Computer Networks and Communications

A time domain (TD) optimal sampling interpolation (OSI) scheme to efficiently reconstruct the transient field radiated by an ultra-wide band (UWB) antenna over a plane in the near-field (NF) region from a minimum number of its NF TD samples, acquired through a non-conventional plane-rectangular scanning, is developed in this study. Such a result has been achieved by properly exploiting the non-redundant sampling representations of electromagnetic fields and modelling the antenna by a proper rotational surface. Since the UWB antennas are usually quasi-planar, such a surface can be conveniently chosen coincident with that of an oblate ellipsoid or with that formed by two bowls with the same circular aperture and possibly different lateral bends. Numerical examples, which assess the accuracy of the related TD OSI representations, are reported.

The recently introduced 5G New Radio is the first wireless standard natively designed to support critical and massive machine type communications (MTC). However, it is already becoming evident that some of the more demanding requirements for MTC cannot be fully supported by 5G networks. Alongside, emerging use cases and applications towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general and MTC in particular. Next generation wireless networks, namely 6G, should therefore be an agile and efficient convergent network designed to meet the diverse and challenging requirements anticipated by 2030. This paper explores the main drivers and requirements of MTC towards 6G, and discusses a wide variety of enabling technologies. More specifically, we first explore the emerging key performance indicators for MTC in 6G. Thereafter, we present a vision for an MTC-optimized holistic end-to-end network architecture. Finally, key enablers towards (1) u...

The project aims to develop a personal-assistant for Linux-based systems. Jarvis draws its inspiration from virtual assistants like Cortana for Windows, and Siri for iOS. ​It has been designed to provide a ​user-friendly interface for carrying out a variety of tasks by employing certain ​well-defined commands​. ​Users can interact with the assistant either through ​voice commands or using keyboard input. As a personal assistant, Jarvis assists the end-user with ​day-to-day activities like general human conversation, searching queries in google, bing or yahoo, searching for videos, retrieving images, live weather conditions, word meanings, searching for medicine details, health recommendations based on symptoms and reminding the user about the scheduled events and tasks. The user statements/commands are analysed with the help of ​machine learning to give an optimal solution.

Accurate seismic signal analysis is vital for earthquake detection, subsurface imaging, and geotechnical assessments. However, field-acquired seismic data is often contaminated by various noise sources. Traditional low-pass filters, like sinc-based FIR filters, reduce high-frequency noise but may distort signal features. This study presents a hybrid denoising approach combining a sinc FIR filter with a machine learning (ML) enhancement using a linear regression (LinearFit) model. A clean seismic signal was simulated and degraded with additive white Gaussian noise, then sequentially processed through the sinc filter and ML model. Results show that while the sinc filter effectively suppresses noise, the LinearFit ML model further reduces residual errors and better preserves signal integrity, offering an efficient and interpretable tool for seismic signal denoising.

This research reports on a study of the interplay between multi-tasking and collaborative work. We conducted an ethnographic study in two different companies where we observed the experiences and practices of thirty-six information workers. We observed that people continually switch between different collaborative contexts throughout their day. We refer to activities that are thematically connected as working spheres. We discovered that to multi-task and cope with the resulting fragmentation of their work, individuals constantly renew overviews of their working spheres, they strategize how to manage transitions between contexts and they maintain flexible foci among their different working spheres. We argue that system design to support collaborative work should include the notion that people are involved in multiple collaborations with contexts that change continually. System design must take into account these continual changes: people switch between local and global perspectives of their working spheres, have varying states of awareness of their different working spheres, and are continually managing transitions between contexts due to interruptions.

This paper presents a performance evaluation study of dual stack, 6to4 tunnelling, and Network Address Translation transition schemes on simulative method in IPv4/IPv6 networks. This research aims to find out what IPv4/IPv6 transition scheme performs better in terms of performance parameters packet losses, delays, segment delays, query response times, packet delay variations, packet end-to-end delays, jitters, mean opinion score value, and traffic sent and received for data, voice and video traffic. An equal capacity of network load in terms of probes for the three scenarios is considered and benchmarked to ascertain the impact on the performance parameters on IPv4/IPv6 networks. The scenarios are basically network models configured and simulated using Optimized Network Engineering Tool modeler. The core model designed represents an Internet Service Provider network deployed to form an ideal choice of IP domain corresponded to a realistic network topology running both IPv4 and IPv6 ...

