Search results for: muscle architecture

Authors: Hoai-Vu Nguyen, Yongsun Choi

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Distributed denial-of-service (DDoS) attacks pose a serious threat to network security. There have been a lot of methodologies and tools devised to detect DDoS attacks and reduce the damage they cause. Still, most of the methods cannot simultaneously achieve (1) efficient detection with a small number of false alarms and (2) real-time transfer of packets. Here, we introduce a method for proactive detection of DDoS attacks, by classifying the network status, to be utilized in the detection stage of the proposed anti-DDoS framework. Initially, we analyse the DDoS architecture and obtain details of its phases. Then, we investigate the procedures of DDoS attacks and select variables based on these features. Finally, we apply the k-nearest neighbour (k-NN) method to classify the network status into each phase of DDoS attack. The simulation result showed that each phase of the attack scenario is classified well and we could detect DDoS attack in the early stage.

Keywords: distributed denial-of-service (DDoS), k-nearestneighbor classifier (k-NN), anti-DDoS framework, DDoS detection.

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Authors: Arun Prasad Jaganathan

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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper presents an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: Artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization.

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Authors: Minwoo Son, Seung-Hun Lee, Dongkyoo Shin, Dongil Shin

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The next stage of the home networking environment is supposed to be ubiquitous, where each piece of material is equipped with an RFID (Radio Frequency Identification) tag. To fully support the ubiquitous environment, home networking middleware should be able to recommend home services based on a user-s interests and efficiently manage information on service usage profiles for the users. Therefore, USN (Ubiquitous Sensor Network) technology, which recognizes and manages a appliance-s state-information (location, capabilities, and so on) by connecting RFID tags is considered. The Intelligent Multi-Agent Middleware (IMAM) architecture was proposed to intelligently manage the mobile RFID-based home networking and to automatically supply information about home services that match a user-s interests. Evaluation results for personalization services for IMAM using Bayesian-Net and Decision Trees are presented.

Keywords: Intelligent Agents, Home Network, Mobile RFID, Intelligent Middleware.

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Authors: Sean Paulsen, Michael Casey

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In this work, we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: Transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training.

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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

Keywords: Hybrid systems, Hidden Markov Models, Recurrent neural networks, Deterministic finite state automata.

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Authors: Shobha Sharma, Amita Dev, Akanksha Kant

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Digital signal processor, image signal processor and FIR filters have multipliers as an important part of their design. On the basis of Vedic mathematics, Vedic multipliers have come out to be very fast multipliers. One of the image processing applications is edge detection. This research presents a small area and high speed 8 bit Vedic multiplier system comprising of compressor based adders. This results in faster edge detection. This architecture is tested on Xilinx vertex 4 FPGA board and simulations were carried out using the Xilinx synthesis tool. Comparisons are made and this system is found to be smaller in area with high speed (the lesser propagation delay). This compressor based Vedic multiplier is 1.1 times speedier than a typical Vedic multiplier. Also, this Vedic Multiplier is 2 times speedier than a ‘simple’ multiplier.

Keywords: Detection of edges, Vedic multiplier, image processing, Urdhva Tiryakbhyam sutra.

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Authors: A. P. Crowle

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Floating offshore wind farms may provide in the future large quantities of renewable energy. One of the challenges to their future development is the provision of installation vessels for the offshore installation of floating wind turbines. This paper examines the current fleet of vessels that can be used for inshore construction. Separate vessels are required for the ocean tow out and the offshore installation. Information will be provided on what new vessels might be required to improve the efficiency and reduce costs of installing floating wind turbines. Specialized cargo vessels are required for this initial mobilization. Anchor handling vessels are required to tow the floating wind turbine offshore and to install and connect the moorings. Subsea work vessels are required to install the dynamic cables whilst cable lay vessels are required for the export power cable. This paper reviews the existing and future installation vessel requirement for floating wind. Dedicated ports are required for vertical integration of the substructure and the tower, nacelle and blades.

