Search results for: deep deterministic policy gradient (DDPG)

Authors: Sajid Hussain

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The complex domain approach for image segmentation based on active contour has been designed, which deforms step by step to partition an image into numerous expedient regions. A novel region-based trigonometric complex pressure force function is proposed, which propagates around the region of interest using image forces. The signed trigonometric force function controls the propagation of the active contour and the active contour stops on the exact edges of the object accurately. The proposed model makes the level set function binary and uses Gaussian smoothing kernel to adjust and escape the re-initialization procedure. The working principle of the proposed model is as follows: The real image data is transformed into complex data by iota (i) times of image data and the average iota (i) times of horizontal and vertical components of the gradient of image data is inserted in the proposed model to catch complex gradient of the image data. A simple finite difference mathematical technique has been used to implement the proposed model. The efficiency and robustness of the proposed model have been verified and compared with other state-of-the-art models.

Keywords: image segmentation, active contour, level set, Mumford and Shah model

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Authors: Ehsan Mehryaar, Reza Bushehri

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One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error.

Keywords: XGBoost, hyper-parameter tuning, soil shear modulus, dynamic response

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Authors: Shuaibu Umar Abdul

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The essence of any state as well as government is to ensure and advance the security of lives and property of its citizens. As a result, providing security in all spheres ranging from safeguarding the territorial integrity, security of lives and property of the citizens as well as economic emancipation have constitute the core objectives cum national interest of virtually all country’s foreign policy in the world. In view of this imperative above, Nigeria has enshrined in the early part of her 1999 constitution as amended, as its duty and responsibility as a state, to ensure security of lives and property of its citizens. Yet, it does not make any significant shift as it relates to the country’s fundamental security needs as exemplified by the current enormous security challenges that reduced the country’s fortune to the background in all ramifications. The study chooses realist paradigm as theoretical underpinning which emphasizes that exigency of the moment should always take priority in the pursuit of foreign policy. The study is historical, descriptive and narrative in method and character. Data for the study was sourced from secondary sources and analysed via content analysis. The study found out that it is lack of political will on the side of the government to guarantee a just and egalitarian society that will be of benefit to all citizens. This could be more appreciated when looking at the gaps between the theory in Nigerian foreign policy and the practice as exemplified by the action or inaction of the government to ensure security in the state. On this account, the study recommends that until the leaderships in Nigerian foreign policy recognized the need for political will and respect for constitutionalism to ensure security of its citizens and territory, otherwise achieving great Nigeria will remain an illusion.

Keywords: foreign policy, nation, national security, Nigeria, security

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Authors: Charles Asenime, Asaju Joel, Fagbenro Abiola, Adetoyese Oguntimehin, Agosu Rebecca

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This paper examines the fundamental principles of the National Transport Policy (NTP) and determined its role in the execution of transport projects, and the establishment of ministries, departments, and agencies. Data used for the paper are from secondary sources of commissioned reports, studies, internet sources, and government releases. Results of the analysis show that the draft NTP has been used to establish transport schemes, master plans, and transport infrastructure. The paper concludes that though, the national transport Policy is still in a draft form, its production, however, has shaped the transport system in Nigeria and has shown how transport has improved the economy through the efficient utilisation of resources, improved mobility, and lifestyle.

Keywords: principles, draft, system, resources

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Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

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Authors: Derejaw Lake Melie, Alemu Kumlachew Tegegne

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The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment.

Keywords: negative emotion, emotion detection, social media filtering sentiment analysis, deep learning.

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Authors: A. Abbou, V. Makis, N. Salari

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We study the condition-based maintenance (CBM) problem of a system subject to stochastic deterioration. The system is composed of three units (or modules): (i) Module 1 deterioration follows a Markov process with two operational states and one failure state. The operational states are partially observable through periodic condition monitoring. (ii) Module 2 deterioration follows a Gamma process with a known failure threshold. The deterioration level of this module is fully observable through periodic inspections. (iii) Only the operating age information is available of Module 3. The lifetime of this module has a general distribution. A CBM policy prescribes when to initiate a maintenance intervention and which modules to repair during intervention. Our objective is to determine the optimal CBM policy minimizing the long-run expected average cost of operating the system. This is achieved by formulating a Markov decision process (MDP) and developing the value iteration algorithm for solving the MDP. We provide numerical examples illustrating the cost-effectiveness of the optimal CBM policy through a comparison with heuristic policies commonly found in the literature.