Reliable image and video communications over noisy channels has been a great challenge especially for the transmission of large volume of data over unreliable and bandwidth limited channels. One technique to deal with this problem involves taking an existing source encoder and then protecting the bits using appropriate channel coding method. However, channel coding techniques add redundant bits resulting in an increase of the required bandwidth. Also, in the case of multimedia communications, the compressed source exhibits different level of importance among different portions of the encoded bit stream which calls for Unequal Error Protection (UEP). The unequal nature of bit importance that compressed source exhibits can be addressed by exploiting UEP within Hierarchical Quadrature Amplitude Modulation (HQAM) where different signalling points in a constellation are placed at different distances from the adjacent symbols according to their importance. This results in different bit er...

The circle travelling delay between OLT (Optical Line Terminal) and ONU (Optical Network Unit) is one of most important items in dynamic bandwidth allocation (DBA) algorithms in PON, called RTT (Round Trip Time). The RTT is taken into account when OLT assigns the start times for upstream bandwidth grants. In most case, RTT is estimated before making bandwidth allocation decisions in dynamic bandwidth allocation algorithms. If the estimated RTT is incorrect, the bandwidth allocation decisions are not matched with bandwidth requests of channels. Thus, performance of PON can get worse by deviation of RTT. There are several reasons that cause the RTT to be varying, such as processing delay, distance of OLT and ONU, changing in fiber refractive index resulting from temperature drift, and degree of accuracy of RTT estimation methods. In this paper, we evaluate the impacts of RTT inaccuracy on performance of DBA and identify levels of collision and waste of bandwidth. By this way, we propose a method to remedy the performance degradation encountered by the situation.

Nowadays, companies to be competitive must develop capabilities that enable them to respond quickly to market needs. According to some managers, the strategy is the development of dynamic logistics networks based on a collaborative environment. However, the absence of mechanisms to detect and even anticipate potential opportunistic behaviour is an obstacle to the proliferation of this way of working. The article aims to understand the role of trust to sustainability of collaborative processes. The paper begins by discussing the trust properties. It is then discussed how statistical control charts can be used to support the trust monitoring of each member within a collaborative ecosystem. The control charts’ tools suggested in this paper are the Z control charts for trust level monitoring and the Zi capacity index. Finally, it is discussed how this approach can be applied to dynamic logistics networks within the context of a collaborative ecosystem.

Nowadays, companies to be competitive must develop capabilities that enable them to respond quickly to market needs. According to some managers, the strategy is the development of dynamic logistics networks based on a collaborative environment. However, the absence of mechanisms to detect and even anticipate potential opportunistic behaviour is an obstacle to the proliferation of this way of working. The article aims to understand the role of trust to sustainability of collaborative processes. The paper begins by discussing the trust properties. It is then discussed how statistical control charts can be used to support the trust monitoring of each member within a collaborative ecosystem. The control charts’ tools suggested in this paper are the Z control charts for trust level monitoring and the Zi capacity index. Finally, it is discussed how this approach can be applied to dynamic logistics networks within the context of a collaborative ecosystem.

Low cost networked consumer electronics (CE) are widely used. Various applications are offered, including IPTV, VoIP, VoD, PVR and games. At the same time the requirements of computer games by means of CPU and graphics performance are continuously growing. For pervasive gaming in various environments like at home, hotels, or internet cafes, it is beneficial to run games also on mobile devices and modest performance CE devices such as set top boxes. EU IST Games@Large project is developing a new cross-platform approach for distributed 3D gaming in local networks. It introduces novel system architecture and protocols used to transfer the game graphics data across the network to end devices. Simultaneous execution of video games on a central server and a novel streaming approach of the 3D graphics output to multiple end devices enable the access of games on low cost devices that natively lack the power of executing high-quality games.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov.