Keywords: Floating wind, naval architecture, offshore installation vessels, ports for renewable energy.

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Authors: Suprijanto, Linda Sari, Vebi Nadhira , IGN. Merthayasa. Farida I.M

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Detection of human emotions has many potential applications. One of application is to quantify attentiveness audience in order evaluate acoustic quality in concern hall. The subjective audio preference that based on from audience is used. To obtain fairness evaluation of acoustic quality, the research proposed system for multimodal emotion detection; one modality based on brain signals that measured using electroencephalogram (EEG) and the second modality is sequences of facial images. In the experiment, an audio signal was customized which consist of normal and disorder sounds. Furthermore, an audio signal was played in order to stimulate positive/negative emotion feedback of volunteers. EEG signal from temporal lobes, i.e. T3 and T4 was used to measured brain response and sequence of facial image was used to monitoring facial expression during volunteer hearing audio signal. On EEG signal, feature was extracted from change information in brain wave, particularly in alpha and beta wave. Feature of facial expression was extracted based on analysis of motion images. We implement an advance optical flow method to detect the most active facial muscle form normal to other emotion expression that represented in vector flow maps. The reduce problem on detection of emotion state, vector flow maps are transformed into compass mapping that represents major directions and velocities of facial movement. The results showed that the power of beta wave is increasing when disorder sound stimulation was given, however for each volunteer was giving different emotion feedback. Based on features derived from facial face images, an optical flow compass mapping was promising to use as additional information to make decision about emotion feedback.

Keywords: Multimodal Emotion Detection, EEG, Facial Image, Optical Flow, compass mapping, Brain Wave

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on hyperspectral image (HSI) dataset on Indian Pines. The results confirm the capability of the proposed method.

Keywords: Continual learning, data reconstruction, remote sensing, hyperspectral image segmentation.

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Authors: Kristina Davoian, Wolfram-M. Lippe

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Evolutionary Programming (EP) represents a methodology of Evolutionary Algorithms (EA) in which mutation is considered as a main reproduction operator. This paper presents a novel EP approach for Artificial Neural Networks (ANN) learning. The proposed strategy consists of two components: the self-adaptive, which contains phenotype information and the dynamic, which is described by genotype. Self-adaptation is achieved by the addition of a value, called the network weight, which depends on a total number of hidden layers and an average number of neurons in hidden layers. The dynamic component changes its value depending on the fitness of a chromosome, exposed to mutation. Thus, the mutation step size is controlled by two components, encapsulated in the algorithm, which adjust it according to the characteristics of a predefined ANN architecture and the fitness of a particular chromosome. The comparative analysis of the proposed approach and the classical EP (Gaussian mutation) showed, that that the significant acceleration of the evolution process is achieved by using both phenotype and genotype information in the mutation strategy.

Keywords: Artificial Neural Networks (ANN), Learning Theory, Evolutionary Programming (EP), Mutation, Self-Adaptation.

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Authors: A. Raceviciute-Stupeliene, V. Sasyte, V. Viliene, V. Slausgalvis, J. Al-Saifi, R. Gruzauskas

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The objective of this study was to investigate the influence of special additives such as chelators, zinc sulphate and silicic acid on productivity parameters, carcass characteristics and meat quality of fattening pigs. The test started with 40 days old fattening pigs (mongrel (mother) and Yorkshire (father)) and lasted up to 156 days of age. During the fattening period, 32 pigs were divided into 2 groups (control and experimental) with 4 replicates (total of 8 pens).  The pigs were fed for 16 weeks’ ad libitum with a standard wheat-barley-soybean meal compound (Control group) supplemented with chelators, zinc sulphate and silicic acid (dosage 2 kg/t of feed, Experimental group). Meat traits in live pigs were measured by ultrasonic equipment Piglog 105. The results obtained throughout the experimental period suggest that supplementation of chelators, zinc sulphate and silicic acid tend to positively affect average daily gain and feed conversion ratio of pigs for fattening (p < 0.05). Pigs’ evaluation with Piglog 105 showed that thickness of fat in the first and second point was by 4% and 3% respectively higher in comparison to the control group (p < 0.05). Carcass weight, yield, and length, also thickness of fat showed no significant difference among the groups. The water holding capacity of meat in Experimental group was lower by 5.28%, and tenderness – lower by 12% compared with that of the pigs in the Control group (p < 0.05). Regarding pigs’ meat chemical composition of the experimental group, a statistically significant difference comparing with the data of the control group was not determined. Cholesterol concentration in muscles of pigs fed diets supplemented with chelators, zinc sulphate and silicic acid was lower by 7.93 mg/100 g of muscle in comparison to that of the control group. These results suggest that supplementation of chelators, zinc sulphate and silicic acid in the feed for fattening pigs had significant effect on pigs growing performance and meat quality.

