Journal articles on the topic 'IBM Watson Visual Recognition'

Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data collected by World Health Organization (WHO) across a hundred of cancer registries worldwide. In this study, the authors present a visual analytics platform, IBM Watson Analytics, to explore the patterns of global cancer incidence. They included 26 forms of cancers from eight different geographic regions which are United States, the United Kingdom, Costa Rica, Sweden, Croatia, Japan, Hong Kong and China (Shanghai). An interactive interface was applied to plot a choropleth map to show global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years. Subgroup analyses were conducted for different age groups. With real-time interactive features, one can easily explore the data with a selection of any cancer type, gender, age group, or geographical region. This platform is running on the cloud, so it can handle data in huge volumes, and is accessible by any computer connected to the Internet. IBM Watson Analytics released a latest version named “IBM Watson Analytics New User Experience” in the end of 2016. The new version streamlined the process to add data, discover data meaning and display result visually. The authors discuss the new features in the end of this paper.

AbstractAutomatic speech recognition (ASR) could potentially improve communication by providing transcriptions of speech in real time. ASR is particularly useful for people with progressive disorders that lead to reduced speech intelligibility or difficulties performing motor tasks. ASR services are usually trained on healthy speech and may not be optimized for impaired speech, creating a barrier for accessing augmented assistance devices. We tested the performance of three state-of-the-art ASR platforms on two groups of people with neurodegenerative disease and healthy controls. We further examined individual differences that may explain errors in ASR services within groups, such as age and sex. Speakers were recorded while reading a standard text. Speech was elicited from individuals with multiple sclerosis, Friedreich’s ataxia, and healthy controls. Recordings were manually transcribed and compared to ASR transcriptions using Amazon Web Services, Google Cloud, and IBM Watson. Accuracy was measured as the proportion of words that were correctly classified. ASR accuracy was higher for controls than clinical groups, and higher for multiple sclerosis compared to Friedreich’s ataxia for all ASR services. Amazon Web Services and Google Cloud yielded higher accuracy than IBM Watson. ASR accuracy decreased with increased disease duration. Age and sex did not significantly affect ASR accuracy. ASR faces challenges for people with neuromuscular disorders. Until improvements are made in recognizing less intelligible speech, the true value of ASR for people requiring augmented assistance devices and alternative communication remains unrealized. We suggest potential methods to improve ASR for those with impaired speech.

AbstractIBM Watson is one of the representative tools for speech recognition system which can automatically generate not only speech-to-text information but also speaker ID and timing information, which is called as Informatized Caption. However, if there is some noise in the voice signal to the IBM Watson API, the recognition performance is significantly decreased. It can be easily found in movies with background music and special sound effects. This paper aims to improve the inaccuracy problem of current Informatized Captions in noisy environments. In this paper, a method of modifying incorrectly recognized words and a method of enhancing timing accuracy while updating database in real time are suggested based on the original caption and Informatized Caption information. Experimental results shows that the proposed method can give 81.09% timing accuracy for the case of 10 representative animation, horror and action movies.

To shape a complete city image, it is necessary to find the first characteristic of the city so as to further improve the easy identification of the city image, create a good city image, and make the city more competitive. This paper combines the Watson visual perception model to carry out the visual image recognition design of Nanchang VI to improve the communication effect of the urban VI visual image. Moreover, this paper proposes a video watermarking algorithm based on MPEG-4 encoding using the open-source Xvid codec. In addition, this paper proves that the proposed algorithm has good application value in imperceptibility and robustness through a large number of experiments and data analysis. Finally, this paper verifies the reliability of the method proposed in this paper through the study of multiple sets of data.

In recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Charu Aggarwal, Ph.D. is the recipient of the 2022 ACM SIGKDD Service Award. Conferred annually on an individual or group, Dr. Aggarwal was honored for his years of service and many contributions that span publishing, programming, sponsorship and more in support of SIGKDD. Dr. Aggarwal is Distinguished Research Staff Member at IBM T. J. Watson Research Center and was the recipient of the 2019 SIGKDD Innovation Award. He sat down with SIGKDD Explorations to discuss how he first became involved with the KDD conference, the evolution of his volunteerism, the focus of his work at IBM, and his advice to those early in their careers.

The IBM Watson AI XPRIZE is a four-year competition where teams work to improve the world with artificial intelligence. The competition began in 2017 with 148 problem domains in sustainability, artificial general intelligence, education, and a variety of other grand challenge areas. 59 teams advanced to the second year of the competition and ten teams earned special recognition as “milestone nominees.” The properties of the advancing problem domains highlight opportunities and challenges for the “AI for Good” movement. We detail the judging process and highlight preliminary results from cutting the field of competing teams.

Abstract: OCR is a short form of Optical character recognition or optical character reader. By the full form, we can understand it is something that can read content present in the image. Every image in the world contains any kind of object in it and some of them have characters that can be read by humans easily, programming a machine to read them can be called OCR. In machine learning, data mining is one of the major sections that cover the extraction of the data from the different platforms. OCR (Optical Character Recognition) is part of the data mining process that mainly deals with typed, handwritten, or printed documents. These documents hold the data mainly in the form of images. Extracting such data requires some optimised models which can detect and recognize the texts. Getting information from complex structured documents becomes difficult and hence they require some effective methodologies for information extraction. In this article, we will discuss OCR with IBM Watson Natural Language Understanding API, a deep learning-based tool for localizing and detecting the text in documents and images.

Speech recognition, an essential component of natural language processing (NLP), plays a pivotal role in enhancing communication and human-computer interaction. This paper reviews the advancements, challenges, and applications of speech recognition, natural language understanding (NLU), and chatbot technologies. Current speech recognition systems utilize techniques like Mel Frequency Cepstral Coefficients (MFCC) and Hidden Markov Models (HMM) to address linguistic errors, gender recognition failures, and inaccurate voice recognition. Applications such as voice assistants offer continuous interaction capabilities, enabling users, including those with disabilities, to perform tasks like web searches and document preparation. Additionally, we examine vulnerabilities in voice assistants, particularly in NLU components like Intent Classifiers, which can misinterpret user inputs and pose security risks. The transformative impact of deep neural networks (DNN) on speech recognition since 2010 is also discussed, alongside their application to fields like machine translation and image captioning. Furthermore, this paper highlights the evolution of chatbots, integrating NLU platforms like Google DialogFlow and IBM Watson, to deliver intelligent, adaptive interactions. By addressing challenges in intent recognition and system integration, this review underscores the potential of AI-driven solutions to revolutionize speech-based applications.

The Frauenkirche was destroyed when Dresden was bombed by the Allied forces February 13-14, 1945. The church is now being reconstructed in an effort led by the Foundation for the Reconstruction of the Frauenkirche. The VRDECK software package developed at the IBM T. J. Watson Research Center was used to view and walk through a model created from the original church plans. A Polhemus tracker and a custom-built joystick using the Logitech 3D mouse were used for six-degree-of-freedom input to the application. The interactive fly-through of the church is in an immersive environment. One can navigate around the model wearing a head-mounted display, sitting in front of a standard monitor, looking at a stereo image produced on a stereo monitor, or standing before a projection screen displaying a stereo image of the scene. The system was developed for and exhibited at the IBM booth in the CeBIT fair in Hannover, Germany in March 1994 with funding from IBM Germany.

