Search results for: convolutional networks

Authors: K. Roushanger, A. Soleymanzadeh

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Weirs are structures often used in irrigation techniques, sewer networks and flood protection. However, the hydraulic behavior of this type of weir is complex and difficult to predict accurately. An accurate flow prediction over a weir mainly depends on the proper estimation of discharge coefficient. In this study, the Genetic Expression Programming (GEP) approach was used for predicting trapezoidal and rectangular sharp-crested side weirs discharge coefficient. Three different performance indexes are used as comparing criteria for the evaluation of the model’s performances. The obtained results approved capability of GEP in prediction of trapezoidal and rectangular side weirs discharge coefficient. The results also revealed the influence of downstream Froude number for trapezoidal weir and upstream Froude number for rectangular weir in prediction of the discharge coefficient for both of side weirs.

Keywords: discharge coefficient, genetic expression programming, trapezoidal weir

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Authors: Stephan Treuke

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The paper analyses the neighbourhood effects on the economic mobility of the inhabitants of three segregated communities of Salvador (Brazil), in other words, the socio-economic advantages and disadvantages affecting the lives of poor people due to their embeddedness in specific socio-residential contexts. Recent studies performed in Brazilian metropolis have concentrated on the structural dimensions of negative externalities in order to explain neighbourhood-level variations in a field of different phenomena (delinquency, violence, access to the labour market and education) in spatial isolated and socially homogeneous slum areas (favelas). However, major disagreement remains whether the contiguity between residents of poor neighbourhoods and higher-class condominio-dwellers provides structures of opportunities or whether it fosters socio-spatial stigmatization. Based on a set of interviews, investigating the variability of interpersonal networks and their activation in the struggle for economic inclusion, the study confirms that the proximity of Nordeste de Amaralina to middle-/upper-class communities affects positively the access to labour opportunities. Nevertheless, residential stigmatization, as well as structures of social segmentation, annihilate these potentials. The lack of exposition to individuals and groups extrapolating from the favela’s social, educational and cultural context restricts the structures of opportunities to local level. Therefore, residents´ interpersonal networks reveal a high degree of redundancy and localism, based on bonding ties connecting family and neighbourhood members. The resilience of segregational structures in Plataforma contributes to the naturalization of social distance patters. It’s embeddedness in a socially homogeneous residential area (Subúrbio Ferroviário), growing informally and beyond official urban politics, encourages the construction of isotopic patterns of sociability, sharing the same values, social preferences, perspectives and behaviour models. Whereas it’s spatial isolation correlates with the scarcity of economic opportunities, the social heterogeneity of Fazenda Grande II interviewees and the socialising effects of public institutions mitigate the negative repercussions of segregation. The networks’ composition admits a higher degree of heterophilia and a greater proportion of bridging ties accounting for the access to broader information actives and facilitating economic mobility. The variability observed within the three different scenarios urges to reflect about the responsability of urban politics when it comes to the prevention or consolidation of the social segregation process in Salvador. Instead of promoting the local development of the favela Plataforma, public housing programs priorize technocratic habitational solutions without providing the residents’ socio-economic integration. The impact of negative externalities related to the homogeneously poor neighbourhood is potencialized in peripheral areas, turning its’ inhabitants socially invisible, thus being isolated from other social groups. The example of Nordeste de Amaralina portrays the failing interest of urban politics to bridge the social distances structuring the brazilian society’s rigid stratification model, founded on mecanisms of segmentation (unequal access to labour market and education system, public transport, social security and law protection) and generating permanent conflicts between the two socioeconomically distant groups living in geographic contiguity. Finally, in the case of Fazenda Grande II, the public investments in both housing projects and complementary infrastructure (e.g. schools, hospitals, community center, police stations, recreation areas) contributes to the residents’ socio-economic inclusion.

Keywords: economic mobility, neighborhood effects, Salvador, segregation

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Khaled Sellami, Lynda Sellami, Pierre F. Tiako

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Ubiquitous computing helps make data and services available to users anytime and anywhere. This makes the cooperation of devices a crucial need. In return, such cooperation causes an overload of the devices and/or networks, resulting in network malfunction and suspension of its activities. Our goal in this paper is to propose an approach of devices reconfiguration in order to help to reduce the energy consumption in ubiquitous environments. The idea is that when high-energy consumption is detected, we proceed to a change in component distribution on the devices to reduce and/or balance the energy consumption. We also investigate the possibility to detect high-energy consumption of devices/network based on devices abilities. As a result, our idea realizes a reconfiguration of devices aimed at reducing the consumption of energy and/or load balancing in ubiquitous environments.