Keywords: Chelators, meat quality, pigs, silicic acid, zinc sulphate.

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Authors: Mayank Kumar, D B Mahesh Kumar, Ashwin S Kumar, N K Srinath

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In this era of technology, fueled by the pervasive usage of the internet, security is a prime concern. The number of new attacks by the so-called “bots", which are automated programs, is increasing at an alarming rate. They are most likely to attack online registration systems. Technology, called “CAPTCHA" (Completely Automated Public Turing test to tell Computers and Humans Apart) do exist, which can differentiate between automated programs and humans and prevent replay attacks. Traditionally CAPTCHA-s have been implemented with the challenge involved in recognizing textual images and reproducing the same. We propose an approach where the visual challenge has to be read out from which randomly selected keywords are used to verify the correctness of spoken text and in turn detect the presence of human. This is supplemented with a speaker recognition system which can identify the speaker also. Thus, this framework fulfills both the objectives – it can determine whether the user is a human or not and if it is a human, it can verify its identity.

Keywords: CAPTCHA, automatic speech recognition, keyword spotting.

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Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

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As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: Apartment housing, machine learning, multi-objective optimization, performance prediction.

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Authors: Xiaohui Zhao, Shahan Mafuz

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Context-aware technologies provide system applications with the awareness of environmental conditions, customer behaviours, object movements, etc. Further, with such capability system applications can be smart to intelligently adapt their responses to the changing conditions. In regard to business operations, this promises businesses that their business processes can run more intelligently, adaptively and flexibly, and thereby either improve customer experience, enhance reliability of service delivery, or lower operational cost, to make the business more competitive and sustainable. Aiming at realising such context-aware business process management, this paper firstly explores its potential benefit, and then identifies some gaps between the current business process management support and the expected. In addition, some preliminary solutions are also discussed in regard to context definition, rule-based process execution, run-time process evolution, etc. A framework is also presented to give a conceptual architecture of context-aware business process management system to guide system implementation.

Keywords: Business process adaptation, business process evolution, business process modelling, and context awareness.

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Authors: M. Sokhanvar, S. M. Shahidipour

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One of the essential requirements for the human beings is the house for living. This is necessary to make the place of satisfaction for contemporary houses residents by attention to their culture. In this article represented the relevant theoretical literature on cultural symbols by use the architecture semiotic to construct the houses as a better place for living. In fact, make a place for everyday life with changing the house to the home is one of the most challengeable subject for architects all around the world. The target of this article is to find Cypriot houses cultural symbols that assist architect to design and build contemporary houses, to make more satisfaction for its residents according to Cypriot life style and their culture. This paper is based on researching the effect of cultural symbols on housing, would require various types of methods. However, this study focuses on two methods, which are quantitative and qualitative. The purpose of the case-specific study is to finding the symbols that used in contemporary houses by attention to the Cypriot cultural symbols in Famagusta houses.

Keywords: Houses, home, architectural symbols, cultural symbols.

