Search results for: collaborative service networks

Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

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Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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Authors: Sisi Xia, Dezhuan Tao, Junyan Yang, Weiting Xiong

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Under the background of Chinese urbanization changing from incremental development to stock development, the completion of urban public service facilities is essential to urban spatial quality. As public services facilities is a huge and complicated system, clarifying the various types of internal rules associated with the land market price is key to optimizing spatial layout. This paper takes Wuxi City as a representative sample location and establishes the digital analysis platform using urban price and several high-precision big data acquisition methods. On this basis, it analyzes the coupling relationship between different public service categories and land price, summarizing the coupling patterns of urban public facilities distribution and urban land price fluctuations. Finally, the internal mechanism within each of the two elements is explored, providing the reference of the optimum layout of urban planning and public service facilities.

Keywords: public service facilities, land price, urban spatial morphology, big data

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Authors: Mandisi Matyana

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Although the South African local government is doing all the best in ensuring improved service delivery for the citizens, service delivery disparity still remains the real challenge for other municipalities. The unequal distribution of services within municipal wards is causing unequal happiness among the citizens; hence others do enjoy different provided municipal services, while others do not. It is acknowledged that less access to municipal services infringes one’s rights, such as the right to human dignity and the right to life. Some of the municipal services are basic services and they are the mainstay of human survival, such as water, housing, etc. It is quite evident that the service delivery disparity could be caused by the various factors within the local municipality affairs, including both administrative and political factors. Therefore, this study is undertaken to check and evaluate the main foundations of service delivery disparity in ensuring equal development of the state, particularly for local communities. The study used the qualitative method to collect the data from the citizens of Winnie Madikizela Mandela Local Municipality. An extensive literature was also conducted in understanding the causes of service delivery disparity. Study findings prove that the service delivery disparity could be caused by factors such as political interference in administration, corruption and fraud, elevated unemployment levels, inadequate institutional capacity, etc. Therefore, the study recommends strong community participation and constant external supervision in the local government so as to encourage openness in local government to ensure fair administration towards services to be provided.

Keywords: administration, development, municipal services, service delivery disparity, Winnie Madikizela Mandela local municipality

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Authors: Amir Rashid

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Today is the world of innovation and Cloud Computing is becoming a day to day technology with every passing day offering remarkable services and features on the go with rapid elasticity. This platform took business computing into an innovative dimension where clients interact and operate through service provider web portals. Initially, the trust relationship between client and service provider remained a big question but with the invention of several cryptographic paradigms, it is becoming common in everyday business. This research work proposes a solution for building a cloud storage service with respect to Data Security addressing public cloud infrastructure where the trust relationship matters a lot between client and service provider. For the great satisfaction of client regarding high-end Data Security, this research paper propose a layer of cryptographic primitives combining several architectures in order to achieve the goal. A survey has been conducted to determine the benefits for such an architecture would provide to both clients/service providers and recent developments in cryptography specifically by cloud storage.

Keywords: data security in cloud computing, cloud storage architecture, cryptographic developments, token key

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Authors: Mornay Roberts-Lombard

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Consumers’ perceptions of value are crucial to ensuring their future commitment and behavioral intentions. As a result, service providers, such as Islamic banks, must provide their customers with products and services that are regarded as valuable, stimulating, collaborative, and competent. Therefore, the value provided to customers must meet or surpass their expectations, which can drive customers’ commitment (affective and calculative) and eventually favorably impact their future behavioral intentions. Consequently, Islamic banks in South Africa, as a growing African market, need to obtain a better understanding of the variables that impact Islamic banking customers’ value perceptions and how these impact their future behavioral intentions. Furthermore, it is necessary to investigate how customers’ perceived value perceptions impact their affective and calculative commitment and how the latter impact their future behavioral intentions. The purpose of this study is to bridge these gaps in knowledge, as the competitiveness of the Islamic banking industry in South Africa requires a deeper understanding of the aforementioned relationships. The study was exploratory and quantitative in nature, and data was collected from 250 Islamic banking customers using self-administered questionnaires. These banking customers resided in the Gauteng province of South Africa. Exploratory factor analysis, Pearson’s coefficient analysis, and multiple regression analysis were applied to measure the proposed hypotheses developed for the study. This research will aid Islamic banks in the country in potentially strengthening customers’ future commitment (affective and calculative) and positively impact their future behavioral intentions. The findings of the study established that service quality has a significant and positive impact on perceived value. Moreover, it was determined that perceived value has a favorable and considerable impact on affective and calculative commitment, while calculative commitment has a beneficial impact on behavioral intention. The research informs Islamic banks of the importance of service engagement in driving customer perceived value, which stimulates the future affective and calculative commitment of Islamic bank customers in an emerging market context. Finally, the study proposes guidelines for Islamic banks to develop an enhanced understanding of the factors that impact the perceived value-commitment-behavioral intention link in a competitive Islamic banking market in South Africa.