Keywords: reliability, maintenance optimization, Markov decision process, heuristics

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

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Authors: Paraskevi Liosatou

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Climate change is considered to be of global concern being amplified by the fact that by its nature, cannot be spatially limited. This fact makes necessary the intergovernmental decision-making procedures. In the intergovernmental level, the institutions such as the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change develop efforts, methods, and practices in order to plan and suggest climate mitigation and adaptation measures. These measures are based on specific scientific findings and methods making clear the strong connection between science and policy. In particular, these scientific recommendations offer a series of practices, methods, and choices mitigating the problem by aiming at the indirect mitigation of the causes and the factors amplifying climate change. Moreover, modern production and economic context do not take into consideration the social, political, environmental and spatial dimensions of the problem. This work studies the decision-making process working in international and European level. In this context, this work considers the policy tools that have been implemented by various intergovernmental organizations. The methodology followed is based mainly on the critical study of standards and process concerning the connections and cooperation between science and policy as well as considering the skeptic debates developed. The finding of this work focuses on the links between science and policy developed by the institutional and scientific mechanisms concerning climate change mitigation. It also analyses the dimensions and the factors of the science-policy framework; in this way, it points out the causes that maintain skepticism in current scientific circles.

Keywords: climate change, climate change mitigation, climate change skepticism, IPCC, skepticism

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Authors: Tokihiko Akita, Seiichi Mita

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A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.

Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation

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Authors: Yuna Lee

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In the current policy process, there has been a growing interest in more open approaches that incorporate creativity and innovation based on the forecasting groups composed by the public and experts together into scientific data-driven foresight methods to implement more effective policymaking. Especially, citizen participation as collective intelligence in policymaking with design and deep scale of innovation at the global level has been developed and human-centred design thinking is considered as one of the most promising methods for strategic foresight. Yet, there is a lack of a common theoretical foundation for a comprehensive approach for the current situation of and post-COVID-19 era, and substantial changes in policymaking practice are insignificant and ongoing with trial and error. This project hypothesized that rigorously developed policy systems and tools that support strategic foresight by considering the public understanding could maximize ways to create new possibilities for a preferable future, however, it must involve a better understating of Behavioural Insights, including individual and cultural values, profit motives and needs, and psychological motivations, for implementing holistic and multilateral foresight and creating more positive possibilities. To what extent is the policymaking system theoretically possible that incorporates the holistic and comprehensive foresight and policy process implementation, assuming that theory and practice, in reality, are different and not connected? What components and environmental conditions should be included in the strategic foresight system to enhance the capacity of decision from policymakers to predict alternative futures, or detect uncertainties of the future more accurately? And, compared to the required environmental condition, what are the environmental vulnerabilities of the current policymaking system? In this light, this research contemplates the question of how effectively policymaking practices have been implemented through the synthesis of scientific, technology-oriented innovation with the strategic design for tackling complex societal challenges and devising more significant insights to make society greener and more liveable. Here, this study conceptualizes the notions of a new collaborative way of strategic foresight that aims to maximize mutual benefits between policy actors and citizens through the cooperation stemming from evolutionary game theory. This study applies mixed methodology, including interviews of policy experts, with the case in which digital transformation and strategic design provided future-oriented solutions or directions to cities’ sustainable development goals and society-wide urgent challenges such as COVID-19. As a result, artistic and sensual interpreting capabilities through strategic design promote a concrete form of ideas toward a stable connection from the present to the future and enhance the understanding and active cooperation among decision-makers, stakeholders, and citizens. Ultimately, an improved theoretical foundation proposed in this study is expected to help strategically respond to the highly interconnected future changes of the post-COVID-19 world.

Keywords: policymaking, strategic design, sustainable innovation, evolution of cooperation

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Authors: D. Calogine, O. Chau, S. Dotti, O. Ramiarinjanahary, P. Rasoavonjy, F. Tovondahiniriko

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Mafate is a natural circus in the north-western part of Reunion Island, without an electrical grid and road network. A micro-grid concept is being experimented in this area, composed of a photovoltaic production combined with electrochemical batteries, in order to meet the local population for self-consumption of electricity demands. This work develops a discrete model as well as a stochastic model in order to reach an optimal equilibrium between production and consumptions for a cluster of houses. The management of the energy power leads to a large linearized programming system, where the time interval of interest is 24 hours The experimental data are solar production, storage energy, and the parameters of the different electrical devices and batteries. The unknown variables to evaluate are the consumptions of the various electrical services, the energy drawn from and stored in the batteries, and the inhabitants’ planning wishes. The objective is to fit the solar production to the electrical consumption of the inhabitants, with an optimal use of the energies in the batteries by satisfying as widely as possible the users' planning requirements. In the discrete model, the different parameters and solutions of the linear programming system are deterministic scalars. Whereas in the stochastic approach, the data parameters and the linear programming solutions become random variables, then the distributions of which could be imposed or established by estimation from samples of real observations or from samples of optimal discrete equilibrium solutions.

