Алгоритмічне та програмне забезпечення комп’ютерного бачення на прикладі сфери масового обслуговування

Актуальність теми: необхідність розпізнавати небажаних осіб та вік, стать і емоційний стан відвідувачів об’єктів сфери масового обслуговування із фото та відеофайлів з камер спостереження. Мета дослідження: аналіз методів ідентифікації осіб за фото та відеофайлами та отримання іх біометричного шаблону. Для реалізації поставленої мети були сформульовані наступні завдання: дослідження існуючих способів розпізнавання осіб та їх біометричного шаблону у сфері масового обслуговування; дослідження існуючих технічних способів ідентифікації осіб; підбір архітектури бекбоунів для нейронної мережі моделі розпізнавання; підбір навчальних датасетів для тренування моделі розпізнавання; розробка програмного забезпечення, яке використовує спроектовану модель розпізнавання; порівняння результатів реалізованої моделі з існуючими засобами ідентифікації осіб. Об’єкт дослідження: процес ідентифікації особи по обличчю та отримання її біометричного шаблону за допомогою методів машинного навчання. Предмет дослідження: точність та ефективність алгоритмів комп’ютерного бачення для обробки фото та відео з наявною великою кількістю осіб. Методи дослідження: дослідження, аналіз, експеримент. Наукова новизна: найбільш суттєвими науковими результатами магістерської дисертації є реалізація унікального програмного модулю для ідентифікації осіб та отримання їх біометричного шаблону за допомогою сучасних алгоритмів комп’ютерного бачення. Практичне значення отриманих результатів визначається тим, що запропоноване програмне рішення може бути використане на об’єктах сфери масового обслуговування для визначення злочинців та емоційного стану відвідувачів. Зв’язок роботи з науковими програмами, планами, темами: Робота виконувалась на кафедрі автоматизованих систем обробки інформації та управління Національного технічного університету України «Київський політехнічний інститут ім. Ігоря Сікорського» в рамках теми «Методи та технології високопродуктивних обчислень та обробки надвеликих масивів даних». Державний реєстраційний номер 0117U000924. Апробація: Основні положення роботи доповідались і обговорювались на Міжнародному науковому симпозіумі "Інтелектуальні рішення" (IntSol-2019), публікувались у науково-технічного журналі “Сучачний захист інформації” 4(36), 2018, виданні “Захист інформації”, том 21, №3, виданні “Magyar Tudomanyos Journal” №31(2019). Публікації: Наукові положення дисертації опубліковані в матеріалах Міжнародного наукового симпозіума "Інтелектуальні рішення" (IntSol- 2019), науково-технічного журналу “Сучачний захист інформації” 4(36), 2018, видання “Захист інформації”, том 21, №3, видання “Magyar Tudomanyos Journal” №31(2019).Topic relevance: the need to recognize unwanted people and the age, gender and emotional state of visitors of retail locations from photos and videos from surveillance cameras. Research purpose: to analyze the methods of identification of persons by photos and videos and to obtain their biometric portrait. To achieve this goal, the following tasks were formulated: research of existing ways of identifying persons and their biometric pattern in queuing; study of existing technical means of identification of persons; selection of the backbone architecture for the neural network recognition model; selection of training datasets for training model recognition; development of software that uses a designed recognition model; comparison of the results of the implemented model with the existing means of identification of persons. Research object: the process of identifying a person by face and obtaining his biometric template using machine learning methods. Research subject: the accuracy and effectiveness of computer vision algorithms for processing multiple-person photos and videos. Research methods: research, analysis, experiment. Scientific Novelty: the most significant scientific result of a master's thesis is the implementation of a unique software module for identifying individuals and obtaining their biometric template using modern computer vision algorithms. The practical significance of the results obtained is determined by the fact that the proposed algorithmic and software solution can be used in queuing facilities to identify criminals and emotional state of visitors. Relationship with working with scientific programs, plans, topics: The work was performed at the Department of Automated Information Processing and Management Systems of the National Technical University of Ukraine «Kyiv Polytechnic Institute Igor Sikorsky” within the topic “Methods and technologies of high-performance computing and processing of large data sets”. State Registration Number 0117U000924. Testing: The main points of the work were reported and discussed at the International Scientific Symposium "Intelligent Solutions" (IntSol-2019), published in the scientific and technical journal "Modern information protection" 4 (36), 2018, publication "Information protection", volume 21, no. 3, editions of “Magyar Tudomanyos Journal” No. 31 (2019). Publications: Scientific Provisions of the Dissertation Published in Materials of the International Scientific Symposium "Intelligent Solutions" (IntSol-2019), Scientific and Technical Journal "Modern Information Protection" 4 (36), 2018, "Information Security", Volume 21, No.3, Edition “Magyar Tudomanyos Journal” No. 31 (2019)

Mechanisms of Cognitive Impairment in Cerebral Small Vessel Disease: Multimodal MRI Results from the St George's Cognition and Neuroimaging in Stroke (SCANS) Study.