Keywords: perceived value, affective commitment, calculative commitment, behavioural intention

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Authors: Mohanad Alhabo, Naveed Nawaz, Mahmoud Al-Faris

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The dense deployment of small cells is a promising solution to enhance the coverage and capacity of the heterogeneous networks (HetNets). However, the unplanned deployment could bring new challenges to the network ranging from interference, unnecessary handovers and handover failures. This will cause a degradation in the quality of service (QoS) delivered to the end user. In this paper, we propose an integrated Grey Rational Analysis Standard Deviation based handover method (GRA-SD) for HetNet. The proposed method integrates the Standard Deviation (SD) technique to acquire the weight of the handover metrics and the GRA method to select the best handover base station. The performance of the GRA-SD method is evaluated and compared with the traditional Multiple Attribute Decision Making (MADM) methods including Simple Additive Weighting (SAW) and VIKOR methods. Results reveal that the proposed method has outperformed the other methods in terms of minimizing the number of frequent unnecessary handovers and handover failures, in addition to improving the energy efficiency.

Keywords: energy efficiency, handover, HetNets, MADM, small cells

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Authors: Flevy Lasrado

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Purpose: The purpose of this paper is to show the relationship between the service quality dimensions and model them to propose quality improvements using interpretive structural modelling (ISM). Methodology: This paper used an interpretive structural modelling (ISM). The data was collected from the expert opinions that included a questionnaire. The detailed method of using ISM is discussed in the paper. Findings: The present research work provides an ISM based model to understand the relationships among the service quality dimensions. Practical implications or Original Value: An ISM based model has been developed for healthcare facility for improving customer satisfaction and increasing market share. Although there is lot of research on SERVQUAL model adapted to healthcare sector, no study has been done to understand the interactions among these dimensions. So the major contribution of this research work is the development of contextual relationships among identified variables through a systematic framework. The present research work provides an ISM based model to understand the relationships among the service quality dimensions.

Keywords: SERQUAL, healthcare, quality, service quality

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Authors: Yoonjung An, Yongtae Park

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Offering a Product-Service System (PSS) is a well-accepted strategy that companies may adopt to provide a set of systemic solutions to customers. PSSs were initially provided in a simple form but now take diversified and complex forms involving multiple services, products and technologies. With the growing interest in the PSS, frameworks for the PSS development have been introduced by many researchers. However, most of the existing frameworks fail to examine various relations existing in a complex PSS. Since designing a complex PSS involves full integration of multiple products and services, it is essential to identify not only product-service relations but also product-product/ service-service relations. It is also equally important to specify how they are related for better understanding of the system. Moreover, as customers tend to view their purchase from a more holistic perspective, a PSS should be developed based on the whole system’s requirements, rather than focusing only on the product requirements or service requirements. Thus, we propose a framework to develop a complex PSS that is coordinated fully with the requirements of both worlds. Specifically, our approach adopts a multi-domain matrix (MDM). A MDM identifies not only inter-domain relations but also intra-domain relations so that it helps to design a PSS that includes highly desired and closely related core functions/ features. Also, various dependency types and rating schemes proposed in our approach would help the integration process.