Artificial intelligence (AI) has the potential to speed up the exponential growth of cutting-edge technology, much way the Internet did. Due to intense competition from the private sector, governments, and businesspeople around the world, the Internet has already reached its peak as an exponential technology. In contrast, artificial intelligence is still in its infancy, and people all over the world are unsure of how it will impact their lives in the future. Artificial intelligence, is a field of technology that enables robots and computer programmes to mimic human intellect by teaching a predetermined set of software rules to learn by repetitive learning from experience and slowly moving toward maximum performance. Although this intelligence is still developing, it has already demonstrated five different levels of independence. Utilized initially to resolve issues. Next, think about solutions. Third, respond to inquiries. Fourth, use data analytics to generate forecasts. Fifth, make tactical recommendations. Massive data sets and "iterative algorithms," which use lookup tables and other data structures like stacks and queues to solve issues, make all of this possible. Iteration is a strategy where software rules are regularly adjusted to patterns in the data for a certain number of iterations. The artificial intelligence continuously makes small, incremental improvements that result in exponential growth, which enables the computer to become incredibly proficient at whatever it is trained to do. For each round of data processing, the artificial intelligence tests and measures its performance to develop new expertise. In order to address complicated problems, artificial intelligence aims to create computer systems that can mimic human behavior and exhibit human-like thought processes [1]. Artificial intelligence technology is being developed to give individualized medication in the field of healthcare. By 2030, six different artificial intelligence sectors will have considerably improved healthcare delivery through the utilization of larger, more accessible data sets. The first is machine learning. This area of artificial intelligence learns automatically and produces improved results based on identifying patterns in the data, gaining new insights, and enhancing the outcomes of whatever activity the system is intended to accomplish. It does this without being trained to learn a particular topic. Here are several instances of machine learning in the healthcare industry. The first is the IBM Watson Genomics, which aids in rapid disease diagnosis and identification by fusing cognitive computing with genome-based tumour sequencing. Second, a project called Nave Bayes allows for the prediction of diabetes years before an official diagnosis, before it results in harm to the kidneys, the heart, and the nerves. Third, employing two machine learning approaches termed classification and clustering to analyse the Indian Liver Patient Data (ILPD) set in order to predict liver illness before this organ that regulates metabolism becomes susceptible to chronic hepatitis, liver cancer, and cirrhosis [2]. Second, deep learning. Deep learning employs artificial intelligence to learn from data processing, much like machine learning does. Deep learning, on the other hand, makes use of synthetic neural networks that mimic human brain function to analyse data, identify relationships between the data, and provide outputs based on positive and negative reinforcement. For instance, in the fields of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), deep learning aids in the processes of picture recognition and object detection. Deep learning algorithms for the early identification of Alzheimer's, diabetic retinopathy, and breast nodule ultrasound detection are three applications of this cutting-edge technology in the real world. Future developments in deep learning will make considerable improvements in pathology and radiology pictures [3]. Third, neural networks. The artificial intelligence system can now accept massive data sets, find patterns within the data, and respond to queries regarding the information processed because the computer learning process resembles a network of neurons in the human brain. Let's examine a few application examples that are now applicable to the healthcare sector. According to studies from John Hopkins University, surgical errors are a major contributor to medical malpractice claims since they happen more than 4,000 times a year in just the United States due to the human error of surgeons. Neural networks can be used in robot-assisted surgery to model and plan procedures, evaluate the abilities of the surgeon, and streamline surgical activities. In one study of 379 orthopaedic patients, it was discovered that robotic surgery using neural networks results in five times fewer complications than surgery performed by a single surgeon. Another application of neural networks is in visualising diagnostics, which was proven to physicians by Harvard University researchers who inserted an image of a gorilla to x-rays. Of the radiologists who saw the images, 83% did not recognise the gorilla. The Houston Medical Research Institute has created a breast cancer early detection programme that can analyse mammograms with 99 percent accuracy and offer diagnostic information 30 times faster than a human [4]. Cognitive computing is the fourth. Aims to replicate the way people and machines interact, showing how a computer may operate like the human brain when handling challenging tasks like text, speech, or image analysis. Large volumes of patient data have been analysed, with the majority of the research to date focusing on cancer, diabetes, and cardiovascular disease. Companies like Google, IBM, Facebook, and Apple have shown interest in this work. Cognitive computing made up the greatest component of the artificial market in 2020, with 39% of the total [5]. Hospitals made up 42% of the market for cognitive computing end users because of the rising demand for individualised medical data. IBM invested more than $1 billion on the development of the WATSON analytics platform ecosystem and collaboration with startups committed to creating various cloud and application-based systems for the healthcare business in 2014 because it predicted the demand for cognitive computing in this sector. Natural Language Processing (NLP) is the fifth. This area of artificial intelligence enables computers to comprehend and analyse spoken language. The initial phase of this pre-processing is to divide the data up into more manageable semantic units, which merely makes the information simpler for the NLP system to understand. Clinical trial development is experiencing exponential expansion in the healthcare sector thanks to NLP. First, the NLP uses speech-to-text dictation and structured data entry to extract clinical data at the point of care, reducing the need for manual assessment of complex clinical paperwork. Second, using NLP technology, healthcare professionals can automatically examine enormous amounts of unstructured clinical and patient data to select the most suitable patients for clinical trials, perhaps leading to an improvement in the patients' health [6]. Computer vision comes in sixth. Computer vision, an essential part of artificial intelligence, uses visual data as input to process photos and videos continuously in order to get better results faster and with higher quality than would be possible if the same job were done manually. Simply put, doctors can now diagnose their patients with diseases like cancer, diabetes, and cardiovascular disorders more quickly and at an earlier stage. Here are a few examples of real-world applications where computer vision technology is making notable strides. Mammogram images are analysed by visual systems that are intended to spot breast cancer at an early stage. Automated cell counting is another example from the real world that dramatically decreases human error and raises concerns about the accuracy of the results because they might differ greatly depending on the examiner's experience and degree of focus. A third application of computer vision in the real world is the quick and painless early-stage tumour detection enabled by artificial intelligence. Without a doubt, computer vision has the unfathomable potential to significantly enhance how healthcare is delivered. Other than for visual data analysis, clinicians can use this technology to enhance their training and skill development. Currently, Gramener is the top company offering medical facilities and research organisations computer vision solutions [7]. The usage of imperative rather than functional programming languages is one of the key difficulties in creating artificial intelligence software. As artificial intelligence starts to increase exponentially, developers employing imperative programming languages must assume that the machine is stupid and supply detailed instructions that are subject to a high level of maintenance and human error. In software with hundreds of thousands of lines of code, human error detection is challenging. Therefore, the substantial amount of ensuing maintenance may become ridiculously expensive, maintaining the high expenditures of research and development. As a result, software developers have contributed to the unreasonably high cost of medical care. Functional programming languages, on the other hand, demand that the developer use their problem-solving abilities as though the computer were a mathematician. As a result, compared to the number of lines of code needed by the programme to perform the same operation, mathematical functions are orders of magnitude shorter. In software with hundreds of thousands of lines of code, human error detection is challenging. Therefore, the substantial amount of ensuing maintenance may become ridiculously expensive, maintaining the high expenditures of research and development. As a result, software developers have contributed to the unreasonably high cost of medical care. Functional programming languages, on the other hand, demand that the developer use their problem-solving abilities as though the computer were a mathematician. As a result, compared to the number of lines of code needed by the programme to perform the same operation, mathematical functions are orders of magnitude shorter. The bulk of software developers that use functional programming languages are well-trained in mathematical logic; thus, they reason differently than most American software developers, who are more accustomed to following step-by-step instructions. The market for artificial intelligence in healthcare is expected to increase from $3.4 billion in 2021 to at least $18.7 billion by 2027, or a 30 percent annual growth rate before 2030, according to market research firm IMARC Group. The only outstanding query is whether these operational reductions will ultimately result in less expensive therapies.

AbstractTypically, automatic Question Answering (QA) approaches use the question in its entirety in the search for potential answers. We argue that decomposing complex factoid questions into separate facts about their answers is beneficial to QA, since an answer candidate with support coming from multiple independent facts is more likely to be the correct one. We broadly categorize decomposable questions as parallel or nested, and we present a novel question decomposition framework for enhancing the ability of single-shot QA systems to answer complex factoid questions. Essential to the framework are components for decomposition recognition, question rewriting, and candidate answer synthesis and re-ranking. We discuss the interplay among these, with particular emphasis on decomposition recognition, a process which, we argue, can be sufficiently informed by lexico-syntactic features alone. We validate our approach to decomposition by implementing the framework on top of IBM Watson™, a state-of-the-art QA system, and showing a statistically significant improvement over its accuracy.

