

There are technologies to control a humanoid robot in many ways. But the use of Electromyogram (EMG) electrodes has its own importance in setting up the control system. The EMG based control system helps to control robotic devices with more fidelity and precision. In this paper, development of an electromyogram based interface for human gesture recognition for the control of a humanoid robot is presented. To recognize control signs in the gestures, a single channel EMG sensor is positioned on the muscles of the human body. Instead of using a remote control unit, the humanoid robot is controlled by various gestures performed by the human. The EMG electrodes attached to the muscles generates an analog signal due to the effect of nerve impulses generated on moving muscles of the human being. The analog signals taken up from the muscles are supplied to a differential muscle sensor that processes the given signal to generate a signal suitable for the microcontroller to get the control over a humanoid robot. The signal from the differential muscle sensor is converted to a digital form using the ADC of the microcontroller and outputs its decision to the CM-530 humanoid robot controller through a Zigbee wireless interface. The output decision of the CM-530 processor is sent to a motor driver in order to control the servo motors in required direction for human like actions. This method for gaining control of a humanoid robot could be used for performing actions with more accuracy and ease. In addition, a study has been conducted to investigate the controllability and ease of use of the interface and the employed gestures.

Keywords: electromyogram, gesture, muscle sensor, humanoid robot, microcontroller, Zigbee

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Authors: Ly Quyet Tien

Abstract:

The object of this paper is to study Vietnamese Les Rap as art to promote rights and gender in the twenty-first century. The author analyzed Vietnamese Les Rap from the point of view of a contemporary person who has witnessed the tumultuous destiny of the most suffering minority group in Vietnam’s LGBT community. He reviewed scholarly studies, and reputable newspapers on the topic and conducted face to face interviews for qualitative data. The study found that Vietnamese lesbians have composed and used rap as an effective tool to express their fight for visibility, identity, and sensibility. Research reveals that these songs did not only touch the heart of the LGBT community but also the larger public, marking the rising queer voice, contributing to a flourishing of LGBT culture, and revolutionizing the colors of Vietnamese hip hop music in the twenty-first century.

Keywords: culture, lesbian, rap, Vietnam

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

Abstract:

This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Maha Saliba Foster

Abstract:

Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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Authors: Michael N. O'Sullivan, Con Sheahan

Abstract:

Researchers have spent decades developing tools and techniques to aid teams in the new product development (NPD) process. Despite this, it is evident that there is a huge gap between their academic prevalence and their industry adoption. For the fuzzy front-end, in particular, there is a wide range of tools to choose from, including the Kano Model, the House of Quality, and many others. In fact, there are so many tools that it can often be difficult for teams to know which ones to use and how they interact with one another. Moreover, while the benefits of using these tools are obvious to industrialists, they are rarely used as they carry a learning curve that is too steep and they become too complex to manage over time. In essence, it is commonly believed that they are simply not worth the effort required to learn and use them. This research explores a streamlined process for the fuzzy front-end, assembling the most effective tools and making them accessible to everyone. The process was developed iteratively over the course of 3 years, following over 80 final year NPD teams from engineering, design, technology, and construction as they carried a product from concept through to production specification. Questionnaires, focus groups, and observations were used to understand the usability issues with the tools involved, and a human-centred design approach was adopted to produce a solution to these issues. The solution takes the form of physical toolkit, similar to a board game, which allows the team to play through an example of a new product development in order to understand the process and the tools, before using it for their own product development efforts. A complimentary website is used to enhance the physical toolkit, and it provides more examples of the tools being used, as well as deeper discussions on each of the topics, allowing teams to adapt the process to their skills, preferences and product type. Teams found the solution very useful and intuitive and experienced significantly less confusion and mistakes with the process than teams who did not use it. Those with a design background found it especially useful for the engineering principles like Quality Function Deployment, while those with an engineering or technology background found it especially useful for design and customer requirements acquisition principles, like Voice of the Customer. Products developed using the toolkit are added to the website as more examples of how it can be used, creating a loop which helps future teams understand how the toolkit can be adapted to their project, whether it be a small consumer product or a large B2B service. The toolkit unlocks the potential of these beneficial tools to those in industry, both for large, experienced teams and for inexperienced start-ups. It allows users to assess the market potential of their product concept faster and more effectively, arriving at the product design stage with technical requirements prioritized according to their customers’ needs and wants.

Keywords: new product development, fuzzy front-end, usability, Kano model, quality function deployment, voice of customer

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