Keywords: inter-domain relations, intra-domain relations, multi-domain matrix, product-service system design

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Authors: Faisal Al Yahmadi, Muhammad R. Ahmed

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Electric power is a fundamental necessity in the 21^st century. Consequently, any break in electric power is probably going to affect the general activity. To make the power supply smooth and efficient, a smart grid network is introduced which uses communication technology. In any communication network, security is essential. It has been observed from several recent incidents that adversary causes an interruption to the operation of networks. In order to resolve the issues, it is vital to understand the threats and vulnerabilities associated with the smart grid networks. In this paper, we have investigated the threats and vulnerabilities in Smart Grid Networks (SGN) and the few solutions in the literature. Proposed solutions showed developments in electricity theft countermeasures, Denial of services attacks (DoS) and malicious injection attacks detection model, as well as malicious nodes detection using watchdog like techniques and other solutions.

Keywords: smart grid network, security, threats, vulnerabilities

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Authors: L. Atif, C. Rosenthal-Sabroux, M. Grundstein

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The requirements engineering process is a crucial phase in the design of complex systems. The purpose of our research is to present a collaborative problem-driven requirements engineering approach that aims at improving the design of a Decision Support System as an Analytics application. This approach has been adopted to design a Human Resource management DSS. The Requirements Engineering process is presented as a series of guidelines for activities that must be implemented to assure that the final product satisfies end-users requirements and takes into account the limitations identified. For this, we know that a well-posed statement of the problem is “a problem whose crucial character arises from collectively produced estimation and a formulation found to be acceptable by all the parties”. Moreover, we know that DSSs were developed to help decision-makers solve their unstructured problems. So, we thus base our research off of the assumption that developing DSS, particularly for helping poorly structured or unstructured decisions, cannot be done without considering end-user decision problems, how to represent them collectively, decisions content, their meaning, and the decision-making process; thus, arise the field issues in a multidisciplinary perspective. Our approach addresses a problem-driven and collaborative approach to designing DSS technologies: It will reflect common end-user problems in the upstream design phase and in the downstream phase these problems will determine the design choices and potential technical solution. We will thus rely on a categorization of HR’s problems for a development mirroring the Analytics solution. This brings out a new data-driven DSS typology: Descriptive Analytics, Explicative or Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics. In our research, identifying the problem takes place with design of the solution, so, we would have to resort a significant transformations of representations associated with the HR Analytics application to build an increasingly detailed representation of the goal to be achieved. Here, the collective cognition is reflected in the establishment of transfer functions of representations during the whole of the design process.

Keywords: DSS, collaborative design, problem-driven requirements, analytics application, HR decision making

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Authors: Aradhna Pandarum

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South Africa is experiencing an exponential growth of distributed solar PV installations. This is due to various factors with the predominant one being increasing electricity tariffs along with decreasing installation costs, resulting in attractive business cases to some end-users. Despite there being a variety of economic and environmental advantages associated with the installation of PV, their potential impact on distribution grids has yet to be thoroughly investigated. This is especially true since the locations of these units cannot be controlled by Network Service Providers (NSPs) and their output power is stochastic and non-dispatchable. This report details two case studies that were completed to determine the possible voltage and technical losses impact of increasing PV penetration in the Northern Cape of South Africa. Some major impacts considered for the simulations were ramping of PV generation due to intermittency caused by moving clouds, the size and overall hosting capacity and the location of the systems. The main finding is that the technical impact is different on a constrained feeder vs a non-constrained feeder. The acceptable PV penetration level is much lower for a constrained feeder than a non-constrained feeder, depending on where the systems are located.

Keywords: medium voltage networks, power system losses, power system voltage, solar photovoltaic

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Authors: Ardavan Zamanpour, Somaieh Yassari

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ZigBee protocol network was developed in industries and MIT laboratory in 1997. ZigBee is a wireless networking technology by alliance ZigBee which is designed to low board and low data rate applications. It is a Protocol which connects between electrical devises with very low energy and cost. The first version of IEEE 802.15.4 which was formed ZigBee was based on 2.4GHZ MHZ 912MHZ 868 frequency band. The name of system is often reminded random directions that bees (BEES) traversing during pollination of products. Such as alloy of the ways in which information packets are traversed within the mesh network. This paper aims to study the performance and effectiveness of this protocol in wireless networks.