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Authors: Gülfem I. Alptekin, S. Emre Alptekin

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The next generation wireless systems, especially the cognitive radio networks aim at utilizing network resources more efficiently. They share a wide range of available spectrum in an opportunistic manner. In this paper, we propose a quality management model for short-term sub-lease of unutilized spectrum bands to different service providers. We built our model on competitive secondary market architecture. To establish the necessary conditions for convergent behavior, we utilize techniques from game theory. Our proposed model is based on potential game approach that is suitable for systems with dynamic decision making. The Nash equilibrium point tells the spectrum holders the ideal price values where profit is maximized at the highest level of customer satisfaction. Our numerical results show that the price decisions of the network providers depend on the price and QoS of their own bands as well as the prices and QoS levels of their opponents- bands.

Keywords: cognitive radio networks, game theory, nextgeneration wireless networks, spectrum management.

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Authors: Farrukh Shahzad

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The rich Islamic resources related to religious text, Islamic sciences, and history are widely available in print and in electronic format online. However, most of these works are only available in Arabic language. In this research, an attempt is made to utilize these resources to create interactive web applications in Arabic, English and other languages. The system utilizes the Pattern Recognition, Knowledge Management, Data Mining, Information Retrieval and Management, Indexing, storage and data-analysis techniques to parse, store, convert and manage the information from authentic Arabic resources. These interactive web Apps provide smart multi-lingual search, tree based search, on-demand information matching and linking. In this paper, we provide details of application architecture, design, implementation and technologies employed. We also presented the summary of web applications already developed. We have also included some screen shots from the corresponding web sites. These web applications provide an Innovative On-line Learning Systems (eLearning and computer based education).

Keywords: Islamic resources, Muslim scholars, hadith, narrators, history, fiqh.

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Authors: Brenden M. Shutt, Lubjana Beshaj, Paul L. Goethals, Ambrose Kam

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Communication security is of particular interest to military data networks. A relatively novel approach to network security is blockchain, a cryptographically secured distribution ledger with a decentralized consensus mechanism for data transaction processing. Recent advances in blockchain technology have proposed new techniques for both data validation and trust management, as well as different frameworks for managing dataflow. The purpose of this work is to test the feasibility of different blockchain architectures as applied to military command and control networks. Various architectures are tested through discrete-event simulation and the feasibility is determined based upon a blockchain design’s ability to maintain long-term stable performance at industry standards of throughput, network latency, and security. This work proposes a consortium blockchain architecture with a computationally inexpensive consensus mechanism, one that leverages a Proof-of-Identity (PoI) concept and a reputation management mechanism.

Keywords: Blockchain, command & control network, discrete-event simulation, reputation management.

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Authors: Taechit Cheuypoung

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The research will focus on creating pattern designs that are inspired by the pagodas, Phra Maha Chedi of King Rama I-IV, that are located in the temple, Wat Phra Chetuphon Vimolmangklararm Rajwaramahaviharn. Different aspects of the temple were studied, including the history, architecture, significance of the temple, and techniques used to decorate the pagodas, Phra Maha Chedi of King Rama I-IV. Moreover, composition of arts and the form of pattern designs which all led to the outcome of four Thai application pattern.

The four patterns combine Thai traditional design with international scheme, however, maintaining the distinctiveness of the glaze mosaic tiles of each Phra Maha Chedi. The patterns consist of rounded and notched petal flowers, leaves and vine, and various square shapes, and original colors which are updated for modernity. These elements are then grouped and combined with new techniques, resulting in pattern designs with modern aspects and simultaneously reflecting the charm and the aesthetic of Thai craftsmanship which are eternally embedded in the designs.

Keywords: Chedi, Pagoda, Pattern, Wat

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Authors: Ahmed K. Hasan, A. A. Zaidan, Anas Majeed, B. B. Zaidan, Rosli Salleh, Omar Zakaria, Ali Zuheir

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Wireless mesh networks based on IEEE 802.11 technology are a scalable and efficient solution for next generation wireless networking to provide wide-area wideband internet access to a significant number of users. The deployment of these wireless mesh networks may be within different authorities and without any planning, they are potentially overlapped partially or completely in the same service area. The aim of the proposed model is design a new model to Enhancement Throughput of Unplanned Wireless Mesh Networks Deployment Using Partitioning Hierarchical Cluster (PHC), the unplanned deployment of WMNs are determinates there performance. We use throughput optimization approach to model the unplanned WMNs deployment problem based on partitioning hierarchical cluster (PHC) based architecture, in this paper the researcher used bridge node by allowing interworking traffic between these WMNs as solution for performance degradation.