The legal industry has undergone a transformation through the combination of machine learning and artificial intelligence techniques. This work focuses the application of such approaches in legal management and also explores how these techniques are useful in various aspects of legal service. With this work, there is an analysis done using case studies from leading organisations such as Lex Machina, JP Morgan, Deloitte, IBM Watson, All State insurance and others. We show that the potential of machine learning is useful in improving the efficiency and decision making in these processes applied to critical legal domains such as contract intelligence, IPR analysis, litigation risk assessment and other our work. The potential of ML to combine with traditional legal practices offer a lot of advantages in the field of data analytics, and pattern recognition.

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.

Voice recognition adalah suatu sistem yang dapat mengidentifikasi seseorang melalui suara. Penelitian ini bertujuan untuk menerapkan teknologi voice recognition dengan menggunakan library IBM watson sebagai media pembelajaran bahasa inggris untuk sekolah menengah pertama. Aplikasi belajar bahasa inggris merupakan alternatif media pembelajaran yang dapat menunjang pembelajaran bahasa inggris yang menyediakan latihan pronounce, soal latihan evaluasi pembelajaran. Berdasarkan pengujian yang dilakukan Kelas VII E, Hasil pre-test dan post-test chapter 1 diperoleh ttabel = 2.04523 dengan thitung = 6.892, chapter 2 hasil pre-test dan post-test diperoleh ttabel = 2.04227 dengan thitung = 7.741. Kemudian berdasarkan pengujian yang dilakukan di kelas VII C, Hasil pre-test dan post-test chapter 1 diperoleh ttabel = 2.04523 dengan thitung = 7,970, chapter 2 hasil pre-test dan post-test diperoleh ttabel = 2.04523 dengan thitung = 7,077. Hasil pengujian yang dilakukan thitung > ttabel maka H0 ditolak sehingga H1 diterima sehingga dapat disimpulkan bahwa penerapan aplikasi belajar bahasa inggris dengan teknologi voice recognition sebagai media pembelajaran berpengaruh terhadap aspek pemahaman siswa mengenai pembelajaran bahasa inggris. Kemudian hasil yang diperoleh dari uji kelayakan aplikasi berada dalam kategori “Sangat Efektif” dengan menggunakan skala interval likert dimana pengoperasian aplikasi dengan rata-rata nilai (3,32 dan 3,4) dari segi tampilan dengan rata-rata nilai (3,39 dan 3,38) dan isi aplikasi dengan rata-rata (3,31 dan 3,33).

<em>Sun&rsquo;iy intellekt (SI) bizning yashash, ishlash sharoitimizda va o&lsquo;zaro munosabatlarimizda inqilob yasadi. SI texnologiyasi rivojlanishda davom etar ekan, uning nima ekanligini, turli SI tizimlari qanday ishlashini va ularning maqsadlari, ijobiy va salbiy tomonlarini tushunish juda muhimdir. Algoritmlar sehrli kuchga ega emas va inson aralashuvisiz yo&lsquo;q joydan qaror qabul qila olmaydi. Agar siz yuqori sifatli moslashtirilgan ma&rsquo;lumotlarni taqdim qilmasangiz, hatto eng yetuk algoritm ham sizga mukammal natijani bermaydi. Ushbu maqolada sun&rsquo;iy intellekt dunyosi va turli toifadagi bir nechta mashhur sun&rsquo;iy intellekt xizmatlarini, jumladan, tabiiy tilni qayta ishlash (natural language processing), computer vision va sog&lsquo;liqni saqlashda sun&rsquo;iy intellekt vositalari keltiriladi.</em>

Despite the growing importance of Automatic Speech Recognition (ASR), its application is still challenging, limited, language-dependent, and requires considerable resources. The resources required for ASR are not only technical, they also need to reflect technological trends and cultural diversity. The purpose of this research is to explore ASR performance gaps by a comparative study of American English, German, and Hebrew. Apart from different languages, we also investigate different speaking styles – utterances from spontaneous dialogues and utterances from frontal lectures (TED-like genre). The analysis includes a comparison of the performance of four ASR engines (Google Cloud, Google Search, IBM Watson, and WIT.ai) using four commonly used metrics: Word Error Rate (WER); Character Error Rate (CER); Word Information Lost (WIL); and Match Error Rate (MER). As expected, findings suggest that English ASR systems provide the best results. Contrary to our hypothesis regarding ASR’s low performance for under-resourced languages, we found that the Hebrew and German ASR systems have similar performance. Overall, our findings suggest that ASR performance is language-dependent and system-dependent. Furthermore, ASR may be genre-sensitive, as our results showed for German. This research contributes a valuable insight for improving ubiquitous global consumption and management of knowledge and calls for corporate social responsibility of commercial companies, to develop ASR under Fair, Reasonable, and Non-Discriminatory (FRAND) terms

The development of neural networks began in the 1940s, when scientists began to model the nervous system. However, the real breakthrough occurred in the 2010s with the advancement of machine learning and deep learning. Digitalization, Big Data, and the ability to process large datasets have created a powerful resource for machine learning. By utilizing large amounts of data and powerful computational resources, neural networks have become able to achieve in-credible results in such areas as computer vision for object recognition, natural language pro-cessing, medicine, forecasting, pattern recognition, autonomous vehicles, etc. A neural network is a mathematical model that describes the interdependencies between different factors with mathematical formulas and allows for discovering patterns and dependencies between input da-ta and the final result during training. After training, such a neural network can be used to pre-dict outcomes. The article discusses the principles of neural network operation, the architec-tures of their mathematical models, the security and challenges of neural networks, modern products based on neural networks, and their practical applications. Neural networks are a mod-ern, powerful tool of artificial intelligence capable of modeling and simulating the operation of the human brain. Machine learning and neural networks have been integrated into information technology for many years. One of the leaders in their development is IBM, which was one of the first companies to implement machine learning technologies, based on which IBM Watson operates. A real sensation was the ChatGPT neural network, a language model that generates text based on user queries, taking into account the context of previous queries. Therefore, neural networks are capable of learning based on large volumes of data and recognizing complex de-pendencies in them, so they are used to solve complex problems that cannot always be solved using mathematical formulas or rules.

With the evolving artificial intelligence technology, the chatbots are becoming smarter and faster lately. Chatbots are typically available round the clock providing continuous support and services. A chatbot or a conversational agent is a program or software that can communicate using natural language with humans. The challenge of developing an intelligent chatbot still exists ever since the onset of artificial intelligence. The functionality of chatbots can range from business oriented short conversations to healthcare intervention based longer conversations. However, the primary role that the chatbots have to play is in understanding human utterances in order to respond appropriately. To that end, there is an increased emergence of Natural Language Understanding (NLU) engines by popular cloud service providers. The NLU services identify entities and intents from the user utterances provided as input. Thus, in order to integrate such understanding to a chatbot, this paper presents a study on existing major NLU platforms. Then, we present a case study chatbot integrated with Google DialogFlow and IBM Watson NLU services and discuss their intent recognition performance.

In the companion paper, Watson et al. (1997), we demonstrated the effectiveness of using perceptual criteria to select the amount of detail that is displayed in an immersive virtual reality (VR) system. Based upon this determination, we will now attempt to develop a principled, perceptually oriented framework to automatically select the appropriate level of detail (LOD) for each object in a scene, taking into consideration the limitations of the human visual system. We apply knowledge and theories from the domain of visual perception to the field of VR, thus optimizing the visual information presented to the user based upon solid metrics of human vision. Through a series of contrast grating experiments, a user's visual acuity may be assessed in terms of spatial frequency (c/deg) and contrast. The results of these tests can be modeled mathematically using a contrast sensitivity function (CSF). Therefore, we can use the CSF results to estimate how much visual detail the user can perceive in an object at any instant. Then, if we could describe this object in terms of its spatial frequencies, this would enable us to select the lowest LOD available without the user being able to perceive any visual change.

Among the reasons for traffic accidents, distractions are the most common. Although there are many traffic signs on the road that contribute to safety, variable message signs (VMSs) require special attention, which is transformed into distraction. ADAS (advanced driver assistance system) devices are advanced systems that perceive the environment and provide assistance to the driver for his comfort or safety. This project aims to develop a prototype of a VMS (variable message sign) reading system using machine learning techniques, which are still not used, especially in this aspect. The assistant consists of two parts: a first one that recognizes the signal on the street and another one that extracts its text and transforms it into speech. For the first one, a set of images were labeled in PASCAL VOC format by manual annotations, scraping and data augmentation. With this dataset, the VMS recognition model was trained, a RetinaNet based off of ResNet50 pretrained on the dataset COCO. Firstly, in the reading process, the images were preprocessed and binarized to achieve the best possible quality. Finally, the extraction was done by the Tesseract OCR model in its 4.0 version, and the speech was done by the cloud service of IBM Watson Text to Speech.

The study aims to determine the effect of audio-visual media based on ICT (Information Communication and Technology) on the introduction of English vocabulary in early childhood in group B students of TK Pertiwi Kapuhan 1 Sawangan District, Magelang Regency. This research was carried out using Pre-Experimental with the type of One Group Pretest-Posttest Design by conducting initial and final measurements. The subjects of this study were students of gruop B TK Pertiwi Kapuhan 1 Sawangan District, Magelang Regency in the 2021/2022 academic year totaling 12 students. The sampling technique in this study used purposive sampling. The data collection method is carried out using the performance method and is measured using a performance assesment sheet based on indicators. The analysis technique used is non-parametric with the Wilcoxon Ranks Test using SPSS IBM version 28.0.1.0 (142). The results showed that ICT (Information Communication and Technology) based audio-visual media had an effect on the introduction of English vocabulary in early childhood. This is evidenced by the results of the Wilcoxon ranks test, a Z value of 3.063 is obtained with a significance level of 0.002 based on the results of the attached SPSS calculation with an average comparison of the initial measurement value of 26.5 and the final measurement of 64.9 after being given treatment using ICT (Information Communication and Technology) based audio-visual media. The results of the study can be said that activities using audio-visual media based on ICT (Information Communication and Technology) affect the introduction of English vocabulary in early childhood.

Life writing scholar Julia Watson critiques the practice of genealogy as “in every sense conservative” (300) because it traditionally charts and enshrines a family’s collective biography through biologistic, heteronormative, and segregated routes. My Americanist contribution, however, zooms in on a recent development of autobiographical works that establish narratives of origin beyond normative boundaries of race and heterosexual reproduction. A number of predominantly white queer parents of black adoptees have turned their family history into children’s read-along books as a medium for pedagogical empowerment that employs first-person narration in the presumable voice of the adoptee. In Arwen and Her Daddies (2009), for instance, Arwen invites the reader into a story of family formation with the following opening words: “Do you know how I and my Dads became a family?” My analysis understands these objects as verbal-visual origin stories which render intelligible a conversion from differently radicalized strangers into kin. I frame this mode of narration as ‘adoptee ventriloquism’ that might tell us more about adult desires of queers for familial recognition than about the needs of their adopted children.

Multimodal sentiment analysis models can determine users’ sentiments by utilizing rich information from various sources (e.g., textual, visual, and audio). However, there are two key challenges when deploying the model in real-world environments: (1) the limitations of relying on the performance of automatic speech recognition (ASR) models can lead to errors in recognizing sentiment words, which may mislead the sentiment analysis of the textual modality, and (2) variations in information density across modalities complicate the development of a high-quality fusion framework. To address these challenges, this paper proposes a novel Multimodal Sentiment Word Optimization Module and a heterogeneous hierarchical fusion (MSWOHHF) framework. Specifically, the proposed Multimodal Sentiment Word Optimization Module optimizes the sentiment words extracted from the textual modality by the ASR model, thereby reducing sentiment word recognition errors. In the multimodal fusion phase, a heterogeneous hierarchical fusion network architecture is introduced, which first utilizes a Transformer Aggregation Module to fuse the visual and audio modalities, enhancing the high-level semantic features of each modality. A Cross-Attention Fusion Module then integrates the textual modality with the audiovisual fusion. Next, a Feature-Based Attention Fusion Module is proposed that enables fusion by dynamically tuning the weights of both the combined and unimodal representations. It then predicts sentiment polarity using a nonlinear neural network. Finally, the experimental results on the MOSI-SpeechBrain, MOSI-IBM, and MOSI-iFlytek datasets show that the MSWOHHF outperforms several baselines, demonstrating better performance.

Abstract Robotic Process Automation (RPA) is going into a “maturity market”. The main vendor providers surpassed USD 1 billion in evaluation and the research they are launching these days on the market will change again radically the business landscape. It can be seen already what is coming next to RPA: intelligent optical character recognition (IOCR), chat-bots, machine learning, big data analytics, cognitive platforms, anomaly detection, pattern analysis, voice recognition, data classification and many more. As a result the top vendors developed partnerships with the main leading artificial intelligence providers, such as: IBM Watson, Microsoft Artificial Intelligence, Microsoft Cognitive services, blockchain, Google etc. On the business part, the consulting companies who are implementing the RPA solution are moving from developing Proof-of-Concepts (POCs) and Pilots to helping clients with RAP global roll-outs and developing Centre of Excellences (CoE). As a result, the experiences gathered so far by the author on this kind of projects will be tackled also in this paper. In this article will we will present also some data related to automation for different business areas (eg. Accounts Payable, Accounts Receivable etc) and how an assessment can be done correctly in order to decide if a process can be automatized and, if yes, up to which extent (ie. percent). Moreover, through the case studies we will provide (1) how now the RPA is integrated with Artificial Intelligence and Cloud, (2) how can be scaled in order to face hypes, (3) how can interpret data and (4) what savings these technologies can bring to the organizations. All the aforementioned services made Robotics Process Automation a very powerful tool since a year ago when the author did the last research. A process that was mainly not recommended for automation or was partially automated can be now fully automated with more advantages, such as: money, non-FTE savings and fulfillment time.

This study is grounded in the recognition of the importance of internal factors such as learning motivation and interest in supporting students' academic achievement, particularly in the subject of Basic Vocational Competencies in Visual Communication Design at vocational high schools (SMK). The purpose of this study is to examine the influence of learning motivation and interest on the academic performance of Grade X students in the Visual Communication Design program at SMK Negeri 1 Batipuh. A quantitative approach with a correlational method was employed. The sample consisted of 62 students selected from the entire population of Grade X students. Data were collected using a closed-ended questionnaire previously tested for validity and reliability, and documentation of Mid-Semester Examination (UTS) scores. Data analysis was conducted using IBM SPSS Statistics 2024. The results indicate that students’ motivation and learning interest are in the good category and have a strong, significant relationship with academic achievement, both individually and collectively, with a multiple correlation value of 0.867. The study concludes that learning motivation and interest exert a positive and significant influence on students’ academic performance. These findings imply the necessity of instructional strategies that foster student motivation and interest as a means to enhance academic achievement in vocational education.

Visual communication is ubiquitous, commanding our attention and commandeering our inattention. The presentation of information can take myriad visual forms, such as bar charts, scatter plots, network diagrams, and tables. These information graphics are attempts to map potentially large amounts of complex data to easily navigable visual form for rapid and accurate knowledge transfer. However, there is not yet a satisfactory formal methodology for selecting the most appropriate visualization method for a given set of data. A data taxonomy and novel visual taxonomy will be used to select visual stimuli from a database of acquired and newly generated information graphics. Oculomotor responses (eye tracking data) and task-based responses (mouse clicks or keyboard input) are recorded; performance on the latter is used to establish an expert subgroup. These results will be used to satisfy the three primary objectives of the proposed research, determining: how the choice of data visualization impacts oculomotor behavior and task performance, if this behavior is discriminable between experts and novices, and an empirically-based taxonomy of visualization based on the results of 1 and 2. Intellectual merit of the proposed activity The proposed research will create a novel taxonomy for and database of acquired and generated information graphics as well as an associated web application to search, organize, and compare entries in the database. Part of this research program is intended to establish the most comprehensive, manually annotated (and taxonomically classified) information graphics database in the world, for use by the public via a web interface. These images will be important for procuring stimuli for other kinds of perceptual and cognitive psychology experiments. The eye tracking and task performance results should help lead to a better understanding of how humans look at data, respond to the relationship between data structures and visual composition, and respond differentially to visualizations of different types. With respect to qualifications, the PI has a background in brain imaging research, image processing, and programming applications for generating graphs. Through his collaborator Dr. Ferrera of Columbia University, he has access to facilities and faculty specialized in eye tracking and psychophysics research. Collaborator Dr. Michelle Zhou, a research manager at IBM T. J. Watson Research Center, has years of experience in the areas of data and visual taxonomies, image databases, and automated generation of information graphics [Zhou and Feiner 1998, Zhou et al. 2002b, Zhou et al. 2002a]. Broader impacts of the proposed activity In addition to contributions the image taxonomy, database, and web application are intended to make to research, they will serve as a rich resource for teaching about the history and scope of visualization methods and design within and across disciplines, and for the general public with an interest in information graphics. The research will be conducted on subjects of varied background and race and will be broadly disseminated via websites in addition to publications. Additionally, defining a visual taxonomy will inform design choices made in information visualization. One implication of this research is a determination of how effective different visualization methods are at conveying information; this understanding will be of profound help to anyone interested in conveying information effectively in a graphical form.