Multi-terminal switching lattices are typically exploited for modeling switching nano-crossbar arrays that lead to the design and construction of emerging nanocomputers. Typically, the circuit is represented on a single lattice composed by fourterminal switches. In this paper, we propose a two-layer model in order to further minimize the area of regular functions, such as autosymmetric and D-reducible functions, and of decomposed functions. In particular, we propose a switching lattice optimization method for a special class of "regular" Boolean functions, called autosymmetric functions. Autosymmetry is a property that is frequent enough within Boolean functions to be interesting in the synthesis process. Each autosymmetric function can be synthesized through a new function (called restriction), depending on less variables and with a smaller on-set, which can be computed in polynomial time. In this paper we describe how to exploit the autosymmetry property of a Boolean function in order to obtain a smaller lattice representation in a reduced minimization time. The original Boolean function can be constructed through a composition of the restriction with some EXORs of subsets of the input variables. Similarly, the lattice implementation of the function can be constructed using some external lattices for the EXORs, whose outputs will be inputs to the lattice implementing the restriction. Finally, the output of the restriction lattice corresponds to the output of the original function. Experimental results show that the total area of the obtained lattices is often significantly reduced. Moreover, in many cases, the computational time necessary to minimize the restriction is smaller than the time necessary to perform the lattice synthesis of the entire function. Finally, we propose the application of this particular lattice composition technique, based on connected multiple lattices, to the synthesis on switching lattices of D-reducible Boolean functions, and to the more general framework of Preprint submitted to Embedded Hardware Design (Microprocessors and Microsystems)November 12, 2019 lattice synthesis based on logic function decomposition.

Electromagnetic articulography (EMA) is one of the instrumental phonetic research methods used for recording and assessing articulatory movements. Usually, articulographic data are analysed together with standard audio recordings. This paper, however, demonstrates how coupling the articulograph with devices providing other types of information may be used in more advanced speech research. A novel measurement system is presented that consists of the AG 500 electromagnetic articulograph, a 16-channel microphone array with a dedicated audio recorder and a video module consisting of 3 high-speed cameras. It is argued that synchronization of all these devices allows for comparative analyses of results obtained with the three components. To complement the description of the system, the article presents innovative data analysis techniques developed by the authors as well as preliminary results of the system’s accuracy.

A educação para os media tem sido uma área incentivada pelas instituições internacionais, como parte de uma educação cívica, e em Portugal tem registado progressos, embora subsista alguma fragmentação. O RadioActive, financiado pela Comissão Europeia, propõe-se explorar as potencialidades da educação para os media com objetivos de empoderamento e participação de crianças e jovens em contextos de carência ou exclusão. O projeto utiliza as potencialidades da rádio em ambiente digital para estimular a inclusão e reforço de capacidades dos jovens participantes. A sua metodologia é de Investigação-Ação Participativa, fortemente inspirada em Paulo Freire, trabalhando com um framework adaptável aos contextos em que atua, para envolver e criar resultados sustentados. A sua proposta foca-se, assim, nos interesses e motivações pessoais, nos conhecimentos de media e digitais já existentes, bem como na adaptação do projeto à comunidade em que se insere. Neste artigo, argumentamos pela relevânci...

Datasets are most important for performing all the type of data mining tasks. Every dataset has many numbers of attributes and instances. Dimensionality reduction (DR) is one of the preprocessing steps which is used to reduce the dimensions (attributes or features) without losing the data. There are two divisions of reduction they are feature extraction and feature reduction. Feature extraction is the process of decomposition of attributes of the original data (i.e.) merging the attributes of the data Feature selection is the process of selecting the subset of attributes by eliminating features with little or no predictive information. Feature extraction techniques are more adequate than the feature selection. Reduction is done to the larger dataset to decrease the curse of dimensionality. The main objective of this paper is to provide a systematic comparative analysis on feature reduction algorithms such as PCA, LDA and FA to medical dataset (Thyroid, Oesophagal).The performance factor considered are number of attributes reduced and time is observed.

It is impossible to print glass directly from a melt, layer by layer. Glass is not only very sensitive to temperature gradients between different layers but also to the cooling process. To achieve a glass state the melt, has to be cooled rapidly to avoid crystallization of the material and then annealed to remove cooling induced stress. In 3D-printing of glass the objects are shaped at room temperature and then fired. The material properties of the final objects are crucially dependent on the frit size of the glass powder used during shaping, the chemical formula of the binder and the firing procedure. For frit sizes below 250 µm, we seem to find a constant volume of pores of less than 5%. Decreasing frit size leads to an increase in the number of pores which then leads to an increase of opacity. The two different binders, 2hydroxyethyl cellulose and carboxymethylcellulose sodium salt, generate very different porosities. The porosity of samples with 2-hydroxyethyl cellulose is similar to frit-only samples, whereas carboxymethylcellulose sodium salt creates a glass foam. The surface finish is determined by the material the glass comes into contact with during firing.