Keywords: photovoltaic production, power consumption, battery storage resources, random variables, stochastic modeling, estimations of probability distributions, mixed integer linear programming, smart micro-grid, self-consumption of electricity.

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Authors: Drago Jakovcevic, Mihovil Andelinovic, Igor Husak

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The aim of this paper is to explore the economic circumstances in which the selective credit policy, the least used instrument of four types of instruments on disposal to central banks, should be used. The most significant example includes the use of selective credit policies in response to the emergence of the global financial crisis by the FED. Specifics of the potential use of selective credit policies as the instigator of economic growth in Croatia, a small open economy, are determined by high euroization of financial system, fixed exchange rate and long-term trend growth of external debt that is related to the need to maintain high levels of foreign reserves. In such conditions, the classic forms of selective credit policies are unsuitable for the introduction. Several alternative approaches to implement selective credit policies are examined in this paper. Also, thorough analysis of distribution of selective monetary policy loans among economic sectors in Croatia is conducted in order to minimize the risk of investing funds and maximize the return, in order to influence the GDP growth.

Keywords: global crisis, selective credit policy, small open economy, Croatia

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Avalloy McCarthy-Curvin

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For over fifty years there has been widespread dissatisfaction with the teaching of Mathematics in Jamaica. Studies, done in the Jamaican context highlight that teachers at the end of training do not have a deep understanding of the mathematics content they teach. Little research has been done in the Jamaican context that targets the advancement of contextual knowledge on the problem to ultimately provide a solution. The aim of the study is to identify what influences this outcome of teacher education in Jamaica so as to remedy the problem. This study formatively evaluated the curriculum documents, assessments and the delivery of the curriculum that are being used in teacher training institutions in Jamaica to determine their rigor -the extent to which written document, instruction, and the assessments focused on enabling pre-service teachers to develop deep understanding of mathematics and relevance- the extent to which the curriculum document, instruction, and the assessments are focus on developing the requisite knowledge for teaching mathematics. The findings show that neither the curriculum document, instruction nor assessments ensure rigor and enable pre-service teachers to develop the knowledge and skills they need to teach mathematics effectively.

Keywords: relevance, rigor, deep understanding, formative evaluation

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Yuanhao Gao, Pin Lin, Kees Weijer

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In this paper, we will determine the external body force distribution with analysis of stokes fluid motion using mathematical modelling and numerical approaching. The body force distribution is regarded as the unknown variable and could be determined by the idea of optimal control theory. The Stokes flow motion and its velocity are generated by given forces in a unit square domain. A regularized objective functional is built to match the numerical result of flow velocity with the generated velocity data. So that the force distribution could be determined by minimizing the value of objective functional, which is also the difference between the numerical and experimental velocity. Then after utilizing the Lagrange multiplier method, some partial differential equations are formulated consisting the optimal control system to solve. Finite element method and conjugate gradient method are used to discretize equations and deduce the iterative expression of target body force to compute the velocity numerically and body force distribution. Programming environment FreeFEM++ supports the implementation of this model.

Keywords: optimal control model, Stokes equation, finite element method, conjugate gradient method

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Authors: Manel Langar, Riad Bourguiba, Jaouhar Mouine

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In this paper, we propose the design and implementation of an enhanced wormhole virtual channel on chip router. It is a heart of a mesh NoC using the XY deterministic routing algorithm. It is characterized by its simple virtual channel allocation strategy which allows reducing area and complexity of connections without affecting the performance. We implemented our router on a Tezzaron process to validate its performances. This router is a basic element that will be used later to design a 3D mesh NoC.