Keywords: ubiquitous computing, load balancing, device energy consumption, reconfiguration

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Authors: Mao Zhaofang, Xu Yida, Fang Kan, Fu Enyuan, Zhao Zhao

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Nowadays, last-mile distribution plays an increasingly important role in the whole industrial chain delivery link and accounts for a large proportion of the whole distribution process cost. Promoting the upgrading of logistics networks and improving the layout of final distribution points has become one of the trends in the development of modern logistics. Due to the discrete and heterogeneous needs and spatial distribution of customer demand, which will lead to a higher delivery failure rate and lower vehicle utilization, last-mile delivery has become a time-consuming and uncertain process. As a result, courier companies have introduced a range of innovative parcel storage facilities, including pick-up points and lockers. The introduction of pick-up points and lockers has not only improved the users’ experience but has also helped logistics and courier companies achieve large-scale economy. Against the backdrop of the COVID-19 of the previous period, contactless delivery has become a new hotspot, which has also created new opportunities for the development of collection services. Therefore, a key issue for logistics companies is how to design/redesign their last-mile distribution network systems to create integrated logistics and distribution networks that consider pick-up points and lockers. This paper focuses on the introduction of self-pickup facilities in new logistics and distribution scenarios and the heterogeneous demands of customers. In this paper, we consider two types of demand, including ordinary products and refrigerated products, as well as corresponding transportation vehicles. We consider the constraints associated with self-pickup points and lockers and then address the location-routing problem with self-pickup facilities and heterogeneous demands (LRP-PFHD). To solve this challenging problem, we propose a mixed integer linear programming (MILP) model that aims to minimize the total cost, which includes the facility opening cost, the variable transport cost, and the fixed transport cost. Due to the NP-hardness of the problem, we propose a hybrid adaptive large-neighbourhood search algorithm to solve LRP-PFHD. We evaluate the effectiveness and efficiency of the proposed algorithm by using instances generated based on benchmark instances. The results demonstrate that the hybrid adaptive large neighbourhood search algorithm is more efficient than MILP solvers such as Gurobi for LRP-PFHD, especially for large-scale instances. In addition, we made a comprehensive analysis of some important parameters (e.g., facility opening cost and transportation cost) to explore their impacts on the results and suggested helpful managerial insights for courier companies.

Keywords: city logistics, last-mile delivery, location-routing, adaptive large neighborhood search

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Authors: Eiman Nour

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The rising interest in harvesting power is because of the capabilities application of expanding self-powered systems based on nanostructures. Using renewable and self-powered sources is necessary for the growth of green electronics and could be of the capability to wireless sensor networks. The ambient mechanical power is among the ample sources for various power harvesting device configurations that are published. In this work, we design and fabricate a paper-based nanogenerator (NG) utilizing piezoelectric zinc oxide (ZnO) nanowires (NWs) grown hydrothermally on a paper substrate. The fabricated NG can harvest ambient mechanical energy from various kinds of human motions, such as handwriting. The fabricated NG from a single ZnO NWs/PVDF-TrFE NG has been used firstly as handwriting-driven NG. The mechanical pressure applied on the paper platform while handwriting is harvested by the NG to deliver electrical energy; depending on the mode of handwriting, a maximum harvested voltage of 4.8 V was obtained.

Keywords: nanostructure, zinc oxide, nanogenerator, energy harvesting

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Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

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Authors: Mohadese Motahari

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In general, we consider the phenomenon of "fandism" or extreme fandom to be an aspect of fandom for a person, a group, or a collection, which leads to extreme support for them. So, for example, we consider a fan or a "fanatic" (which literally means a "fanatical person") to be a person who is extremely interested in a certain topic or topics and has a special passion and fascination for that issue. It may also be beyond the scope of logic and normal behavior of the society. With the expansion of the media and the advancement of technology, the phenomenon of fandom also underwent many changes and not only became more intense, but a large economy was also formed alongside it, and it is becoming more and more important every day. This economy, which emerged from the past with the formation of the first media, has now taken a different form with the development of media and social networks, as well as the change in the interaction between celebrities and audiences. Earning huge amounts of money with special methods in every social network and every media is achieved through fans and fandoms. In this article, we have studied the relationship between fans and famous people with reference to the economic debates surrounding it.