Two main concerns for designing a wireless system are more network capacity and less energy consumption. Recently, distributed antenna system (DAS) has received considerable attention due to its potential to provide higher spectral efficiency (SE) and uniform coverage for cellular networks. In this regard, this paper compares the performance of DAS with centralized antenna system (CAS) in LTE-A system in terms of energy efficiency (EE), where practical restrictions such as out-of-cell interference, path loss, and small scale fading are taken into account. Furthermore, the EE and system power consumption are investigated under three different cell-load scenarios (high, moderate, and low load) where different numbers of antennas are activated and remaining of antennas are under sleep mode. Finally, based on the tradeoff between power-saving and EE, two optimal DAS antenna deployments are proposed for low and moderate cell-load scenarios. The results reveal that DAS considerably outper...

Energy efficiency in cellular networks has received significant attention from both academia and industry because of the importance of reducing the operational expenditures and maintaining the profitability of cellular networks, in addition to making these networks “greener.” Because the base station is the primary energy consumer in the network, efforts have been made to study base station energy consumption and to find ways to improve energy efficiency. In this paper, we present a brief review of the techniques that have been used recently to improve energy efficiency, such as energy-efficient power amplifier techniques, time-domain techniques, cell switching, management of the physical layer through multiple-input multiple-output (MIMO) management, heterogeneous network architectures based on Micro-Pico-Femtocells, cell zooming, and relay techniques. In addition, this paper discusses the advantages and disadvantages of each technique to contribute to a better understanding of eac...

This paper describes our preliminary steps towards the deployment of a brand-new original feature for a telemedicine portal aimed at helping people suffering from diabetes. In fact, people with diabetes necessitate careful handling of their disease to stay healthy. As such a disease is correlated to a malfunction of the pancreas that produces very little or no insulin, a way to enhance the quality of life of these subjects is to implement an artificial pancreas able to inject an insulin bolus when needed. The goal of this paper is to extrapolate a regression model, capable of estimating the blood glucose (BG) through interstitial glucose (IG) measurements, that represents a possible revolutionizing step in constructing the fundamental element of such an artificial pancreas. In particular, a new evolutionary approach is illustrated to stem a mathematical relationship between BG and IG. To accomplish the task, an automatic evolutionary procedure is also devised to estimate the missing BG values within the investigated real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other models during the experimental phase on global and personalized data treatment. Moreover, investigation is performed about the accuracy of one single global relationship model for all the subjects involved in the study, as opposed to that obtained through a personalized model found for each of them. Once this research is clinically validated, the important feature of estimating BG will be added to a web portal for diabetic subjects for telemedicine purposes.

Gamification refers to the use of gaming elements to enhance user experience and engagement in non-gaming systems. In this paper we report the design and implementation of two higher education courses in which ludic elements were used to enhance the quality of the learning experience. A game can be regarded as a system of organised gameplay activities, and a course can be regarded as a system of organised learning activities. Leveraging this analogy, analysing games can provide valuable insights to organise learning activities within a learning experience. We examined a sample of successful commercial games to identify patterns of organisation of gameplay activities that could be applied to a course design. Five patterns were identified: quest structure, strategic open-endedness, non-linear progression, orientation, and challenge-based reward. These patterns were then used to define the instructional design of the courses. As a result, courses were organised as systems of quests tha...

Smart industrial processes are impossible without the use of machinery. Majority of these machines comprise rotating machines. As supporting tools for developing maintenance strategies, a Data-driven approach for multifault diagnosis of Rotating Machines is currently used in smart industry. This Chapter presents Multisensor Data Fusion and its application in Predictive maintenance methodology for multifault diagnosis of Industrial Rotating Machines. The main objective of Multisensor Data Fusion, rather than the different sensors being applied independently, are optimally fused to take advantage of their respective strengths, by combining data from multiple sensors and related information to achieve specific inferences than could be achieved by a single sensor. This chapter presents a methodology using data extraction features domain parameters for multifault diagnosis of Rotating Machines. The analysis shows that Root Mean Square Amplitude increases as fault develops, Crest Factor is more for healthy bearing and less for faulty bearing, these can provide early warning of faults

Internet is widely depends on the IP over Ethernet networks architecture. IP and Ethernet protocols uses in each Local Area Network LAN, wire and wireless. Due to the rapid expansion of the technology field this architecture reveals many shortcomings. TCP/IP suite protocols are consists from layers, each layer accomplish its jobs separately. However, these layers susceptible to different attacks. Data link layer one of the most layers targeted by the attackers. Attack at lower layer may lead to more sophisticated attacks to upper layers, like Man-in-The-Middle (MiTM), DNS spoofing and Denial of Service (DoS). These attacks applicable even with encrypted protocols such as HTTPS and SSL. In this paper we discuss the security in the Data Link Layer in IP over Ethernet networks and the attacks depend on the Address resolution protocol (ARP). Moreover, explain our proposed method to prevent address resolution protocol attacks.