Keywords: NoC, mesh, router, 3D NoC

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Authors: Ephraim Ugwu, Christopher Ehinomen

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Despite efforts by the Nigerian government to harmonize the macroeconomic policy implementations by establishing various committees to resolve disputes between the fiscal and monetary authorities, it is still evident that the federal government had continued its expansionary policy by increasing spending, thus creating huge budget deficit. This study evaluates the effect of macroeconomic policy coordination on economic growth uncertainty in Nigeria from 1980 to 2020. Employing the Auto regressive distributed lag (ARDL) bound testing procedures, the empirical results shows that the error correction term, ECM(-1), indicates a negative sign and is significant statistically with the t-statistic value of (-5.612882 ). Therefore, the gap between long run equilibrium value and the actual value of the dependent variable is corrected with speed of adjustment equal to 77% yearly. The long run coefficient results showed that the estimated coefficients of the intercept term indicates that other things remains the same (ceteris paribus), the economics growth uncertainty will continue reduce by 7.32%. The coefficient of the fiscal policy variable, PUBEXP, indicates a positive sign and significant statistically. This implies that as the government expenditure increases by 1%, economic growth uncertainty will increase by 1.67%. The coefficient of monetary policy variable MS also indicates a positive sign and insignificant statistically. The coefficients of merchandise trade variable, TRADE and exchange rate EXR show negative signs and significant statistically. This indicate that as the country’s merchandise trade and the rate of exchange increases by 1%, the economic growth uncertainty reduces by 0.38% and 0.06%, respectively. This study, therefore, advocate for proper coordination of monetary, fiscal and exchange rate policies in order to actualize the goal of achieving a stable economic growth.

Keywords: macroeconomic, policy coordination, growth uncertainty, ARDL, Nigeria

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Authors: M. Moso-Diez, J. L. Moragues-Oregi, K. Simon-Elorz

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Innovation procurement is designed to steer the development of solutions towards concrete public sector needs as a driver for innovation from the demand side (in public services as well as in market opportunities for companies), is horizontally emerging as a new policy instrument. In 2014 the new EU public procurement directives 2014/24/EC and 2014/25/EC reinforced the support for Public Procurement for Innovation, dedicating funding instruments that can be used across all areas supported by Horizon 2020, and targeting potential buyers of innovative solutions: groups of public procurers with similar needs. Under this programme, new policy adapters and networks emerge, aiming to embed innovation criteria into new procurement processes. As these initiatives are in process, research related to is scarce. We argue that Innovation Public Procurement can arise as an innovative policy instrument to public procurement in different policy domains, in spite of existing institutional and cultural barriers (legal guarantee versus innovation). The presentation combines insights from public procurement to supply management chain management in a sustainability and innovation policy arena, as a means of providing understanding of: (1) the circumstances that emerge; (2) the relationship between public and private actors; and (3) the emerging capacities in the definition of the agenda. The policy adopters are the contracting authorities that mainly are at municipal level where they interact with the supply management chain, interconnecting sustainability and climate measures with other policy priorities such as innovation and urban planning; and through the Competitive Dialogue procedure. We found that geography and territory affect both the level of municipal budget (due to municipal income per capita) and its institutional competencies (due to demographic reasons). In spite of the relevance of institutional determinants for public procurement, other factors play an important role such as human factors as well as both public policy and private intervention. The experience is a ‘city project’ (Bilbao) in the field of brownfield decontamination. Brownfield sites typically refer to abandoned or underused industrial and commercial properties—such as old process plants, mining sites, and landfills—that are available but contain low levels of environmental contaminants that may complicate reuse or redevelopment of the land. This article concludes that Innovation Public Procurement in sustainability and climate issues should be further developed both as a policy instrument and as a policy research line that could enable further relevant changes in public procurement as well as in climate innovation.

Keywords: innovation, city projects, public policy, public procurement

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: G. A. Kuanaliyeva, G. T. Aigarinova, G. K. Shulanbekova