Keywords: ZigBee, protocol, wireless, networks

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Authors: Jiao Sun, Li Pan, Shijun Liu

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Recommender systems have been widely used in contemporary industry, and plenty of work has been done in this field to help users to identify items of interest. Collaborative Filtering (CF, for short) algorithm is an important technology in recommender systems. However, less work has been done in automobile recommendation system with the sharp increase of the amount of automobiles. What’s more, the computational speed is a major weakness for collaborative filtering technology. Therefore, using MapReduce framework to optimize the CF algorithm is a vital solution to this performance problem. In this paper, we present a recommendation of the users’ comment on industrial automobiles with various properties based on real world industrial datasets of user-automobile comment data collection, and provide recommendation for automobile providers and help them predict users’ comment on automobiles with new-coming property. Firstly, we solve the sparseness of matrix using previous construction of score matrix. Secondly, we solve the data normalization problem by removing dimensional effects from the raw data of automobiles, where different dimensions of automobile properties bring great error to the calculation of CF. Finally, we use the MapReduce framework to optimize the CF algorithm, and the computational speed has been improved times. UV decomposition used in this paper is an often used matrix factorization technology in CF algorithm, without calculating the interpolation weight of neighbors, which will be more convenient in industry.

Keywords: collaborative filtering, recommendation, data normalization, mapreduce

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Authors: Ying Su, Morgan C. Wang

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Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN).

Keywords: automated machines learning, autoregressive integrated moving average, neural networks, time series analysis

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

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Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

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In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps

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Authors: Xu Jie

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As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decisionmaking problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security). By modeling the multi-job collaborative cryptographic service scheduling problem as a multiobjective optimized job flow scheduling problem, and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing, and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: multi-agent reinforcement learning, non-stationary dynamics, multi-agent systems, cooperative and competitive agents

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Authors: Ali Isapour, Ramin Nateghi

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— Markov parameters are unique parameters of the system and remain unchanged under similarity transformations. Markov parameters from a power series that is convergent only if the system matrix’s eigenvalues are inside the unity circle. Therefore, Markov parameters of a stable discrete-time system are convergent. In this study, we aim to realize the system based on Markov parameters by using Artificial Neural Networks (ANN), and this end, we use Flexible Neural Networks. Realization means determining the elements of matrices A, B, C, and D.

Keywords: Markov parameters, realization, activation function, flexible neural network

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Authors: Azfar Adib

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Like many other countries of the world, mobile internet has been playing a key role in the growth of internet subscriber base in Bangladesh. This study has attempted to identify particular behavioral or usage patterns of 2G mobile internet subscribers who were using the service of the topmost internet service provider (as well as the top mobile operator) of Bangladesh prior to the launching of 3G services (when 2G was fully dominant). It contains some comprehensive analysis carried on different info regarding 2G mobile internet subscribers, obtained from the operator’s own network insights.This is accompanied by the results of a survey conducted among 40 high-frequency users of this service.

Keywords: mobile internet, Symbian, Android, iPhone

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Authors: Moheb R. Girgis, Tarek M. Mahmoud, Bahgat A. Abdullatif, Ahmed M. Rabie

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Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. We usually need multiple gateways, which takes time and costs a lot of money set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. In this paper, we concentrate on this challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

Keywords: wireless mesh networks, genetic algorithms, simulated annealing, topology design

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Authors: Minh Thi Hai Vo

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This article draws on in-depth interviews with state employees at public hospitals and universities in its institutional analysis of the autonomy practices of public service delivery units in Vietnam. Unlike many empirical and theoretical studies that view formal and informal institutions as complements or substitutes, this article finds no evidence of complementary or substitutive relationships. Instead, the article finds that formal institutions accommodate informal ones and that informal institutions tend to compete and interfere, with the existing and ineffective formal institutions. The result of such conflicting relationship is that the actual autonomy of public service delivery units is, in most cases, perceived to be greater than the formal autonomy they are given. In the condition of poor regulation, the informal autonomy may result in unethical practices including rent-seeking and corruption. The implication of the study finding is policy-makers need to redesign and reorganize the autonomisation of public service delivery units to make informal institutions support and reinforce formal ones in a complementary manner.