Increased prevalence of chronic non-communicable diseases (NCD) and increased related mortality stimulate development of effective methods of their prevention. To date, there are little data on the combined effect of various risk factors on the development of a particular chronic disease, and how much the risk of developing chronic non-communicable diseases increases or decreases with a different combination of risk factors. Purpose. To assess contribution of the combined effect of risk factors into the development of chronic NCD using the method of neural network. Material and methods. Data on 9505 visitors seeking care at the Tomsk health centers were analyzed. To build a multidimensional decision-making model, the authors used the multi-layer perceptron algorithm implemented on the IBM Watson platform. Results. The highest accuracy of disease recognition in the test sample added up to 95.8% for diabetes mellitus. Chronic obstructive pulmonary disease (84.5%) and coronary heart disease (80.4%) rank second. Lower accuracy was registered for such diseases as asthma (73.6%) and arterial hypertension (73.3%). For the development of diabetes mellitus, such factors as patient’s age, level of systolic and diastolic blood pressure, and body mass index (BMI) are equally important. Smoking and gender are identified as the most significant factors for the development of chronic obstructive pulmonary disease. The most significant contribution to the development of arterial hypertension is made by body mass index only. Age and BMI turned out to be most significant for coronary heart disease and arterial hypertension. Conclusion. Use of the neural network method makes it possible to determine contribution of risk factors to the development of chronic ICD, to predict the risk of developing a disease depending on the combination of risk factors and to carry out preventive measures in a personalized manner, taking into account clinical situation of every person. Scope of application. The results of the study can be used by managers of medical organizations to optimize approaches to preventive activities. Keywords: risk factors; chronic non-communicable diseases; neural networks

Background and aim. Stroke remains one of the most common causes of death and disability in the world. The evident decline in disabling outcomes can be attributed to the increasing use of reperfusion therapies. Stroke recognition and urgent admission of patients to specialized stroke centres after the onset of first stroke symptoms are essential for treatment outcomes. This study aims to assess public stroke awareness and its change since 2019.Methods. An anonymous cross-sectional study involving 300 Vilnius residents was conducted in 2022, the results of which were compared with the data of 502 respondents in 2019. A closed-ended questionnaire was used for the study. Statistical analysis was performed using IBM SPSS software with p&lt;0.05 significance level.Results. Stroke was identified as an acute cerebrovascular disorder by 83.3% of respondents in 2022 (82.7% in 2019). At least one correct warning sign of stroke was indicated by 98.7% of respondents (96.4% in 2019). The most commonly mentioned symptoms of stroke were one-sided face, arm or leg sensory disturbances, paralysis, or weakness (90.0%) and speech disorder (83.3%), with 82.1% and 81.5% respectively in 2019. Only 58% (45.4% in 2019) of respondents reported visual impairment as a stroke symptom. Women had better knowledge of stroke than men (p&lt;0.05). The internet was the main source of information for 63.3% of respondents (64.9% in 2019).Conclusions. Since 2019, stroke awareness has improved in Vilnius. This proves that F.A.S.T. campaign has reached the public. Women have better knowledge of stroke than men. Visual impairment is the least known symptom of stroke. Therefore, the search for new information programmes, especially directed to the targeted audience, can be useful for better publicity.

Ekman, P. (2005). Basic Emotions. In Handbook of Cognition and Emotion (p. 45–60). John Wiley &amp; Sons, Ltd. https://doi.org/10.1002/0470013494.ch3 Bachorowski, J.-A., &amp; Owren, M. J. (1995). Vocal Expression of Emotion: Acoustic Properties of Speech Are Associated With Emotional Intensity and Context. Psychological Science, 6(4), 219–224. https://doi.org/10.1111/j.1467-9280.1995.tb00596.x Hirschberg, J. (2006). Pragmatics and Intonation. In The Handbook of Pragmatics (eds L.R. Horn and G. Ward). https://doi.org/10.1002/9780470756959.ch23 Tereykovska, L. (2023). Methodology of automated recognition of the emotional state of listeners of the distance learning system [Dissertation, Kyiv National University of Construction and Architecture]. Institutional repository of National transport university. http://www.ntu.edu.ua/nauka/oprilyudnennya-disertacij/ Kominek, J., &amp; Black, A. (2004). The CMU Arctic speech databases. SSW5-2004. https://www.lti.cs.cmu.edu/sites/default/files/CMU-LTI-03-177-T.pdf (date of access: 01.06.2023) Zhou, K., Sisman, B., Liu, R., &amp; Li, H. (2022). Emotional voice conversion: Theory, databases and ESD. Speech Communication, 137, 1–18. https://doi.org/10.1016/j.specom.2021.11.006 Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., &amp; Weiss, B. (2005). A database of German emotional speech. In Interspeech 2005. ISCA. https://doi.org/10.21437/interspeech.2005-446 Livingstone, S. R., &amp; Russo, F. A. (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE, 13(5), Стаття e0196391. https://doi.org/10.1371/journal.pone.0196391 James, J., Tian, L., &amp; Inez Watson, C. (2018). An Open Source Emotional Speech Corpus for Human Robot Interaction Applications. In Interspeech 2018. ISCA. https://doi.org/10.21437/interspeech.2018-1349 10) Costantini, G., Iaderola, I., Paoloni, A., &amp; Todisco, M. (2014). EMOVO Corpus: an Italian Emotional Speech Database. У Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), 3501–3504, Reykjavik, Iceland. European Language Resources Association (ELRA).