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This article is devoted to judicial control in criminal legal proceedings of the Republic of Kazakhstan in the light of the new Concept of legal policy till 2020. In article the general characteristic and concept of judicial control, and also its signs and types are considered. Different views of scientists are analyzed. Foreign experiment on application of judicial control is given. The author states also the point sight on this problem and gives the definition to concept of judicial control. The code of criminal procedure of the Republic of Kazakhstan (RK Criminal Procedure Code) doesn't consolidate concept of judicial control. The author in article suggests making change and addition to the existing Code of criminal procedure of the Republic of Kazakhstan by definition of judicial control. The decree of the President of the Republic of Kazakhstan of August 24, 2009 No. 858 approved the Concept of legal policy of the Republic for the period from 2010 to 2020. The new Concept of legal policy of the Republic of Kazakhstan, defines prospects of development of national legal system of the country on the following decade. The concept of legal policy completely mentions also institute of judicial control. Since finding of the independence by Kazakhstan the set of laws, including the rights directed on providing, freedoms and a legitimate interest of citizens was accepted. Certainly, in any country, whatever democratic it was, there are problems to human rights. However, it is obvious that Kazakhstan strongly intends to guarantee all Republic of Kazakhstan proclaimed in the Constitution the rights and freedoms of the citizens. Our country seeks for creation of the constitutional state, tries to provide a guarantee from various arbitrariness in activity of competent government bodies, officials. In the concept of legal policy of the Republic of Kazakhstan it is specified: "...priority of development of the criminal procedure right there is a further consecutive realization of the fundamental principles of the criminal legal proceedings directed on protection of the rights and freedoms of the person". Judicial control just also is such guarantee.

Keywords: rights and freedoms of the person, concept, legal policy, court, judicial control

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Authors: Victor Oyewumi Ogunbiyi

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Food waste is recognised as one of the major global challenges in achieving a sustainable future. Currently, very little is known about the multi-stakeholder approach to food waste management downstream of the supply chain, particularly in the foodservice sector. In order to better understand and explain the complex issues of food waste, a qualitative study was conducted on the generation of food waste in food services (restaurants, catering, canteens, and local food vendors) and policy initiatives to mitigate it from the perspective of the stakeholders. A semi-structured interview approach and observation were used to collect data from some 32 selected stakeholders in Garki, Abuja, Nigeria. Thematic analysis was employed to analyse the data from the qualitative instrument adopted in this study. Results revealed that the attitude of stakeholders, poor environmental hygiene, poor food cooking skills and handling, and lack of communication are the major causes of food waste. This study identified seven policy initiatives: regulations, information and education campaigns, economic instruments, mobile applications, stakeholders’ collaboration, firm internal action, and training. Finally, we link policy initiatives to food waste mitigation to provide a response to the damaging shock of food waste.

Keywords: food waste, foodservices, emerging opportunities, policy initiatives, food waste prevention, multistakeholder. garki district-abuja

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Lidija Jevrić, Evgenija Djurendić, Jovana Ajduković

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In the present study, the molecular modeling of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives whit significant anticancer activity was carried out. Modelling of studied compounds was performed by CS ChemBioDraw Ultra v12.0 program for drawing 2D molecular structures and CS ChemBio3D Ultra v12.0 for 3D molecular modelling. The obtained 3D structures were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. Full geometry optimization was done by the Austin Model 1 (AM1) until the root mean square (RMS) gradient reached a value smaller than 0.0001 kcal/Åmol using Molecular Orbital Package (MOPAC) program. The obtained physicochemical, lipophilicity and topological descriptors were used for analysis of molecular similarities and dissimilarities applying suitable chemometric methods (principal component analysis and cluster analysis). These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1306.

Keywords: androstane derivatives, anticancer activity, chemometrics, molecular descriptors

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Authors: S. Zeynep Siretioglu Girgin, Gizem Turna Cebeci

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Turkey’s immigration policy is a controversial issue considering its legal, economic, social, and political and human rights dimensions. Formulation of an immigration policy goes hand in hand with political processes, where natives’ attitudes play a significant role. On the other hand, as was the case in Turkey, radical changes made in immigration policy or policies lacking transparency may cause severe reactions by the host society. The underlying discussion paper aims to analyze quantitatively the effects of the existing ‘open door’ immigration policy on the economic integration of Syrian refugees in Turkey, and on the perception of the native population of refugees. For the analysis, semi-structured in-depth interviews and focus group interviews have been conducted. After the introduction, a literature review is provided, followed by theoretical background on the explanation of natives’ attitudes towards immigrants. In the next section, a qualitative analysis of natives’ attitudes towards Syrian refugees is presented with the subtopics of (i) awareness, general opinions and expectations, (ii) open-door policy and management of the migration process, (iii) perception of positive and negative impacts of immigration, (iv) economic integration, and (v) cultural similarity. Results indicate that, natives concurrently have social, economic and security concerns regarding refugees, while difficulties regarding security and economic integration of refugees stand out. Socio-economic characteristics of the respondents, such as the educational level and employment status, are not sufficient to explain the overall attitudes towards refugees, while they can be used to explain the awareness of the respondents and the priority of the concerns felt.