Keywords: Wireless Mesh Networks, 802.11s Internetworking, partitioning Hierarchical Cluste.

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Authors: Elvan Ender, Murat Zencirkıran

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One of the most important criteria that increase the success of design in landscape architecture is the visual effect. The characteristics that affect visual appearance in plant design vary depending on the phenological periods of the plants. In plants, although different effects are observed in different periods of the year, this effect is felt most prominently in flowering periods. For this reason, knowing the flowering time, duration and flower characteristics should be considered as a factor increasing the success of plant design. In this study, flower characteristics of natural woody plants with attractive flowers have been examined. Because of the variability of these characteristics of plants in the region, consideration of these criteria in the planting design processes in the region may increase the success of the design. At the same time, when species selection is made considering the obtained data, visuality and sustainability of natural species can be possible in Bursa city with planting design.

Keywords: Bursa, flower characteristics, natural plants, planting design.

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Authors: Hanaa M. M. El-Khayat, Hoda Abdel-Hamid, Kadria M. A. Mahmoud, Hanan S. Gaber, Hoda, M. A. Abu Taleb, Hassan E. Flefel

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Snails are considered as suitable diagnostic organisms for heavy metal–contaminated sites. Biomphalaria alexandrina snails are used in this work as pollution bioindicators after exposure to chemical mixtures consisted of heavy metals (HM); zinc (Zn), copper (Cu) and lead (Pb); and persistent organic pollutants; Decabromodiphenyl ether 98% (D) and Aroclor 1254 (A). The impacts of these tested chemicals, individual and mixtures, on liver and kidney functions, antioxidant enzymes, complete blood picture, and tissue histology were studied. Results showed that Cu was proved to be the highly toxic against snails than Zn and Pb where LC₅₀ values were 1.362, 213.198 and 277.396 ppm, respectively. Also, B. alexandrina snails exposed to the mixture of HM (¼ LC₅ Cu, Pb and Zn) showed the highest bioaccumulation of Cu and Zn in their whole tissue, the most significant increase in AST, ALT & ALP activities and the highest significant levels of total protein, albumin and globulin. Results showed significant alterations in CAT activity in snail tissue extracts while snail samples exposed to most experimental tests showed significant increase in GST activity. Snail samples that exposed to HM mixtures showed a significant decrease in total hemocytes count while snail samples that exposed to mixtures containing A & D showed a significant increase in total hemocytes and Hyalinocytes. Histopathological alterations in snail samples exposed to individual HM and their mixtures for 4 weeks showed degeneration, edema, hyper trophy and vaculation in head-foot muscle, degeneration and necrotic changes in the digestive gland and accumulation in most tested organs. Also, the hermaphrodite gland showed mature ova with irregular shape and reduction in sperm number. In conclusion, the resulted damage and alterations in B. alexandrina studied parameters can be used as bioindicators to the presence of pollutants in its habitats.

Keywords: Biomphalaria, Zn, Cu, Pb, AST, ALT, ALP, total protein albumin, globulin, CAT and Histopathology.

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Authors: Bruno Vilić Belina, Ivan Župan

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Smart industry concepts and digital transformation are very popular in many industries. They develop their own digital platforms, which have an important role in innovations and transactions. The main idea of smart industry digital platforms is central data collection, industrial data integration and data usage for smart applications and services. This paper presents the development of a digital platform for building critical infrastructure protection systems in smart industries. Different service contraction modalities in Service Level Agreements (SLAs), Customer Relationship Management (CRM) relations, trends and changes in business architectures (especially process business architecture) for the purpose of developing infrastructural production and distribution networks, information infrastructure meta-models and generic processes by critical infrastructure owner demanded by critical infrastructure law, satisfying cybersecurity requirements and taking into account hybrid threats are researched.