Keywords: fandism, famous people, social media, new media

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Authors: Tobias Milz, L. Macpherson, Varvara Vetrova

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The law is a fundamental pillar of human societies as it shapes, controls and governs how humans conduct business, behave and interact with each other. Recent advances in computer-assisted technologies such as NLP, data science and AI are creating opportunities to support the practice, research and study of this pervasive domain. It is therefore not surprising that there has been an increase in investments into supporting technologies for the legal industry (also known as “legal tech” or “law tech”) over the last decade. A sub-discipline of particular appeal is concerned with assisted legal research. Supporting law researchers and practitioners to retrieve information from the vast amount of ever-growing legal documentation is of natural interest to the legal research community. One tool that has been in use for this purpose since the early nineteenth century is legal citation indexing. Among other use cases, they provided an effective means to discover new precedent cases. Nowadays, computer-assisted network analysis tools can allow for new and more efficient ways to reveal the “hidden” information that is conveyed through citation behavior. Unfortunately, access to openly available legal data is still lacking in New Zealand and access to such networks is only commercially available via providers such as LexisNexis. Consequently, there is a need to create, analyze and provide a legal citation network with sufficient data to support legal research tasks. This paper describes the development and analysis of a legal citation Network for New Zealand containing over 300.000 decisions from 125 different courts of all areas of law and jurisdiction. Using python, the authors assembled web crawlers, scrapers and an OCR pipeline to collect and convert court decisions from openly available sources such as NZLII into uniform and machine-readable text. This facilitated the use of regular expressions to identify references to other court decisions from within the decision text. The data was then imported into a graph-based database (Neo4j) with the courts and their respective cases represented as nodes and the extracted citations as links. Furthermore, additional links between courts of connected cases were added to indicate an indirect citation between the courts. Neo4j, as a graph-based database, allows efficient querying and use of network algorithms such as PageRank to reveal the most influential/most cited courts and court decisions over time. This paper shows that the in-degree distribution of the New Zealand legal citation network resembles a power-law distribution, which indicates a possible scale-free behavior of the network. This is in line with findings of the respective citation networks of the U.S. Supreme Court, Austria and Germany. The authors of this paper provide the database as an openly available data source to support further legal research. The decision texts can be exported from the database to be used for NLP-related legal research, while the network can be used for in-depth analysis. For example, users of the database can specify the network algorithms and metrics to only include specific courts to filter the results to the area of law of interest.

Keywords: case citation network, citation analysis, network analysis, Neo4j

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Authors: Ravneil Nand

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Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

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Authors: Sandip Mitra

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Studies on Governance and Accountability has often stressed the need to release Data in public domain to increase transparency ,which otherwise act as an evidence of performance. However, inefficient handling, lack of capacity and the dynamics of transfers (especially fund transfers) are important issues which need appropriate attention. E-Governance alone can not serve as a measure of transparency as long as a comprehensive planning is instituted. Studies on Governance and public exposure has often triggered public opinion in favour or against any government. The root of the problem (especially in local governments) lies in the management of the governance. The participation of the people in the local government functioning, the networks within and outside the locality, synergy with various layers of Government are crucial in understanding the activities of any government. Unfortunately, data on such issues are not released in the public domain .If they are at all released , the extraction of information is often hindered for complicated designs. A Study has been undertaken with a few local Governments in India. The data has been analysed to substantiate the views.

Keywords: accountability, e-governance, transparency, local government

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Elmabruk Laias

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IEEE Standard 802.16 provides QoS (Quality of Service) for the applications such as Voice over IP, video streaming and high bandwidth file transfer. With the ability of broadband wireless access of an IEEE 802.16 system, a WiMAX TDD frame contains one downlink subframe and one uplink subframe. The capacity allocated to each subframe is a system parameter that should be determined based on the expected traffic conditions. a proper resource allocation scheme for packet transmissions is imperatively needed. In this paper, we present a new resource allocation scheme, called additional bandwidth yielding (ABY), to improve transmission efficiency of an IEEE 802.16-based network. Our proposed scheme can be adopted along with the existing scheduling algorithms and the multi-priority scheme without any change. The experimental results show that by using our ABY, the packet queuing delay could be significantly improved, especially for the service flows of higher-priority classes.

Keywords: IEEE 802.16, WiMAX, OFDMA, resource allocation, uplink-downlink mapping

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Authors: R. Rezaeipour

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One of the key aspects of power quality improvement in power system is the mitigation of voltage sags/swells and flicker. Custom power devices have been known as the best tools for voltage disturbances mitigation as well as reactive power compensation. Dynamic voltage restorer (DVR) which is the most efficient and effective modern custom power device can provide the most commercial solution to solve several problems of power quality in distribution networks. This paper deals with analysis and simulation technique of DVR based on instantaneous power theory which is a quick control to detect signals. The main purpose of this work is to remove three important disturbances including voltage sags/swells and flicker. Simulation of the proposed method was carried out on two sample systems by using MATLAB software environment and the results of simulation show that the proposed method is able to provide desirable power quality in the presence of wide range of disturbances.

Keywords: DVR, power quality, voltage sags, voltage swells, flicker

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Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

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The Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse the Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. In this paper, we do an exploratory data analysis using STATS19 data. For the past 30 years, the UK has had a good record in reducing fatalities. The UK ranked third based on the number of road deaths per million inhabitants. There were around 165,000 accidents reported in the Great Britain in 2009 and it has been decreasing every year until 2019 which is under 120,000. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe.

Keywords: road safety, data analysis, openstreetmap, feature expanding.

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Authors: André Smith, Amr Abdel-Dayem

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The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.

Keywords: colour data, local stereo matching, stereo correspondence, disparity map

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Authors: Sudha Arlikatti, Laura Siebeneck, Simon A. Andrew

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The objective of this research is to measure organizational resiliency through five attributes namely, rapidity, redundancy, resourcefulness, and robustness and to provide recommendations for resiliency building in flood risk communities. The research was conducted in Thailand following the severe floods of 2011 triggered by Tropical Storm Nock-ten. The floods lasted over eight months starting in June 2011 affecting 65 of the country’s 76 provinces and over 12 million people. Funding from a US National Science Foundation grant was used to collect ephemeral data in rural (Ayutthaya), suburban (Pathum Thani), and urban (Bangkok) provinces of Thailand. Semi-structured face-to-face interviews were conducted in Thai with 44 contacts from public, private, and non-profit organizations including universities, schools, automobile companies, vendors, tourist agencies, monks from temples, faith based organizations, and government agencies. Multiple triangulations were used to analyze the data by identifying selective themes from the qualitative data, validated with quantitative data and news media reports. This helped to obtain a more comprehensive view of how organizations in different geographic settings varied in their understanding of what enhanced or hindered their resilience and consequently their speed and capacities to respond. The findings suggest that the urban province of Bangkok scored highest in resourcefulness, rapidity of response, robustness, and ability to rebound. This is not surprising considering that it is the country’s capital and the seat of government, economic, military and tourism sectors. However, contrary to expectations all 44 respondents noted that the rural province of Ayutthaya was the fastest to recover amongst the three. Its organizations scored high on redundancy and rapidity of response due to the strength of social networks, a flood disaster sub-culture due to annual flooding, and the help provided by monks from and faith based organizations. Organizations in the suburban community of Pathum Thani scored lowest on rapidity of response and resourcefulness due to limited and ambiguous warnings, lack of prior flood experience and controversies that government flood protection works like sandbagging favored the capital city of Bangkok over them. Such a micro-level examination of organizational resilience in rural, suburban and urban areas in a country through mixed methods studies has its merits in getting a nuanced understanding of the importance of disaster subcultures and religious norms for resilience. This can help refocus attention on the strengths of social networks and social capital, for flood mitigation.

Keywords: disaster subculture, flood response, organizational resilience, Thailand floods, religious beliefs and response, social capital and disasters

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Authors: Julia Córdoba

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People are not completely autonomous, as we live in society; therefore, people could be defined as relationally dependent. The lack, decrease or loss of physical, psychological and/or social interdependence due to a disability situation is known as dependence. This is related to the need for help from another person in order to carry out activities of daily living. This population group lives with major social limitations that significantly reduce their participation and autonomy. They have high levels of stigma and invisibility from private environments (family and close networks), as well as from the public order (environment, community). The importance of this study lies in the fact that the lack of support and adjustments leads to what authors call the circle of exclusion. This circle describes how not accessing services - due to the difficulties caused by the disability situation impacts biological, social and psychological levels. This situation produces higher levels of exclusion and vulnerability. This study will focus on the process of autonomy and dependence of adults with disability from the model of disability proposed by the International Classification of Functioning, Health and Disability (ICF). The objectives are: i) to write down the relationship between autonomy and dependence based on socio-health variables and ii) to determine the relationship between the situation of autonomy and dependence and the expectations and interests of the participants. We propose a study that will use a survey technique through a previously validated virtual questionnaire. The data obtained will be analyzed using quantitative and qualitative methods for the details of the profiles obtained. No less than 200 questionnaires will be administered to people between 18 and 64 years of age who self-identify as having some degree of dependency due to disability. For the analysis of the results, the two main variables of autonomy and dependence will be considered. Socio-demographic variables such as age, gender identity, area of residence and family composition will be used. In relation to the biological dimension of the situation, the diagnosis, if any, and the type of disability will be asked. For the description of these profiles of autonomy and dependence, the following variables will be used: self-perception, decision-making, interests, expectations and life project, care of their health condition, support and social network, and labor and educational inclusion. The relationship between the target population and the variables collected provides several guidelines that could form the basis for the analysis of other research of interest in terms of self-perception, autonomy and dependence. The areas and situations where people state that they have greater possibilities to decide and have a say will be obtained. It will identify social (networks and support, educational background), demographic (age, gender identity and residence) and health-related variables (diagnosis and type of disability, quality of care) that may have a greater relationship with situations of dependency or autonomy. It will be studied whether the level of autonomy and/or dependence has an impact on the type of expectations and interests of the people surveyed.

Keywords: life project, disability, inclusion, autonomy

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Authors: Edlira Kalemi, Sule Yildirim-Yayilgan

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Online Social Networks (OSNs) are nowadays being used widely and intensively for crime investigation and prevention activities. As they provide a lot of information they are used by the law enforcement and intelligence. An extensive review on existing solutions and models for collecting intelligence from this source of information and making use of it for solving crimes has been presented in this article. The main focus is on smart solutions and models where ontologies have been used as the main approach for representing criminal domain knowledge. A framework for a prototype ontology named SC-Ont will be described. This defines terms of the criminal domain ontology and the relations between them. The terms and the relations are extracted during both this review and the discussions carried out with domain experts. The development of SC-Ont is still ongoing work, where in this paper, we report mainly on the motivation for using smart ontology models and the possible benefits of using them for solving crimes.

Keywords: criminal digital evidence, social media, ontologies, reasoning

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Authors: Yamba Dabone, Tounwendyam Frédéric Ouedraogo, Pengwendé Justin Kouraogo, Oumarou Sie

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Reliable data transport over the Internet is one of the goals of researchers in the field of computer science. Data such as videos and audio files are becoming increasingly large. As a result, transporting them over the Internet is becoming difficult. Therefore, it has been important to establish a method to locally interconnect autonomous systems (AS) with each other to facilitate traffic exchange. It is in this context that Internet Exchange Points (IXPs) are set up to facilitate local and even regional traffic. They are now the lifeblood of the Internet. Therefore, it is important to think about the factors that can characterize IXPs. However, other more quantifiable characteristics can help determine the quality of an IXP. In addition, these characteristics may allow ISPs to have a clearer view of the exchange node and may also convince other networks to connect to an IXP. To that end, we define five new IXP characteristics: the attraction rate (τₐₜₜᵣ); and the peering rate (τₚₑₑᵣ); the target rate of an IXP (Objₐₜₜ); the number of IXP links (Nₗᵢₙₖ); the resistance rate τₑ𝒻𝒻 and the attraction failure rate (τ𝒻).

Keywords: characteristic, autonomous system, internet service provider, internet exchange point, rate

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Authors: Li Qiang, Yang Ze-Ming, Liu Bao-Xu, Jiang Zheng-Wei

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With the increasing complexity of cyberspace security, the cyber-attack attribution has become an important challenge of the security protection systems. The difficult points of cyber-attack attribution were forced on the problems of huge data handling and key data missing. According to this situation, this paper presented a reasoning method of cyber-attack attribution based on threat intelligence. The method utilizes the intrusion kill chain model and Bayesian network to build attack chain and evidence chain of cyber-attack on threat intelligence platform through data calculation, analysis and reasoning. Then, we used a number of cyber-attack events which we have observed and analyzed to test the reasoning method and demo system, the result of testing indicates that the reasoning method can provide certain help in cyber-attack attribution.

Keywords: reasoning, Bayesian networks, cyber-attack attribution, Kill Chain, threat intelligence

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Authors: Nicole Christiansen, Hanne Marie Motzfeldt

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In recent years, more and more HR professionals have been using cyber-vetting in job recruitment in an effort to find the perfect match for the company. These practices are growing rapidly, accessing a vast amount of data from social networks, some of which is privileged and protected information. Thus, there is a risk that the right to privacy is becoming a duty to manage your private data. This paper investigates to which degree a job applicant's fundamental rights are protected adequately in current and future legislation in the EU. This paper argues that current data protection regulations and forthcoming regulations on the use of AI ensure sufficient protection. However, even though the regulation on paper protects employees within the EU, the recruitment sector may not pay sufficient attention to the regulation as it not specifically targeting this area. Therefore, the lack of specific labor and employment regulation is a concern that the social partners should attend to.