Background:Giant cell arteritis (GCA) is a large vessel vasculitis (LVV) that affects mostly the elderly (1). Temporal artery biopsy (TAB) has been defined as the gold standard, although this has come to question (3). EULAR guidelines recommended non-invasive imaging techniques such as the temporal artery Doppler ultrasound to aid in LVV diagnosis (2). The characterization of GCA in Latin American countries, and specifically in Colombia, is scarce.Objectives:To perform a clinical, demographical, histopathological, and radiological characterization ofColombian GCA patients.Methods:Retrospective data from GCA patients at two teaching hospitals in Medellín, Colombia, was collected. A bioethics committee previously approved the research protocol. Inclusion criteria: diagnosis of GCA, based on clinical characteristics or imaging/biopsy findings. Exclusion criteria: HIV diagnosis, paraneoplastic large vessel vasculitis. Clinical, radiological, and histopathological variables were selected based on the clinicians’ expertise and the 2018 EULAR recommendations for a core data set to support observational research and clinical care in giant cell arteritis (3). The data were analyzed in SPSS v22.0 (IBM, USA).Results:Twenty-two patients were collected. Demographical and clinical characteristics are summarized in Table 1. Most patients were women (68.1%), with a mean age of 71.8 years, mean duration of symptoms of 5.3 months. In the cranial form of the disease, the most common symptoms were headache and jaw claudication (95% and 68%). Six patients experienced visual loss: complete unilateral visual loss in two, partial unilateral in two, complete bilateral in one, and partial bilateral in one patient.Sixteen patients (72.7%) underwent TAB; the most common finding was mononuclear infiltrate and internal elastic fragmentation in 43.7% of the biopsies. Giant cells were observed in 6 biopsies (37.5%). Doppler ultrasound was performed in 19 patients (86.35%), finding intima-media thickening and the halo sign t in 7 patients (36.8%).Table 1.Demographical and clinical characteristicsCharacteristicn = 22Mean age (years SD)71.8 (10.6)Female sex n (%)15 (68.1%)Mean symptoms duration (months ± SD)5.3 ± 6.8Mestizo n (%)20 (90%)Headache n (%)21 (95.4%)Jaw claudication n (%)15 (68.1%)Weight loss n (%)13 (59%)Scalp sensitivity n (%)11 (50%)Fever n (%)8 (36.3%)Presence of polymyalgia rheumatica n (%)7 (31.8%)Visual loss n (%)6 (27.2%)Amaurosis fugax n (%)1 (4.5%)Glucocorticoids n (%)22 (100%)Pulse n (%)8 (36.3%)Mean dose (mg ± SD)47.1 ± 16.7Methotrexate n (%)17 (77.7%)Azathioprine n (%)2 (9%)Tocilizumab n (%)1 (4.5%)Conclusion:In this GCA cohort, the main phenotype was cranial GCA, with 95% of the patients presenting with headache. The most common imaging study performed was Doppler ultrasound of temporal arteries, with intima-media thickening and halo sign being the most common findings. The mainstay of treatment was glucocorticoids with methotrexate as a sparing agent. To our knowledge, this study is the first to perform a specific characterization of GCA patients in the Colombian population, and it can represent the foundation of further research for these patients.References:[1]Lazarewicz K, Watson P. Giant cell arteritis. BMJ. 2019 May 30;365:l1964.[2]Dejaco C, Ramiro S, Duftner C, Besson FL, Bley TA, Blockmans D, et al. EULAR recommendations for the use of imaging in large vessel vasculitis in clinical practice. Ann Rheum Dis. 2018 May;77(5):636–43.[3]Ehlers L, Askling J, Bijlsma HWJ, Cid MC, Cutolo M, Dasgupta B, et al. 2018 EULAR recommendations for a core data set to support observational research and clinical care in giant cell arteritis. Ann Rheum Dis. 2019;78(9):1160–6.Disclosure of Interests:None declared

Background. Cognitive dysfunction, including mild cognitive impairment and dementia, is increasingly recognized as a serious complication of diabetes mellitus (DM) that affects patient well-being and disease management. Magnetic resonance imaging (MRI)-studies have shown varying degrees of cortical atrophy, cerebral infarcts, and deep white matter lesions. To explain the relationship between DM and cognitive decline, several hypotheses have been proposed, based on the variability of glycemia leading to morphometric changes in the brain. The ability to predict cognitive decline even before its clinical development will allow the early prevention of this pathology, as well as to predict the course of the existing pathology and to adjust medication regimens.&#x0D; Aim. To create a computer neural network model for predicting the development of cognitive impairment in DM on the basis of brain neuroimaging techniques.&#x0D; Materials and methods. The study was performed in accordance with the standards of good clinical practice; the protocol was approved by the Ethics Committee. The study included 85 patients with type 1 diabetes and 95 patients with type 2 diabetes, who were divided into a group of patients with normal cognitive function and a group with cognitive impairment. The patient groups were comparable in age and duration of disease. Cognitive impairment was screened using the Montreal Cognitive Assessment Scale. Data for glycemic variability were obtained using continuous glucose monitoring (iPro2, Libre). A standard MRI scan of the brain was performed axially, sagittally, and coronally on a Signa Creator E, GE Healthcare, 1.5 Tesla, China. For MRI data processing we used Free Surfer program (USA) for analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies, and for segmentation we used Recon-all batch program directly. All statistical analyses and data processing were performed using Statistica Statsofi software (version 10) on Windows 7/XP Pro operating systems. The IBM WATSON cognitive system was used to build a neural network model.&#x0D; Results. As a result of the study, cognitive impairment in DM type 1was predominantly of mild degree 36.9% (n=24) and moderate degree 30.76% (n=20), and in DM type 2 mild degree 37% (n=30), moderate degree 49.4% (n=40) and severe degree 13.6% (n=11). Cognitive functions in DM type 1 were impaired in memory and attention, whereas in DM type 2 they were also impaired in tasks of visual-constructive skills, fluency, and abstraction (p0.001). The analysis revealed differences in glycemic variability indices in patients with type 1 and type 2 DM and cognitive impairment. Standard MRI of the brain recorded the presence of white and gray matter changes (gliosis and leukoareosis). General and regional cerebral atrophy is characteristic of type 1 and type 2 DM, which is associated with dysglycemia. When building neural network models for type 1 diabetes, the parameters of decreased volumes of the brain regions determine the development of cognitive impairment by 93.5%, whereas additionally, the coefficients of glycemic variability by 98.5%. The same peculiarity was revealed in type 2 DM 95.3% and 97.9%, respectively.&#x0D; Conclusion. In DM type 1 and type 2 with cognitive impairment, elevated coefficients of glycemic variability are more frequently recorded. This publication describes laboratory and instrumental parameters as potential diagnostic options for effective management of DM and prevention of cognitive impairment. Neural network models using glycemic variability coefficients and MR morphometry allow for predictive diagnosis of cognitive disorders in both types of diabetes.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Schematic (network) maps are helpful for people to perform route planning and orientation tasks. The London Underground Map designed by Harry Beck is an excellent example of such maps. Generally, there are three approaches to generate schematic maps: manual, semi-automated (or computer-aided) and fully automated. In the past twenty years, many researchers have been devoted to the development of automated methods for generation of schematic maps. In these automated methods, various sets of constraints are used. Most of these constraints are for geometric properties of individual features (such as the lengths and orientations of lines); a few constraints are for relations between features (such as the minimum distance threshold between non-incident edges); but none are explicitly for the main structures of whole networks. It is believed that preservation of the main structure is the most important, because main structure is represented by global features which is first recognized by a pre-attentive process in human cognition – a global-to-local process (in which local features are then recognized by an attentive process). It is hypothesized here that an automated method with the preservation of main structures of networks should be able to generate schematic maps with improved clarity and aesthetics.&lt;/p&gt;&lt;p&gt;This paper describes the development of an automated method with the preservation of the main structures of line networks. In this method, automated schematization is treated as an optimization problem and is represented as a Mixed-Integer Programming (MIP) model, which consists of an objective function and a set of constraints. The preservation of main structures is modelled into constraints (i.e., making important lines straight and orientating them to specific directions) for the model. The MIP model is imported into a commercial optimization software called “IBM ILOG CPLEX Optimization Studio” (version 12.6.3) for the acquisition of optimal solutions (i.e., coordinates of vertices and edges on schematic maps). The whole process is shown in Figure 1.&lt;/p&gt;&lt;p&gt;Experimental evaluations have been conducted with a set of real-life data as shown in Figure 2a and 2d. Schematic maps are generated by this new method with the preservation of main structures and by an old method without the particular consideration for main structures, as shown in Figures 2b, 2c, 2e and 2f. A psychological test with a questionnaire has been conducted, which consists of questions regarding “clarity”, “recognition of major lines”, “visual simplicity” and “satisfaction”. It is found that, in all these four aspects, the map generated by new methods with preservation of main structures have higher scores than those by the old method. These improvements are proved to be significant after paired-t tests.&lt;/p&gt;&lt;p&gt;Therefore, it is concluded that the new automated method with the preservation of main structures can generate schematic maps with significant improvement in clarity and aesthetics. This study is helpful to improve automated methods for generation of schematic maps and other visual representations.&lt;/p&gt;