Keywords: Cybersecurity, critical infrastructure, smart industries, digital platform.

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Authors: Y. Jayanta Singh, Suresh C. Mehrotra

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A real time distributed computing has heterogeneously networked computers to solve a single problem. So coordination of activities among computers is a complex task and deadlines make more complex. The performances depend on many factors such as traffic workloads, database system architecture, underlying processors, disks speeds, etc. Simulation study have been performed to analyze the performance under different transaction scheduling: different workloads, arrival rate, priority policies, altering slack factors and Preemptive Policy. The performance metric of the experiments is missed percent that is the percentage of transaction that the system is unable to complete. The throughput of the system is depends on the arrival rate of transaction. The performance can be enhanced with altering the slack factor value. Working on slack value for the transaction can helps to avoid some of transactions from killing or aborts. Under the Preemptive Policy, many extra executions of new transactions can be carried out.

Keywords: Real distributed systems, slack factors, transaction scheduling, priority policies.

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Authors: Nour Charara, Iman Jarkass, Maria Sokhn, Elena Mugellini, Omar Abou Khaled

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Intelligent Video-Surveillance (IVS) systems are being more and more popular in security applications. The analysis and recognition of abnormal behaviours in a video sequence has gradually drawn the attention in the field of IVS, since it allows filtering out a large number of useless information, which guarantees the high efficiency in the security protection, and save a lot of human and material resources. We present in this paper ADABeV, an intelligent video-surveillance framework for event recognition in crowded scene to detect the abnormal human behaviour. This framework is attended to be able to achieve real-time alarming, reducing the lags in traditional monitoring systems. This architecture proposal addresses four main challenges: behaviour understanding in crowded scenes, hard lighting conditions, multiple input kinds of sensors and contextual-based adaptability to recognize the active context of the scene.

Keywords: Behavior recognition, Crowded scene, Data fusion, Pattern recognition, Video-surveillance

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Authors: Parisa Göker

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With the birth of Islam, the definitions of paradise in Quran have spread across three continents since 7^th century, showing itself in the palace gardens as a reflection of Islamic Culture. The design characteristics of Islamic gardens come forth with the influence of religious beliefs, as well as taking its form as per the cultural, climatic and soil characteristics of its geography, and showing its difference. It is possible to see these differences from the garden examples that survived to present time from the civilizations in the lands of Islamic proliferation. The main material of this research is the Islamic gardens in Iran and Spain. Field study was carried out in Alhambra Palace in Spain, Granada and Shah Goli garden in Iran, Tabriz. In this study, the birth of Islamic gardens, spatial perception of paradise, design principles, spatial structure, along with the structural/plantation materials used are examined. Also the characteristics and differentiation of the gardens examined in different cultures and geographies have been revealed. In the conclusion section, Iran and Spain Islamic garden samples were evaluated and their properties were determined.

Keywords: Islamic civilization, Islamic architecture, cultural landscape, Islamic garden.

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Authors: Sulaiman Ainin, Yusniza Kamar ulzaman, Abdul Ghani Farinda

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This study was initiated with a three prong objective. One, to identify the relationship between Technological Competencies factors (Technical Capability, Firm Innovativeness and E-Business Practices and professional service firms- business performance. To investigate the predictors of professional service firms business performance and finally to evaluate the predictors of business performance according to the type of professional service firms, a survey questionnaire was deployed to collect empirical data. The questionnaire was distributed to the owners of the professional small medium size enterprises services in the Accounting, Legal, Engineering and Architecture sectors. Analysis showed that all three Technology Competency factors have moderate effect on business performance. In addition, the regression models indicate that technical capability is the most highly influential that could determine business performance, followed by e-business practices and firm innovativeness. Subsequently, the main predictor of business performance for all types of firms is Technical capability.