Keywords: AI, cyber vetting, data protection, job recruitment, online privacy

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Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

Abstract:

Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: Boudemagh Naime

Abstract:

Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: Umer Mukhtar

Abstract:

Supply Chain Competitiveness is the capability of a supply chain to deliver value to the customer for the sake of competitive advantage. Service Performance and Quality intervene between supply chain actors including functions inside the firm in a significant way for the supply chain to achieve a competitive position in the market to gain competitive advantage. Supply Chain competitiveness is the current issue of interest because of supply chains’ competition for competitive advantage rather than firms’. A proposed theoretical model is developed by extracting and integrating different theories to pursue further inquiry based on case studies and survey design. It is also intended to develop a scale of service performance for functions of the focal firm that is a revolving center for a whole supply chain.

Keywords: supply chain competitiveness, service performance in supply chain, service quality in supply chain, competitive advantage by supply chain, networks and supply chain, customer value, value supply chain, value chain

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Authors: Sheng-Shih Wang, Wei-Ting Wang

Abstract:

This paper designs an intelligent guidance system, based on wireless sensor networks, for efficient parking in parking lots. The proposed system consists of a parking space allocation subsystem, a parking space monitoring subsystem, a driving guidance subsystem, and a vehicle detection subsystem. In the system, we propose a novel and effective virtual coordinate system for sensing and displaying devices to determine the proper vacant parking space and provide the precise guidance to the driver. This study constructs a ZigBee-based wireless sensor network on Arduino platform and implements the prototype of the proposed system using Arduino-based complements. Experimental results confirm that the proposed prototype can not only work well, but also provide drivers the correct parking information.

Keywords: Arduino, parking guidance, wireless sensor network, ZigBee

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Authors: Hesham A. El Zouka, Mustafa M. Hosni ka

Abstract:

The Radio Frequency Identification (RFID) technology has a diverse base of applications, but it is also prone to security threats. There are different types of security attacks that limit the range of the RFID applications. For example, deploying the RFID networks in insecure environments could make the RFID system vulnerable to many types of attacks such as spoofing attack, location traceability attack, physical attack and many more. Therefore, security is often an important requirement for RFID systems. In this paper, RFID mutual authentication protocol is implemented based on mobile agent technology and timestamp, which are used to provide strong authentication and integrity assurances to both the RFID readers and their corresponding RFID tags. The integration of mobile agent technology and timestamp provides promising results towards achieving this goal and towards reducing the security threats in RFID systems.

Keywords: RFID, security, authentication protocols, privacy, agent-based architecture, time-stamp, digital signature

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Authors: Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević, Lidija R. Jevrić, Stela Jokić

Abstract:

The subject of this paper is to correlate antibacterial behavior of benzimidazole derivatives with their molecular characteristics using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on the inhibitory activity of benzimidazole derivatives against Staphylococcus aureus. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as a calibration models for prediction of the inhibitory activity. The quality of the models was validated by leave one out (LOO) technique and by using external data set. High agreement between experimental and predicted inhibitory acivities indicated the good quality of the derived models. These results are part of the CMST COST Action No. CM1306 "Understanding Movement and Mechanism in Molecular Machines".

Keywords: Antibacterial, benzimidazoles, chemometric, QSAR.

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Authors: Soran Tarkhani

Abstract:

A glance at social media shows that Russia's invasion of Ukraine receives considerable support among Arabs. This significant support for the Russian invasion of Ukraine is puzzling since most Arab leaders openly condemned the Russian invasion through the UN ES‑11/4 Resolution, and Arabs are among the first who experienced the devastating consequences of war firsthand. This article tries to answer this question by using multiple regression to analyze the online content of Arab responses to Russia's invasion of Ukraine on seven major news networks: CNN Arabic, BBC Arabic, Sky News Arabic, France24 Arabic, DW, Aljazeera, and Al-Arabiya. The article argues that the underlying reason for this Arab support is a reaction to the common anti-Western sentiments among Arabs. The empirical result from regression analysis supports the central arguments and uncovers the motivations behind the endorsement of the Russian invasion of Ukraine and the opposing Ukraine by many Arabs.

Keywords: Ukraine, Russia, Arabs, Ukrainians, Russians, Putin, invasion, Europe, war

Procedia PDF Downloads 49