AbstractWith the rapid advancement of innovative technology, coupled with IT being a core function in contemporary business, there has been an upward trend of multi-national companies (MNCs) reporting a skill deficit in areas such as data analytics and cybersecurity (Columbus, 2017. IBM predicts demand for data scientists will soar 28% By 2020. Forbes; NeSmith, 2018. The cybersecurity gap is an industry crisis. Forbes). In a recent survey with over 3,000 CIOs, 65% indicated their organizations were unable to maintain par with the progression of technology in areas such as data analytics and security due to a lack of adequate talent (Harvey Nash &amp;amp; KPMG, 2018. CIO survey 2018). Although, organizations have recently started to expand their talent pipeline following a neurological breakthrough: research as well as anecdotal evidence suggests adults with mild forms of autism display above-average intelligence, increased attention focus, and high visual–spatial abilities; a combination in high market demand for roles such as software testing, data analysis, cybersecurity, and engineering due to their uncanny ability with pattern recognition, information processing, analytics, and attention to detail.These auspicious developments come at the helm of an increasing rate of governments around the world implementing provisions to their labour regulations towards equitable hiring of people with disabilities (Myors et al., 2017. Perspectives from 22 countries on the legal environment for selection. Handbook of Employee Selection. 659–677. Research Collection Lee Kong Chian School of Business.). Some, such as France, Japan, Kenya, Korea, and Taiwan, have gone so far as to set quota targets (Myors et al., 2017. Perspectives from 22 countries on the legal environment for selection. Handbook of Employee Selection. 659–677. Research Collection Lee Kong Chian School of Business.). The implication for organizations is that they need to develop disability-inclusive recruitment and selection systems along with work designs and environments that are disability friendly. But what does this mean in practice? What does a disability-inclusive recruitment and selection system look like?Enter DXC Technology (DXC): born out of a merger between global conglomerate Computer Science Corporation and Hewlett Packard Enterprise, generating close to $25 billion annually in revenue, with clients across more than 70 countries, they strategically became a pioneer in the digital transformation that was taking place globally. In the wake of the breakthrough in employment diversity, DXC recognized this as an opportunity to gain a critical edge within the increasingly competitive talent pool market. First, design a program of their own for recruiting and selecting adults with high functioning autism. Next, through a collaboration with various universities including the University of Queensland and Macquarie University, Neurodiversity Hubs were established; an initiative designed to assist neurodivergent students with obtaining work experience and internships. In doing so, they faced the following key challenges: How could they design a recruitment and selection strategy for neurodivergent individuals that was equitable, ethical, and efficient? In particular, where could they find suitable neurodivergent candidates, what criteria should they use to select them, and how should they handle unsuccessful candidates to ensure beneficial outcomes for all stakeholders?

&#x0D; &#x0D; &#x0D; Character education in early childhood is not new, and character education is also not just a transfer of knowledge, but something that needs to be built early on through various stimula- tions. This study aims to develop the character of early childhood through audio-visual media with traditional Javanese songs. Using educational design-based research to develop audio-visual media from traditional songs, this media was tested in the field with an experimental design with a control group. Respondents involved 71 kindergarten students from one experimental class in one control class. The data revealed that character education in children shows the average value of the experi- mental class is higher than the control group, this means character education in children can be built through traditional songs. Further research can be done to improve the character of early childhood through a variety of media that interests children.&#x0D; Keywords: Early Childhood, Character Education, Javanese Traditional Songs Media&#x0D; References:&#x0D; Anderson, T., &amp; Shattuck, J. (2012). Design-based research: A decade of progress in education research? Educational Researcher, 41(1), 16–25. https://doi.org/10.3102/0013189X11428813&#x0D; Bates, A. (2016). The management of ‘emotional labour’ in the corporate re-imagining of primary education in England. International Studies in Sociology of Education, 26(1), 66–81. https://doi.org/10.1080/09620214.2016.1175959&#x0D; Bates, A. (2019). Character education and the ‘priority of recognition.’ Cambridge Journal of Education, 49(6), 695–710. https://doi.org/10.1080/0305764X.2019.1590529&#x0D; Battistich, V., Schaps, E., Watson, M., Solomon, D., &amp; Lewis, C. (2000). 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Nursery rhymes, songs, and stories for babies. Vancouver: BC: Blacksheep Press.&#x0D; Damon, W. (1988). The moral child: Nurturing children’s natural moral growth. New York: Free press.&#x0D; Derlicki, J. (2005). Ethno-pedagogy - the curse or the cure? The role of the school among youth in Nelemnoe (Yakutia). Sibirica, 4(1), 63–73. https://doi.org/10.1080/13617360500070731&#x0D; Dick, W., &amp; Carey, L. (2009). The Systematic Design of Instruction. New Jersey: Pearson Education.&#x0D; Ecclestone, K. (2012). From emotional and psychological well-being to character education: Challenging policy discourses of behavioural science and “vulnerability.” Research Papers in Education, 27(4), 463–480. https://doi.org/10.1080/02671522.2012.690241&#x0D; Fleer, M., &amp; Hedegaard, M. (2010). Children’s development as participation in everyday practices across different institutions. Mind, Culture, and Activity, 17(2), 149–168. https://doi.org/10.1080/10749030903222760&#x0D; Goodman, J. F. (2019). Searching for character and the role of schools. Ethics and Education, 14(1), 15–35. https://doi.org/10.1080/17449642.2018.1537989&#x0D; Greenberg, M. T., Kusche, C. A., Cook, E. T., &amp; Quamma, J. P. (1995). Promoting emotional competence in school-aged children: The effects of the PATHS curriculum. Development and Psychopathology, 7(1), 117–136. https://doi.org/10.1017/S0954579400006374&#x0D; Hanna, W. (2014). A Reggio-Inspired Music Atelier: Opening the Door Between Visual Arts and Music. Early Childhood Education Journal, 42(4), 287–294. https://doi.org/10.1007/s10643-013-0610-9&#x0D; Harahap, N., Kahar, I. A., &amp; Nasution, L. H. (2018). Preservation of lullabies songs in forming character based on local wisdom. International Journal of Linguistics, Literature and Culture, 5(1), 32–42. https://doi.org/10.21744/ijllc.v5n1.479&#x0D; Hariswari, K. P., &amp; Iswidayanti, S. (2019). 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Abstract Background and Aims Artificial Intelligence (AI) is the branch of computer technology aimed at creating hardware and software systems solving problems similarly to human intelligence, whereas specifically machine learning (ML) is the “field of study that gives computers the ability to learn without being explicitly programmed”. Our study was carried out taking advantage of the Watson Visual Recognition System by IBM, an advanced AI tool based on ML able to classify complex visual content. The aim of the study was to train and test the ability of the system in recognizing sclerotic and non-sclerotic glomeruli. Method A dataset of 26 renal biopsies performed at the Nephrology unit of the Department of Emergency and Organ Transplantation (DETO), University of Bari, Italy, was used for the analysis. All biopsies were stained with Periodic Acid-Schiff (PAS) staining. Each bioptic section was acquired using Aperio ScanScope; glomeruli were identified and sclerotic glomeruli were marked in yellow, non-sclerotic ones in green. Annotations were validated by two renal pathologists. The final dataset consisted of 2772 glomeruli: 428 with sclerosis, 2344 with no sclerosis (ratio 1/5.5). The dataset was divided in three parts: training set (about 70% of the entire dataset), validation set (about 10%) and test set (about 20%). Watson Visual Recognition Service is customizable and the system can be trained on recognizing any visual content. Classifiers are created and trained uploading both positive (in our study, sclerotic glomeruli) and negative (non-sclerotic glomeruli) classes. The IBM Watson learning algorithm is not open, therefore in order to improve the performance of the system, it is necessary to train it with different models and choose the best one. Results We created all the possible models derived from the arrangement of the following 4 variables: color of the image (PAS staining or grey scales), dimension (original or resized), number of images in order to balance the positive and negative classes, binary (one class containing sclerotic glomeruli) or multi-class (two classes: sclerotic and non-sclerotic glomeruli). Every test had a cut-off of 0.5: if &amp;gt;0.5, the system considered the glomerulus belonging to the tested class. After validating all the models with all the variables considered, the best performing model was the following: grey scaled, resized and multi-class. This model was then tested with a different number of input images (300, 600, 900, 1200, and 1600). The models with the most numerous dataset have been created using data augmentation, a technique that virtually increases the number of available samples. Results show that the use of larger input datasets does not yield a better linear performance. In fact, models 900 and 1200 had worst performances than other models, the best performances of the system were reached with model 1600, both in recognizing sclerotic (positive class) and non-sclerotic (negative class) glomeruli (Table 1, Figure 1 and 2). Conclusion In our study, renal biopsy images were analyzed and classified using the IBM Watson Visual Recognition tool which was able to distinguish automatically and with very high accuracy between sclerotic and non-sclerotic glomeruli. This study focuses only on the glomerular compartment and the next step will be the recognition of intermediate lesions and other portion of renal tissue. Our results prove the potential of AI and ML techniques in supporting the activity of renal pathologists.