Keywords: autonomy, formal institutions, informal institutions, public service delivery units, Vietnam

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Authors: Omnia F. Elkommos

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In the course of teaching stylistics to undergraduate students of the Department of English Language and Literature, Faculty of Arts and Humanities, the linguistic tool kit of theories comes in handy and useful for the better understanding of the different literary genres: Poetry, drama, and short stories. In the present paper, a model of teaching of stylistics is compiled and suggested. It is a collaborative group project technique for use in the undergraduate diverse specialisms (Literature, Linguistics and Translation tracks) class. Students initially are introduced to the different linguistic tools and theories suitable for each literary genre. The second step is to apply these linguistic tools to texts. Students are required to watch videos performing the poems or play, for example, and search the net for interpretations of the texts by other authorities. They should be using a template (prepared by the researcher) that has guided questions leading students along in their analysis. Finally, a practical analysis would be written up using the practical analysis essay template (also prepared by the researcher). As per collaborative learning, all the steps include activities that are student-centered addressing differentiation and considering their three different specialisms. In the process of selecting the proper tools, the actual application and analysis discussion, students are given tasks that request their collaboration. They also work in small groups and the groups collaborate in seminars and group discussions. At the end of the course/module, students present their work also collaboratively and reflect and comment on their learning experience. The module/course uses a drama play that lends itself to the task: ‘The Bond’ by Amy Lowell and Robert Frost. The project results in an interpretation of its theme, characterization and plot. The linguistic tools are drawn from pragmatics, and discourse analysis among others.

Keywords: applied linguistic theories, collaborative learning, cooperative principle, discourse analysis, drama analysis, group project, online acting performance, pragmatics, speech act theory, stylistics, technology enhanced learning

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Zhifeng Kong

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Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.

Keywords: over-parameterization, rectified linear units ReLU, convergence, gradient descent, neural networks

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Authors: Frodouard Minani

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Since last decade, wireless sensor networks (WSNs) have been used in many areas like health care, agriculture, defense, military, disaster hit areas and so on. Wireless Sensor Networks consist of a Base Station (BS) and more number of wireless sensors in order to monitor temperature, pressure, motion in different environment conditions. The key parameter that plays a major role in designing a protocol for Wireless Sensor Networks is energy efficiency which is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. Maximizing sensor node’s lifetime is an important issue in the design of applications and protocols for Wireless Sensor Networks. Clustering sensor nodes mechanism is an effective topology control approach for helping to achieve the goal of this research. In this paper, the researcher presents an energy efficiency protocol to prolong the network lifetime based on Energy efficient clustering algorithm. The Low Energy Adaptive Clustering Hierarchy (LEACH) is a routing protocol for clusters which is used to lower the energy consumption and also to improve the lifetime of the Wireless Sensor Networks. Maximizing energy dissipation and network lifetime are important matters in the design of applications and protocols for wireless sensor networks. Proposed system is to maximize the lifetime of the Wireless Sensor Networks by choosing the farthest cluster head (CH) instead of the closest CH and forming the cluster by considering the following parameter metrics such as Node’s density, residual-energy and distance between clusters (inter-cluster distance). In this paper, comparisons between the proposed protocol and comparative protocols in different scenarios have been done and the simulation results showed that the proposed protocol performs well over other comparative protocols in various scenarios.

Keywords: base station, clustering algorithm, energy efficient, sensors, wireless sensor networks

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Authors: Sunico Armie Flores

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Early childhood education (ECE) is pivotal in shaping the future of individuals and society. This paper explores the collaborative efforts required among educators, families, and communities to create a collective impact on young children’s development. It delves into the importance of these partnerships, effective strategies for engagement, and the challenges and opportunities inherent in fostering such collaboration. By examining current research and practices, the paper aims to highlight the essential role of an integrated approach in achieving significant and sustainable improvements in early childhood outcomes.