Keywords: technology competency, technology capability, innovativeness, E-business practice

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Authors: Nophaket Napong

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Rooted in the study of social functioning of space in architecture, Space Syntax (SS) and the more recent Network Pattern (NP) researches demonstrate the 'spatial structures' of city, i.e. the hierarchical patterns of streets, junctions and alley ends. Applying SS and NP models, planners can conceptualize the real city-s patterns. Although, both models yield the optimal path of the city their underpinning displays of the city-s spatial configuration differ. The Axial Map analyzes the topological non-distance-based connectivity structure, whereas, the Central-Node Map and the Shortcut-Path Map, in contrast, analyze the metrical distance-based structures. This research contrasts and combines them to understand various forms of city-s structures. It concludes that, while they reveal different spatial structures, Space Syntax and Network Pattern urban models support each the other. Combining together they simulate the global access and the locally compact structures namely the central nodes and the shortcuts for the city.

Keywords: Street pattern, space syntax, syntactic and metrical models, network pattern models.

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Authors: Weifeng Pan

Abstract:

The stability of a software system is one of the most important quality attributes affecting the maintenance effort. Many techniques have been proposed to support the analysis of software stability at the architecture, file, and class level of software systems, but little effort has been made for that at the feature (i.e., method and attribute) level. And the assumptions the existing techniques based on always do not meet the practice to a certain degree. Considering that, in this paper, we present a novel metric, Stability of Software (SoS), to measure the stability of object-oriented software systems by software change propagation analysis using a simulation way in software dependency networks at feature level. The approach is evaluated by case studies on eight open source Java programs using different software structures (one employs design patterns versus one does not) for the same object-oriented program. The results of the case studies validate the effectiveness of the proposed metric. The approach has been fully automated by a tool written in Java.

Keywords: Software stability, change propagation, design pattern, software maintenance, object-oriented (OO) software.

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Authors: V. Holeva, V. Aliki Nikopoulou, P. Kechayas, M. Dialechti Kerasidou, M. Papadopoulou, G. A. Papakostas, V. G. Kaburlasos, A. Evangeliou

Abstract:

Cognitive Behavioral Therapy (CBT) has been proven an effective tool to address anger and anxiety issues in children and adolescents with Autism Spectrum Disorders (ASD). Robot-enhanced therapy has been used in psychosocial and educational interventions for children with ASD with promising results. Whenever CBT-based techniques were incorporated in robot-based interventions, they were mainly performed in group sessions. Objectives: The study’s main objective was the implementation and evaluation of the effectiveness of a relaxation training intervention for children with ASD, delivered by the social robot NAO. Methods: 20 children (aged 7–12 years) were randomly assigned to 16 sessions of relaxation training implemented twice a week. Two groups were formed: the NAO group (children participated in individual sessions with the support of NAO) and the control group (children participated in individual sessions with the support of the therapist only). Participants received three different relaxation scenarios of increasing difficulty (a breathing scenario, a progressive muscle relaxation scenario and a body scan medication scenario), as well as related homework sheets for practicing. Pre- and post-intervention assessments were conducted using the Child Behavior Checklist (CBCL) and the Strengths and Difficulties Questionnaire for parents (SDQ-P). Participants were also asked to complete an open-ended questionnaire to evaluate the effectiveness of the training. Parents’ satisfaction was evaluated via a questionnaire and children satisfaction was assessed by a thermometer scale. Results: The study supports the use of relaxation training with the NAO robot as instructor for children with ASD. Parents of enrolled children reported high levels of satisfaction and provided positive ratings of the training acceptability. Children in the NAO group presented greater motivation to complete homework and adopt the learned techniques at home. Conclusions: Relaxation training could be effectively integrated in robot-assisted protocols to help children with ASD regulate emotions and develop self-control.

Keywords: Autism spectrum disorders, CBT, children relaxation training, robot-assisted therapy.

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