Object sorting is a very common industrial application but at the same time it is a tiresome process as handling so many objects is a menial task which is not so promising in maintaining consistency and thereby arising quality issues. Object sorting, if done manually, is not only time consuming but also it seems to be an uphill task pragmatically. Nowadays amid various technological advancements, industries have become fully automated so an automated sorting system is essentially required to replace this conventional system of manual sorting knowing that this process can be made completely autonomous by properly channeling the use of technology. The main objective of this paper is to propose a smarter, intelligent and cost-effective object sorting system which categorizes the objects based on their respective color and will place them at their designated locations to minimize the cost and optimize the productivity. We have implemented the sorting system using Raspberry pi (an open-sourced Linux based board) interfaced with a camera module along with some side electronic circuitry such as servo motors and sensors. The color recognition is done using the IBM Watson visual recognition model where we have uploaded the dataset of captured images. For picking and sorting the objects, we have made use of a robotic arm that will rotate with the help of servo motor up to certain angles.

Lack of skill English is a major problem for someone who wants to learn to speak English in the pronunciation section. A person often feels insecure to speak English with other people even though in his own understanding it can be said to understand English. English learning applications, it can help to learn and practice good and correct pronunciation or pronunciation in English without having to involve other people. Using the Extreme Programming development method, it is found that the use of technology can be a solution for learning to speak English without having to be seen and valued by others. With the help of Speech Recognition, the process of recognizing a person's voice from a spoken phrase is the result that can be obtained. This English learning application is used to learn English spelling, so you can know how to pronounce English words and sentences properly and correctly.

IBM Watson API is a kind of speech recognition API system which can automatically generate not only recognized words from voice signal but also generate speaker ID and timing information of each words including the starting time and the ending time. The performance of IBM Watson API is very good at the well-recorded voice signal by the clearly speaking trained speakers but the performance is not enough good when there are some noises in the recorded voice signal. This situation is easily found with movie sounds that include not only speaking voice signal but also background music or special sound effects. This paper deals with a novel method to enhance this informatized caption from IBM Watson API to resolve this noisy signal problem based on speaker pronunciation time-DB. To do this, the proposed method uses the original caption information as an additional input. By comparing the original caption with the output of IBM Watson API, the error words could be automatically detected and correctly modified. And using the speaker pronunciation time-DB containing the average pronunciation time of each word for each speaker, the timing information of each error word could be estimated. In this way, more precisely enhanced informatized captions could be generated based on the IBM Watson API. The usefulness of the proposed method is verified with two case studies with noisy voice signals.

A lot of us are misinformed about trash and how it can be managed. We don’t know what kind of trash can be repurposed or be turned into usable items such jas manure. We tend to throw everything out together without segregating at the source of generation. Through our app, they can simply point towards objects and understand what to do with that trash. The goal is to sensitize people not only to segregate trash but also reduce their waste regeneration through practices like composting and reusing. We should first try to understand the plight of waste management in India and how it can be made better. In this project, we will create a mobile app, Python Server with Flask, and Watson Visual Recognition. This mobile app sends pictures of waste and garbage to be analyzed by a server app, using Watson Visual Recognition. The server application will use pictures of common trash to train Watson Visual Recognition to identify various categories of waste, e.g. recycle, compost, or landfill. The resultant smart dustbin is very effective as people have an urge of curiosity and after learning more about their waste, they always tend to make the right decisions. We aim to make a difference in the society through our app and help in making a cleaner and better tomorrow.

Abstract Objective Big data-based artificial intelligence (AI) is on the way to develop into a part of daily clinical life and its reasonable application could help to improve disease or injury outcomes. A visual polytrauma analytics tool based on IBM WATSON was developed and described in a previous publication. The present article relates to the validation of the IBM WATSON Trauma Pathway Explorer. Methods A retrospective prediction model validation in a level I trauma center including 107 patients with an Injury Severity Score (ISS) ≥16 and age ≥16 was performed. Age, ISS, temperature and the presence of head injury were the predictors used to validate the following three outcomes: SIRS and sepsis within 21 days since admission of the patient, as well as early death within 72 hours since admission. The area under the receiver operating characteristic (ROC) curve was used to determine predictive quality. Calibration plots showed the graphical goodness of fit. The Brier score assessed the overall performance of the two models. Results The area under the curve (AUC) is 0.77 (95% CI: 0.679-0.851) for SIRS, 0.71 (95% CI: 0.578-0.831) for sepsis and 0.90 (95% CI: 0.786-987) for early death. The Brier scores are as follows: early death 0.06, sepsis 0.12 and SIRS 0.15. Conclusion The validation has shown that the predictive performance of WATSON for SIRS and early death corresponds to the clinical outcome in nearly 80% of cases and 90% of cases, respectively. The concordance for sepsis was modest with over 70% of cases. This visual analytics tool for polytrauma patients can be used to obtain valid predictions for SIRS, sepsis and early death. Here, we can present a possible working variant of AI in trauma surgery.

El presente estudio tiene como propuesta elaborar el diseño de un edificio inteligente para los laboratorios ubicados en el pabellón académico de la Facultad de Ingeniería Industrial, Sistemas e Informática de la Universidad Nacional José Faustino Sánchez Carrión, basado en la tecnología inmótica, tecnología que automatiza los edificios con la ayuda de sensores digitales, controladores electrónicos, dispositivos y sistemas de comunicación que registran de manera continua datos de funcionamiento creando la transparencia necesaria para la digitalización. El resultado es diseñar el edificio inteligente para los laboratorios ubicados en el pabellón académico de la Facultad de Ingeniería Industrial, Sistemas e Informática, que permita mejorar la calidad, el confort, bienestar y seguridad por medio de la digitalización y la gestión eficiente de recursos como sensores y controladores de confort visual, lumínico, térmico, acústico, reconocimiento facial, reconocimiento de voz y detección de movimiento entre otros factores, que estarán integrados a la plataforma tecnológica de la universidad y a los servicios de IBM: Watson e internet de las cosas, que es la plataforma de inteligencia artificial para los negocios e internet.Palabras clave: Edificio inteligente; inmótica; IBM Watson, internet de las cosas

El presente estudio tiene como propuesta elaborar el diseño de un edificio inteligente para los laboratorios ubicados en el pabellón académico de la Facultad de Ingeniería Industrial, Sistemas e Informática de la Universidad Nacional José Faustino Sánchez Carrión, basado en la tecnología inmótica, tecnología que automatiza los edificios con la ayuda de sensores digitales, controladores electrónicos, dispositivos y sistemas de comunicación que registran de manera continua datos de funcionamiento creando la transparencia necesaria para la digitalización. El resultado es diseñar el edificio inteligente para los laboratorios ubicados en el pabellón académico de la Facultad de Ingeniería Industrial, Sistemas e Informática, que permita mejorar la calidad, el confort, bienestar y seguridad por medio de la digitalización y la gestión eficiente de recursos como sensores y controladores de confort visual, lumínico, térmico, acústico, reconocimiento facial, reconocimiento de voz y detección de movimiento entre otros factores, que estarán integrados a la plataforma tecnológica de la universidad y a los servicios de IBM: Watson e internet de las cosas, que es la plataforma de inteligencia artificial para los negocios e internet.Palabras clave: Edificio inteligente; inmótica; IBM Watson, internet de las cosas