Cerebral small vessel disease (SVD) is a common cause of vascular cognitive impairment. A number of disease features can be assessed on MRI including lacunar infarcts, T2 lesion volume, brain atrophy, and cerebral microbleeds. In addition, diffusion tensor imaging (DTI) is sensitive to disruption of white matter ultrastructure, and recently it has been suggested that additional information on the pattern of damage may be obtained from axial diffusivity, a proposed marker of axonal damage, and radial diffusivity, an indicator of demyelination. We determined the contribution of these whole brain MRI markers to cognitive impairment in SVD. Consecutive patients with lacunar stroke and confluent leukoaraiosis were recruited into the ongoing SCANS study of cognitive impairment in SVD (n = 115), and underwent neuropsychological assessment and multimodal MRI. SVD subjects displayed poor performance on tests of executive function and processing speed. In the SVD group brain volume was lower, white matter hyperintensity volume higher and all diffusion characteristics differed significantly from control subjects (n = 50). On multi-predictor analysis independent predictors of executive function in SVD were lacunar infarct count and diffusivity of normal appearing white matter on DTI. Independent predictors of processing speed were lacunar infarct count and brain atrophy. Radial diffusivity was a stronger DTI predictor than axial diffusivity, suggesting ischaemic demyelination, seen neuropathologically in SVD, may be an important predictor of cognitive impairment in SVD. Our study provides information on the mechanism of cognitive impairment in SVD

Iranian undergraduate non-English majors’ interpretation of english structures

The present study sought to determine whether Iranian non-English major students with or without the experience of attending language institutes, were more influenced by the type of verb or the argument structure patterns in their interpretation of English structures. To answer this question, 100 non-English major participants took part in a grouping task which was designed to reveal the participants' preference in sentence interpretation. Except for those who did not have the required knowledge base and displayed no grouping preference, the participants of the study exhibited three different grouping or sorting strategies in their performance: verb-centered strategy and two types of construction-based performance. The results of a Chi square test indicated that regardless of attending language institutes, the said participants were more inclined to group the structures (i.e., through interpreting them) by relying on the structures' verb types rather than paying attention to the argument structure patterns around which the structures were configured.The implication of these findings is that at least in a foreign language context, a verb valency-based reading strategy is needed to enhance the foreign language learners’ information processing skills. The pedagogical overtones of the findings would affect both teaching activities as well as syllabus design and material development for non- English majors’ English books used in the universit

What happens around earning announcements? An investigation of information asymmetry and trading activity in the Saudi market

This paper examines stock returns and trading activities around earnings announcements for listed companies in the Saudi stock market (SSM). Specifically, we examine the levels of stock liquidity, trading activity, volatility, bid-ask spread, asymmetric information and investor trading behaviour around earnings announcements for all firms in the market for the period 2002-2009. Abnormal price and volume reactions around earnings announcements suggest that these announcements produce highly informative contents. The magnitude of the cumulative abnormal returns around earnings announcement is induced by trading activity in the two weeks before the release date. We also show evidence of an increased adverse selection cost around earnings announcement, which is then gradually reduced in the post-announcement period, indicating that earnings announcements reduce uncertainty in the market. We also examine trading behaviour among small and large investors in the market through constructing order imbalance measures. In general, large investors are more sophisticated and show higher informed trading before earnings announcements whereas smaller investors show stronger reaction to news. Moreover, small investors show a buying pattern which is consistent with times-series based earnings surprise. They are net-buyers for good news and net-sellers for bad news portfolios

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text Summarization

In Automatic Text Summarization, preprocessing is an important phase to reduce the space of textual representation. Classically, stemming and lemmatization have been widely used for normalizing words. However, even using normalization on large texts, the curse of dimensionality can disturb the performance of summarizers. This paper describes a new method for normalization of words to further reduce the space of representation. We propose to reduce each word to its initial letters, as a form of Ultra-stemming. The results show that Ultra-stemming not only preserve the content of summaries produced by this representation, but often the performances of the systems can be dramatically improved. Summaries on trilingual corpora were evaluated automatically with Fresa. Results confirm an increase in the performance, regardless of summarizer system used.Comment: 22 pages, 12 figures, 9 table

Hypermedia-based discovery for source selection using low-cost linked data interfaces

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness