Search results for: local interconnect network

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: Lee Artz

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Based on content analyses of major US media, immediately following the George Floyd killing in May 2020, some mayors and local, state, and national officials offered favorable representations of protests against police violence. As the protest movement grew to historic proportions with 26 million joining actions in large cities and small towns, dominant representations of racism by elected officials and leading media shifted—replacing both the voices and demands of protestors with representations by elected officials. Major media quoted Black mayors and Congressional representatives who emphasized concerns about looting and the disruption of public safety. Media coverage privileged elected officials who criticized movement demands for defunding police and deplored isolated instances of property damaged by protestors. Subsequently, public opinion polls saw an increase in concern for law and order tropes and a decrease in support for protests against police violence. Black Lives Matter and local organizations had no coordinated response and no effective means of communication to counter dominant representations voiced by politicians and globally disseminated by major media. Politician and media-instigated public opinion shifts indicate that social movements need their own means of communication and collective decision-making--both of which were largely missing from Black Lives Matter leaders, leading to disaffection and a political split by more than 20 local affiliates. By itself, social media by myriad individuals and groups had limited purchase as a means for social movement communication and organization. Lacking a collaborative, coordinated strategy, organization, and independent media, the loose network of Black Lives Matter groups was unable to offer more accurate, democratic, and favorable representations of protests and their demands for more justice and equality. The fight for equality was diverted by the fight for representation.

Keywords: black lives matter, public opinion, racism, representations, social movements

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Elabed Saud

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This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

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Authors: Jorge Fernandes Teixeira Filho, André Oliveira Alcantara Fontenele, Érick Aragão Ribeiro

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The constant emergence of new technologies used in automated processes makes it necessary for teachers and traders to apply new technologies in their classes. This paper presents an application of a new technology that will be employed in a didactic plant, which represents an effluent treatment process located in a laboratory of a federal educational institution. At work were studied in the first place, all components to be placed on automation laboratory in order to determine ways to program, parameterize and organize the plant. New technologies that have been implemented to the process are basically an AS-i network and a Profinet network, a SCADA system, which represented a major innovation in the laboratory. The project makes it possible to carry out in the laboratory various practices of industrial networks and SCADA systems.

Keywords: automation, industrial networks, SCADA systems, lab automation

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Authors: Mehdi Montakhabrazlighi, Ercan Balikci

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The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783.

Keywords: neural network, rupture strength, superalloy, thermocalc

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Authors: I. Iremashvili, I. Pirtskhalaishvili, K. Kiknadze, F. Lortkipanidze

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The paper deals with the causes of rockfall and its possible consequences on slopes adjacent to motorways and railways. A list of measures is given that hinder rockfall; these measures are directed at protecting roads from rockfalls, and not preventing them. From the standpoint of local stability of slopes the main effective measure is perhaps strengthening their surface by the method of filling, which will check or end (or both) the process of deformation, local slipping off, sliding off and development of erosion.

Keywords: rockfall, concrete spraying, heliodevices, railways

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Authors: Thiago M. R. Dias, Patrícia M. Dias, Gray F. Moita

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The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords.

Keywords: bibliometrics, data analysis, extraction and data integration, scientometrics

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Authors: Rasha F. Sharaf

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Administration of local anesthesia for a child is considered a painful procedure, which affects his vital signs, physiological parameters, and his further attitude in the dental clinic. During the extraction of mandibular molars, the nerve block technique is the most commonly used for the administration of local anesthesia; however, this technique requires deep penetration of the needle, which causes pain and discomfort for the child. Therefore, the inferior alveolar nerve block technique can be substituted with an infiltration technique which is not painful if a potent anesthetic solutions will be used. In the current study, the effect of Articaine 4% will be compared to Mepivacaine 2%, and their influence on the vital signs of the child, as well as their ability to control pain during extraction, will be assessed.

Keywords: anesthesia, articaine, pain control, extraction

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

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This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

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Authors: Latif Adnane, Benaatou Wafa, Pla Vicent

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Handover is of great significance to achieve seamless connectivity in wireless networks. This paper gives an impression of the main factors which are being affected by the soft and the hard handovers techniques. To know and understand the handover process in The Universal Mobile Telecommunications System (UMTS) network, different statistics are calculated. This paper focuses on the quality of service (QoS) of soft and hard handover in UMTS network, which includes the analysis of received power, signal to noise radio, throughput, delay traffic, traffic received, delay, total transmit load, end to end delay and upload response time using OPNET simulator.

Keywords: handover, UMTS, mobility, simulation, OPNET modeler

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Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway networks play a vital role in post-disaster recovery for disaster-damaged areas. Damaged bridges in such networks can disrupt the recovery activities by impeding the transportation of people, cargo, and reconstruction resources. Therefore, rapid restoration of damaged bridges is of paramount importance to long-term disaster recovery. In the post-disaster recovery phase, the key to restoration scheduling for a highway network is prioritization of bridge-repair tasks. Resilience is widely used as a measure of the ability to recover with which a network can return to its pre-disaster level of functionality. In practice, highways will be temporarily blocked during the downtime of bridge restoration, leading to the decrease of highway-network functionality. The failure to take downtime effects into account can lead to overestimation of network resilience. Additionally, post-disaster recovery of highway networks is generally divided into emergency bridge repair (EBR) in the response phase and long-term bridge repair (LBR) in the recovery phase, and both of EBR and LBR are different in terms of restoration objectives, restoration duration, budget, etc. Distinguish these two phases are important to precisely quantify highway network resilience and generate suitable restoration schedules for highway networks in the recovery phase. To address the above issues, this study proposes a novel resilience quantification method for the optimization of long-term bridge repair schedules (LBRS) taking into account the impact of EBR activities and restoration downtime on a highway network’s functionality. A time-dependent integer program with recursive functions is formulated for optimally scheduling LBR activities. Moreover, since uncertainty always exists in the LBRS problem, this paper extends the optimization model from the deterministic case to the stochastic case. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. The proposed methods are tested using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that, in this case, neglecting the bridge restoration downtime can lead to approximately 15% overestimation of highway network resilience. Moreover, accounting for the impact of EBR on network functionality can help to generate a more specific and reasonable LBRS. The theoretical and practical values are as follows. First, the proposed network recovery curve contributes to comprehensive quantification of highway network resilience by accounting for the impact of both restoration downtime and EBR activities on the recovery curves. Moreover, this study can improve the highway network resilience from the organizational dimension by providing bridge managers with optimal LBR strategies.

Keywords: disaster management, highway network, long-term bridge repair schedule, resilience, restoration downtime

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Ruth Yunji Nange

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Tourism has continued to increase as a significant industry, enhancing economic growth and development in Cameroon; due to the tremendous success of this industry, community participation (CP) has enhanced tourism development (TD). While gaining augmented attractiveness, considering how local CP is encouraged in such initiatives has become imperative. It has become essential to examine the importance of CP in the development of policies and planning processes for the equitable distribution of benefits and the effects of TD in the country. This study will exactly explore the cases of CP in the most populated cities in Cameroon (Douala and Yaoundé) and also understand how local CP is incorporated into tourism to enhance development in the tourism industry in particular and Cameroon in general. This paper is based on a qualitative research method, semi-structured interviews, and in-depth, face-to-face interviews carried out with the top administrators of tourism, both in the public and private sectors, such as the minister, provincial and regional delegates of tourism, non-governmental organizations (NGO), leaders of local community associations and tour operators. The forms and surveys were open-ended with a high level of flexibility. The findings of this study will pose implications for the development of CP in tourism initiatives programs in Cameroon and other developing economies.

Keywords: Cameroon, local community, participation and planning, tourism

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Authors: Simon Shibru, Karen Vancampenhout, Jozef Deckers, Herwig Leirs

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We investigated the population size of the endemic and endangered Swayne’s Hartebeest (Alcelaphus buselaphus swaynei) in Nech Sar National Park from 2012 to 2014 and document the major threats why the species is on the verge of local extinction. The park was once known for its abundant density of Swayne’s Hartebeest. We used direct total count methods for a census. We administered semi-structured interviews and open-ended questionnaires with senior scouts who are the member of the local communities. Historical records were obtained to evaluate the population trends of the animals since 1974. The density of the animal decreased from 65 in 1974 to 1 individual per 100 km2 in 2014 with a decline of 98.5% in the past 40 years. The respondents agreed that the conservation status of the park was in its worst condition ever now with only 2 Swayne’s Hartebeest left, with a rapid decline from 4 individuals in 2012 and 12 individuals in 2009. Mainly hunting and habitat loss, but also the unsuitable season of reproduction and shortage of forage as minor factors were identified as threats for a local extinction of the Swayne’s Hartebeests. On the other hand, predation, fire, disease, and ticks were not considered a cause for the declining trend. Hunting happens mostly out of some kind of revenge since the local community thought that they were pushed out from the land because of the presence of Swayne's Hartebeest in the area. Respondents agreed that the revenge action of the local communities was in response to their unwillingness to be displaced from the park in 1982/3. This conflict situation is resulting from the exclusionary wildlife management policy of the country. We conclude that the human interventions in general and illegal hunting, in particular, pushed the Swayne’s Hartebeest to a point of local extinction. Therefore, we recommend inclusive wildlife management approach for continuing existence of the park together with its natural resources so that sustainable use of the resources is in place.

Keywords: hunting, habitat destruction, local extinction, Nech Sar National Park, Swayne’s Hartebeest

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

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Authors: Pankhuri Dagaonkar, Charumani Singh, Poornima Krothapalli, Krishna Karthik

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The backbone of any retail e-commerce success story is a unique design of supply chain network, providing the business an unparalleled speed and scalability. Primary goal of the supply chain strategy is to meet customer expectation by offering fastest deliveries while keeping the cost minimal. Meeting this objective at the large market that India provides is the problem statement that we have targeted here. There are many models and optimization techniques focused on network design to identify the ideal facility location and size, optimizing cost and speed. In this paper we are presenting a tactical approach to optimize cost of an existing network for a predefined speed. We have considered both forward and reverse logistics of a retail e-commerce supply chain consisting of multiple fulfillment (warehouse) and delivery centers, which are connected via sortation nodes. The mathematical model presented here determines if the shipment from a node should get sorted directly for the last mile delivery center or it should travel as consolidated package to another node for further sortation (resort). The objective function minimizes the total cost by varying the resort percentages between nodes and provides the optimal resource allocation and number of sorts at each node.

Keywords: distribution strategy, mathematical model, network design, supply chain management

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Kious Mecheri, Hadjadj Abdechafik, Ameur Aissa

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The wear of cutting tool degrades the quality of the product in the manufacturing processes. The online monitoring of the cutting tool wear level is very necessary to prevent the deterioration of the quality of machining. Unfortunately there is not a direct manner to measure the cutting tool wear online. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission etc. In this work, a neural network system is elaborated in order to estimate the flank wear from the cutting force measurement and the cutting conditions.

Keywords: flank wear, cutting forces, high speed milling, signal processing, neural network

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Authors: Helen J. Spray, Karina J. Linnell

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Local-global bias in adulthood is strongly dependent on environmental factors and a global bias is not the universal characteristic of adult perception it was once thought to be: whilst Western adults typically demonstrate a global bias, Namibian adults living in traditional villages possess a strong local bias. Furthermore, environmental effects on local-global bias have been shown to be highly gender-specific; whereas urbanisation promoted a global bias in urbanised Namibian women but not men, education promoted a global bias in urbanised Namibian men but not women. Adult populations, however, provide only a snapshot of the gene-environment interactions which shape perceptual bias. Yet, to date, there has been little work on the development of local-global bias across environmental settings. In the current study, local-global bias was assessed using a similarity-matching task with Navon figures in children aged between 4 and 15 years from across three populations: traditional Namibians, urban Namibians, and urban British. For the two Namibian groups, measures of urbanisation and education were obtained. Data were subjected to both between-group and within-group analyses. Between-group analyses compared developmental trajectories across population and gender. These analyses revealed a global bias from even as early as 4 in the British sample, and showed that the developmental onset of a global bias is not fixed. Urbanised Namibian children ultimately developed a global bias that was indistinguishable from British children; however, a global bias did not emerge until much later in development. For all populations, the greatest developmental effects were observed directly following the onset of formal education. No overall gender effects were observed; however, there was a significant gender by age interaction which was difficult to reconcile with existing biological-level accounts of gender differences in the development of local-global bias. Within-group analyses compared the effects of urbanisation and education on local-global bias for traditional and urban Namibian boys and girls separately. For both traditional and urban boys, education mediated all effects of age and urbanisation; however, this was not the case for girls. Traditional Namibian girls retained a local bias regardless of age, education, or urbanisation, and in urbanised girls, the development of a global bias was not attributable to any one factor specifically. These results are broadly consistent with aforementioned findings that education promoted a global bias in urbanised Namibian men but not women. The development of local-global bias does not follow a fixed trajectory but is subject to environmental control. Understanding how variability in the development of local-global bias might arise, particularly in the context of gender, may have far-reaching implications. For example, a number of educationally important cognitive functions (e.g., spatial ability) are known to show consistent gender differences in childhood and local-global bias may mediate some of these effects. With education becoming an increasingly prevalent force across much of the developing world it will be important to understand the processes that underpin its effects and their implications.

Keywords: cross-cultural, development, education, gender, local-global bias, perception, urbanisation, urbanization

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Authors: Manal Bouasria, Mohammed-Hichem Benzaama, Valérie Pralong, Yassine El Mendili

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Reducing the quantity of cement in cementitious composites can help to reduce the environmental effect of construction materials. By-products such as ferronickel slags (FNS), fly ash (FA), and Crepidula fornicata (CR) are promising options for cement replacement. In this work, we investigated the relevance of substituting cement with FNS-CR and FA-CR on the mechanical properties of mortar and on the thermal properties of concrete. Foraging intervals ranging from 2 to 28 days, the mechanical properties are obtained by 3-point bending and compression tests. The chosen mix is used to construct a prototype in order to study the material’s hygrothermal performance. The data collected by the sensors placed on the prototype was utilized to build an artificial neural network.

Keywords: artificial neural network, cement, circular economy, concrete, by products

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Authors: Mohammad Arif Khan

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This paper analyzes and presents the development of Artificial Neural Network based controller of space vector modulation (ANN-SVPWM) for a seven-phase voltage source inverter. At first, the conventional method of producing sinusoidal output voltage by utilizing six active and one zero space vectors are used to synthesize the input reference, is elaborated and then new PWM scheme called Artificial Neural Network Based PWM is presented. The ANN based controller has the advantage of the very fast implementation and analyzing the algorithms and avoids the direct computation of trigonometric and non-linear functions. The ANN controller uses the individual training strategy with the fixed weight and supervised models. A computer simulation program has been developed using Matlab/Simulink together with the neural network toolbox for training the ANN-controller. A comparison of the proposed scheme with the conventional scheme is presented based on various performance indices. Extensive Simulation results are provided to validate the findings.

Keywords: space vector PWM, total harmonic distortion, seven-phase, voltage source inverter, multi-phase, artificial neural network

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Edouard Ngor Sarr, Abel Diatta, Serigne Mor Toure, Ousmane Sall, Lamine Faty

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Thanks to Web 2, we are witnessing a form of democratization of the spoken word, an exponential increase in the number of users on the web, but also, and above all, the accumulation of a daily flow of content that is becoming, at times, uncontrollable. Added to this is the rise of a violent social fabric characterised by hateful and racial comments, insults, and other content that contravenes social rules and the platforms' terms of use. Consequently, managing and regulating this mass of new content is proving increasingly difficult, requiring substantial human, technical, and technological resources. Without regulation and with the complicity of anonymity, this toxic content can pollute discussions and make these online spaces highly conducive to abuse, which very often has serious consequences for certain internet users, ranging from anxiety to suicide, depression, or withdrawal. The toxicity of a comment is defined as anything that is rude, disrespectful, or likely to cause someone to leave a discussion or to take violent action against a person or a community. Two levels of measures are needed to deal with this deleterious situation. The first measures are being taken by governments through draft laws with a dual objective: (i) to punish the perpetrators of these abuses and (ii) to make online platforms accountable for the mistakes made by their users. The second measure comes from the platforms themselves. By assessing the content left by users, they can set up filters to block and/or delete content or decide to suspend the user in question for good. However, the speed of discussions and the volume of data involved mean that platforms are unable to properly monitor the moderation of content produced by Internet users. That's why they use human moderators, either through recruitment or outsourcing. Moderating comments on the web means assessing and monitoring users‘ comments on online platforms in order to strike the right balance between protection against abuse and users’ freedom of expression. It makes it possible to determine which publications and users are allowed to remain online and which are deleted or suspended, how authorised publications are displayed, and what actions accompany content deletions. In this study, we look at the problem of automatic moderation of toxic comments in the face of local African languages and, more specifically, on social network comments in Senegal. We review the state of the art, highlighting the different approaches, algorithms, and tools for moderating comments. We also study the issues and challenges of moderation in the face of web ecosystems with lesser-known languages, such as local languages.