How to process the big data generated in large IoT networks is still challenging current techniques. To date, a lot of network clustering approaches have been proposed to improve the performance of data aggregation in IoT. However, most of them focus on partitioning networks with static topologies, and thus they are not optimal on handling the case with moving objects in the networks. Moreover, as the best of knowledge, none of them has ever considered the performance of following computing in edge servers. To improve these problems, in this work, we propose a highly efficient IoT network dynamic clustering solution in edge computing using deep reinforcement learning (DRL). Our approach can both fulfill the data communication requirements from IoT networks and load-balancing requirements from edge servers, and thus provide a great opportunity for future high performance IoT data analytics. We implement our approach by Deep Q-learning Network (DQN) model, and our preliminary experimental results show that the DQN solution can achieve higher score in cluster partitioning compared with the current static benchmark solution.

PurposeCollaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively.Design/methodology/approachA collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity in...

WorkWays is a science gateway that supports human-in-the-loop scientific workflows. Human-workflow interactions are enabled by a dynamic Input Output (IO) model, which allows users to insert data into, or export data out of, a continuously running workflow. WorkWays has been used to solve a number of scientific problems where the user wishes to examine intermediate results in order to interact with the computation as the workflow progresses. This interactive capability not only provides better insights into the computation but also allows users to focus on different input parameter combinations. We have implemented a variety of data types and modes of interaction to account for a wide range of use cases and application domains. This paper demonstrates the applicability of WorkWays on three use cases from different domains.

We present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit...

In the last decade Peer to Peer technology has been thoroughly explored, becauseit overcomes many limitations compared to the traditional client server paradigm. Despite its advantages over a traditional approach, the ubiquitous availability of high speed, high bandwidth and low latency networks has supported the traditional client-server paradigm. Recently, however, the surge of streaming services has spawned renewed interest in Peer to Peer technologies. In addition, services like geolocation databases and browser technologies like Web-RTC make a hybrid approach attractive. In this paper we present algorithms for the construction and the maintenance of a hybrid P2P overlay multicast tree based on topological distances. The essential idea of these algorithms is to build a multicast tree by choosing neighbours close to each other. The topological distances can be easily obtained by the browser using the geolocation API. Thus the implementation of algorithms can be done web-based in a distributed manner. We present proofs of our algorithms as well as experimental results and evaluations.

The control of communication networks is an important aspect from both the service provider and user points of view. There are several approaches to communication network control including game theory, genetic algorithms and Markov decision processes. Data mining methods have been successfully used to discover optimized solutions to this problem, and have the capability to learn the network behavior under different network conditions and during operation so that complete knowledge of the network behavior is not required a priori. This article identifies the concepts behind the idea of using data mining for communication network control, provides a structured survey of the results in this area, and discusses the guidelines for future applications.

Mobile Ad hoc Networks (MANETs) are very considered attractive for many applications. Routing protocol is considered as the most important element of MANET. However, media streaming over MANET is a quite demanding task. In this paper the performances of MANET routing protocols have been investigated for video applications. Some popular routing protocols namely Dynamic Source Routing (DSR), Ad hoc On-demand Distance Vector (AODV), Temporally-Ordered Routing Algorithm (TORA), Optimized Link State Routing Protocol (OLSR), and Geographic Routing Protocol (GRP) have been considered in this paper. A comparative performance analysis of these routing protocols has been presented in this paper for supporting video streaming applications.

Online recruitment fraud (ORF) is a new challenge in the cyber security area. In ORF, scammers give job seekers lucrative job offers and in-return steal their money and personal information. In India, scammers have stolen millions of moneys from innocent job seekers. Hence, it is important to find solution to this problem. In this paper, we propose, ORFDetector, an ensemble learning based model for ORF detection. We test the proposed model on publicly available dataset of 17,860 annotated jobs. The proposed model is found to be effective and give average f1-score and accuracy of 94% and 95.4, respectively. Additionally, it increases the specificity by 8% as compared to the baseline classifiers.