Keywords: early childhood education, lifelong learning, cognitive development, socio-emotional development, educators, families, communities, collaborative efforts, collective impact, early learning environments, holistic development, high-quality ECE programs, investment in education

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Authors: Christiane Dumont

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This paper proposes to revisit foreign-language learning as a tool to increase motivation through advocacy and develop effective natural communication skills, which are critical leadership qualities. To this end, collaborative initiatives undertaken by advanced university students of French with local and international community partners will be reviewed. Close attention will be paid to the acquisition of intercultural skills, the reflective process, as well as the challenges and outcomes. Two international development projects conducted in Haiti will be highlighted, i.e., collaboration with a network of providers in the Haitian cultural heritage preservation and tourism sector (2014-15) and development of investigation and teacher training tools for a primary/secondary school in the Port-au-Prince area (current). The choice of community-service learning as a framework to teach French-as-a-second-language stemmed from the need to raise awareness against stereotypes and prejudice, which hinder the development of effective intercultural skills. This type of experiential education also proved very effective in identifying and preventing miscommunication caused by the lack of face-to-face interaction in our increasingly technology-mediated world. Learners experienced first-hand, the challenges and advantages of face-to-face communication, which, in turn, enhanced their motivation for developing effective intercultural skills. Vygotsky's and Kolb's theories, current research on service learning (Dwight, Eyler), action/project-based pedagogy (Beckett), and reflective learning (TSC Farrell), will provide useful background to analyze the benefits and challenges of community-service learning. The ultimate goal of this paper is to find out what makes experiential learning truly unique and transformative for both the learners and the community they wish to serve. It will demonstrate how enhanced motivation, community engagement, and clear, concise, and respectful communication impact and empower learners. The underlying hope is to help students in high-profile, and leading-edge industries become effective global leaders.

Keywords: experiential learning, intercultural communication, reflective learning, effective leadership, learner motivation

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Authors: Ramy Shaaban

Abstract:

The purpose of this study was to examine the difference in clinical reasoning skills of students when using virtual audiology cases with and without collaborative assistance from major learning approaches important to clinical reasoning skills and computer-based learning models: Situated Learning Theory, Social Development Theory, Scaffolding, and Collaborative Learning. A quasi-experimental design was conducted at two United States universities to examine whether there is a significant difference in clinical reasoning skills between three treatment groups using IUP Audiosim software. Two computer-based audiology case simulations were developed, and participants were randomly placed into the three groups: no collaboration, virtual collaboration, and live collaboration. The clinical reasoning data were analyzed using One-Way ANOVA and Tukey posthoc analyses. The results show that there was a significant difference in clinical reasoning skills between the three treatment groups. The score obtained by the no collaboration group was significantly less than the scores obtained by the virtual and live collaboration groups. Collaboration, whether virtual or in person, has a positive effect on students’ clinical reasoning. These results with audiology students indicate that combining collaboration models with scaffolding and embedding situated learning and social development theories into the design of future virtual patients has the potential to improve students’ clinical reasoning skills.

Keywords: clinical reasoning, virtual patients, collaborative learning, scaffolding

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Authors: Sathapath Kilaso

Abstract:

Nowadays, Today, wireless sensor networks are an important technology that works with Internet of Things. It is receiving various data from many sensor. Then sent to processing or storing. By wireless network or through the Internet. The devices around us are intelligent, can receiving/transmitting and processing data and communicating through the system. There are many applications of wireless sensor networks, such as smart city, smart farm, environmental management, weather. This article will explore the use of wireless sensor networks in Thailand and collect data from Thai Thesis database in 2012-2017. How to Implementing Wireless Sensor Network Technology. Advantage from this study To know the usage wireless technology in many fields. This will be beneficial for future research. In this study was found the most widely used wireless sensor network in agriculture field. Especially for smart farms. And the second is the adoption of the environment. Such as weather stations and water inspection.

Keywords: wireless sensor network, smart city, survey, Adhoc Network

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