Keywords: economic integration, immigration policy, integration policies, migrants, natives’ sentiments, perception, Syrian refugees, Turkey

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Authors: Min Jindong, Wang Mingxia

Abstract:

With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: Yung-Ching Chou, Albert Shangpao Yeh, Luke H. C. Hsiao

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Police used to be an honor job. However, the police are no longer concerned about the mission and public safety instead of the issue of retirement. The main reason is the amendment of 'Public Servants Retirement Act' in Taiwan was effective since January 2011. The purposes of change were to solve the problem of the financial crisis which caused by the Hugh deficit of the civil servants pension fund. The policy of the civil servants pension reform was not only seriously impact the motives of policy, but also negatively impact the workforce of police. This research conducted a secondary data of Baoanjingcha Fifth Police Corps during the period between 2011 and 2015. Secondly, the research interviewed six representatives from the retired police in order to explore the retirement motives. In short, there were several major findings and suggestions in the following: 1. The police won't choice to retire which the nature of task is simple. 2. The ranking level of positions positively correlated with the retired age of police. 3. The police officers who are categorized as 'hazardous work' first class personnel should decrease the standard of the retirement age and allow the option of a monthly pension. 4. The information of the retirees' rights, as well as protection, are correlated with the service as well professional of personnel officer. More findings, as well as suggestions, will be elaborated on the content of this paper.

Keywords: human resource management, pension policy change, police retirement rush, public servants retirement act

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

Abstract:

A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Jose Neri, Fabrice Canto, Alastair Magnaldo, Laurent Guillerme, Vincent Dugas

Abstract:

A miniaturized and automated approach for the volumetric titration of free nitric acid in U(VI) solutions is presented. Free acidity measurement refers to the acidity quantification in solutions containing hydrolysable heavy metal ions such as U(VI), U(IV) or Pu(IV) without taking into account the acidity contribution from the hydrolysis of such metal ions. It is, in fact, an operation having an essential role for the control of the nuclear fuel recycling process. The main objective behind the technical optimization of the actual ‘beaker’ method was to reduce the amount of radioactive substance to be handled by the laboratory personnel, to ease the instrumentation adjustability within a glove-box environment and to allow a high-throughput analysis for conducting more cost-effective operations. The measurement technique is based on the concept of the Taylor-Aris dispersion in order to create inside of a 200 μm x 5cm circular cylindrical micro-channel a linear concentration gradient in less than a second. The proposed analytical methodology relies on the actinide complexation using pH 5.6 sodium oxalate solution and subsequent alkalimetric titration of nitric acid with sodium hydroxide. The titration process is followed with a CCD camera for fluorescence detection; the neutralization boundary can be visualized in a detection range of 500nm- 600nm thanks to the addition of a pH sensitive fluorophore. The operating principle of the developed device allows the active generation of linear concentration gradients using a single cylindrical micro channel. This feature simplifies the fabrication and ease of use of the micro device, as it does not need a complex micro channel network or passive mixers to generate the chemical gradient. Moreover, since the linear gradient is determined by the liquid reagents input pressure, its generation can be fully achieved in faster intervals than one second, being a more timely-efficient gradient generation process compared to other source-sink passive diffusion devices. The resulting linear gradient generator device was therefore adapted to perform for the first time, a volumetric titration on a chip where the amount of reagents used is fixed to the total volume of the micro channel, avoiding an important waste generation like in other flow-based titration techniques. The associated analytical method is automated and its linearity has been proven for the free acidity determination of U(VI) samples containing up to 0.5M of actinide ion and nitric acid in a concentration range of 0.5M to 3M. In addition to automation, the developed analytical methodology and technique greatly improves the standard off-line oxalate complexation and alkalimetric titration method by reducing a thousand fold the required sample volume, forty times the nuclear waste per analysis as well as the analysis time by eight-fold. The developed device represents, therefore, a great step towards an easy-to-handle nuclear-related application, which in the short term could be used to improve laboratory safety as much as to reduce the environmental impact of the radioanalytical chain.

Keywords: free acidity, lab-on-a-chip, linear concentration gradient, Taylor-Aris dispersion, volumetric titration

Procedia PDF Downloads 387