Spectrum congestion is a major concern in both military and commercial wireless networks. To support growing demand for ubiquitous spectrum usage, Cognitive Radio is a new paradigm in wireless communication that can be used to exploit unused part of the spectrum by dynamically adjusting its operating parameters. While cognitive radio technology is a promising solution to the spectral congestion problem, efficient methods for detecting white spaces in wideband radio spectrum remain a challenge. Conventional methods of detection are forced to use the high sampling rate requirement of Nyquist criterion. In this paper, the feasibility and efficacy of using compressive sensing (CS) algorithms in conjunction with Haar wavelet for identifying spectrum holes in the wideband spectrum is explored. Compressive sensing is an emerging theory that shows that it's possible to achieve good reconstruction, at sampling rates lower than that specified by Nyquist. CS approach is robust in AWGN and fading channel.

This paper proposes through a case study for a college, described as Faculty Barddal, evaluation of wireless network characteristics between the real and nominal. Since the growing need in pursuit of excellence in the operation of wireless Faculty Barddal needed to migrate its network of wireless Internet for a recent technology being able to add value with new services and improvements in data transmission. The technology used is the IEEE 802.11g / n, enabling innovation and deployment of new features, previously not possible due to limitations of the old Wi-fi. The network IEEE 802.11g / n for Faculty Barddal described in detail and functionality deployed contributes to understanding the use and benefit from this new technology. This trend is growing in Brazil and the world, being attentive to business needs and migrate your network to a modern technology that allows more competitive market with low cost.

Analytic tools are beginning to be largely employed, given their ability to rank, e.g., the visibility of social media users. Visibility that, in turns, can have a monetary value, since social media popular people usually either anticipate or establish trends that could impact the real world (at least, from a consumer point of view). The above rationale has fostered the flourishing of private companies providing statistical results for social media analysis. These results have been accepted, and largely diffused, by media without any apparent scrutiny, while Academia has moderately focused its attention on this phenomenon. In this paper, we provide evidence that analytic results provided by field-flagship companies are questionable (at least). In particular, we focus on Twitter and its "fake followers". We survey popular Twitter analytics that count the fake followers of some target account. We perform a series of experiments aimed at verifying the trustworthiness of their results. We compare the results of such tools with a machine-learning classifier whose methodology bases on scientific basis and on a sound sampling scheme. The findings of this work call for a serious re-thinking of the methodology currently used by companies providing analytic results, whose present deliveries seem to lack on any reliability.

This paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal snapshot; ii) recursive signal reconstruction from a stream of noisy measurements. For both strategies, a mean square error analysis is performed to highlight the role played by the filter response and the sampled nodes, and to propose a graph sampling strategy. Our findings are validated with numerical results, which illustrate the potential of the proposed algorithms for distributed reconstruction of graph signals.

With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorize historical patterns through user's trajectories for recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences the model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each checkin sequence and the decoder predicts the possible missing checkins based on the encoded information. In order to learn timeaware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two realworld check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.

With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorize historical patterns through user's trajectories for recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences the model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each checkin sequence and the decoder predicts the possible missing checkins based on the encoded information. In order to learn timeaware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two realworld check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.

We present a chronological evolution for naval telecommunication networks. We distinguish periods: with or without multiplexers, with switch systems, with federative systems, with medium switching, and with medium switching with wireless networks. This highlights the introduction of new layers and technology in the architecture. These architectures are presented using layer models of transmission, in a unified way, which enables us to integrate pre-existing models. A ship of a naval fleet has internal communications (i.e. applications' networks of the edge) and external communications (i.e. the use of the means of transmission between edges). We propose architectures, deduced from the layer model, which are the point of convergence between the networks on board and the HF, UHF radio, and satellite resources. This modelling allows to consider end-to-end naval communications, and in a more global way, that is from the user on board towards the user on shore, including transmission...

A distributed control algorithm is proposed to manage the electrical power demand for the purpose of charging electric vehicles so that (a) the overall power demand remains within the limitations of the distribution network and (b) each vehicle obtains a sufficiently charged battery at the end of the charging cycle. The charging algorithm utilises only local measurements to determine the charge current. The control problem is modelled as a noncooperative game with weakly coupled cost functions for each vehicle. The cost function for each vehicle consist of an individual cost term and a group cost term. The group cost term expresses the aggregated demand of all vehicles and serves the purpose of ensuring that the infrastructure capacity constraints are respected. It is shown that this term can be estimated from local voltage measurements. The individual cost term reflects the need to achieve a desired charge level in the battery. Sufficient conditions for the overall system to admit a unique Nash Equilibrium are identified. Convergence and stability properties for a particular greedy algorithm implementation are described. To illustrate the efficacy of the proposed charging methodology, the algorithm is simulated in the context of an Australian suburban low voltage electricity distribution network.