Keywords: moderation, local languages, Senegal, toxic comments

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Authors: H. El-Kamchouchi, Heba Gaber, Fatma Ahmed, Dalia H. El-Kamchouchi

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Proxy signature scheme permits an original signer to delegate his/her signing capability to a proxy signer, and then the proxy signer generates a signing message on behalf of the original signer. The two parties must be able to authenticate one another and agree on a secret encryption key, in order to communicate securely over an unreliable public network. Authenticated key agreement protocols have an important role in building secure communications network between the two parties. In this paper, we present a secure proxy signature scheme over an efficient and secure authenticated key agreement protocol based on the discrete logarithm problem.

Keywords: proxy signature, warrant partial delegation, key agreement, discrete logarithm

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Authors: Hyunjoo Park, Taehyun Kim, Taehyun Kim

Abstract:

The government that declared 'de-nuclearization' pushes up renewable energy policies such as solar power and wind power as an alternative to nuclear power generation. However, local residents who are concerned about the development and natural disasters have been hit by opposition, and related businesses around the country are experiencing difficulties. There is also a voice saying that installing a large wind power generator will cause landslides, low frequencies and noise, which will have a bad influence. Renewal is only a harmful and disgusting facility for the residents. In this way, it is expected that extreme social conflicts will occur in the decision making process related to the locally unwanted land-use (LULU). The government's efforts to solve this problem have been steadily progressing, but the systematic methodology for bringing in active participation and opinion gathering of the residents has not yet been established except for the simple opinion poll or referendum. Therefore, it is time to identify the factors that concern the local residents about the wind power generation facilities, and to find ways to make policy decision-making possible. In this study, we analyze the perception of people about offshore and onshore wind power facilities through questionnaires or interviews, and examine quantitative and qualitative precedent studies to analyze them. In addition, the study evaluates what factors affect the local acceptance of wind power facilities. As a result of the factor analysis of the questionnaire items, factors affecting the residents' acceptance of the wind power facility were extracted from four factors such as environmental, economic, risk, social, and management factor. The study also found that the influence of the determinants of local acceptance on the regional acceptability differs according to the demographic characteristics such as gender and income level. This study will contribute to minimizing the conflict on the installation of wind power facilities through communication among the local residents.

Keywords: factor analysis, local acceptance, locally unwanted land-use, LULU, wind power generation facilities

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Authors: Prathibha Ganesan

Abstract:

This paper discusses discontents of inhabitants in the landfills and its culmination into resistance against centralised waste disposal during the last three decades in Kerala. The study is based on a sample survey of 175 households located in the landfill sites and city limits of two Municipal Corporations viz. Thrissur and Cochin. The study found that waste is dumped in the periphery of the urban area where economically and socially vulnerable people are densely populated. Moreover, landfill sites are unscientifically managed to cause severe socio-economic and health issues to the local people, finally leading to their mobilisation and persistent struggle. The struggles often culminate in the closure of landfills or forced relocation or abandonment of the region by the community. The study concluded that persistent people’s struggles compel the local state to either find alternatives to centralised solid waste management system or use political power to subsume the local resistance. The persistence of the struggles determined the type waste governance adopted by the local governments.

Keywords: solid waste management, municipal corporation, resistance movements, urban, Kerala

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