Aesthetic accounts of interaction design (Löwgren 2008, Wright et al 2008) acknowledge the importance of the descriptive and dialogic roles that design artefacts play. Yet, much of the focus in this aesthetic turn (Udsen 2005) concerns final designs, or products of the design project. Ephemeral artefacts that are produced in the course of these projects or the design actions by those who created the artefacts inside projects are often omitted and rarely discussed. This paper critically reflects on a project to shed some light on the 'secret life of artifacts' and the role they play through making and using by the project team.

Mobile Ad hoc Networks (MANETs) are very considered attractive for many applications. Routing protocol is considered as the most important element of MANET. However, media streaming over MANET is a quite demanding task. In this paper the performances of MANET routing protocols have been investigated for video applications. Some popular routing protocols namely Dynamic Source Routing (DSR), Ad hoc On-demand Distance Vector (AODV), Temporally-Ordered Routing Algorithm (TORA), Optimized Link State Routing Protocol (OLSR), and Geographic Routing Protocol (GRP) have been considered in this paper. A comparative performance analysis of these routing protocols has been presented in this paper for supporting video streaming applications.

Queueing models with batch service have been studied frequently, for instance in the domain of telecommunications or manufacturing. Although the batch server's capacity may be variable in practice, only a few authors have included variable capacity in their models. We analyse a batch server with multiple customer classes and a variable service capacity that depends on both the number of waiting customers and their classes. The service times are generally distributed and class-dependent. These features complicate the analysis in a non-trivial way. We tackle it by examining the system state at embedded points, and studying the resulting Markov Chain. We first establish the joint probability generating function (pgf) of the service capacity and the number of customers left behind in the queue immediately after service initiation epochs. From this joint pgf, we extract the pgf for the number of customers in the queue and in the system respectively at service initiation epochs and departure epochs, and the pgf of the actual server capacity. Combined with additional techniques, we also obtain the pgf of the queue and system content at customer arrival epochs and random slot boundaries, and the pgf of the delay of a random customer. In the numerical experiments, we focus on the impact of correlation between the classes of consecutive customers, and on the influence of different service time distributions on the system performance.

WSNs (Wireless Sensor Networks) introduces significant challenges which do not arise in static WSNs. Reliable data transport is an important aspect of attaining consistency and QoS (Quality of Service) in several applications of MWSNs (Mobile Wireless Sensor Networks). It is important to understand how each of the wireless sensor networking characteristics such as duty cycling, collisions, contention and mobility affects the reliability of data transfer. If reliability is not managed well, the MWSN can suffer from overheads which reduce its applicability in the real world. In this paper, reliability assessment is being studied by deploying MWSN in different indoor and outdoor scenarios with various duty cycles of the motes and speeds of the mobile mote. Results show that the reliability is greatly affected by the duty cycled motes and the mobility using inherent broadcast mechanisms.

Playfulness, with non-intrusive elements, can be considered a useful resource for enhancing social awareness and community building within work organizations. Taking inspirations from the cultural probes approach, we developed organizational probes as a set of investigation tools that could provide useful information about employees' everyday playful experiences within their work organizations. In an academic work environment, we applied our organizational probes over a period of three weeks. Based on the collected data we developed two design concepts for playful technologies in work environments.

With the explosive growth of multitudinous wireless services and high-data-rate required applications, relay aided Device-to-Device (D2D) communications have attracted increasing attentions. In this paper, we investigate the performance trade-off in relay aided D2D-cellular networks, where D2D users reuse the resource of cellular uplink transmissions. To optimize the performance of relay aided D2D communications, we firstly study the trade-off between spectral efficiency (SE) and energy efficiency, and propose a source-relay joint power allocation scheme. The optimal transmit powers for both source and relay users are derived, based on which we further introduce a SEvariation function to explicitly evaluate the impact of enabling D2D communications on D2D-cellular networks. Theoretical analysis and simulation results demonstrate the viability of the proposed scheme and provide comprehensive insights into the coexistence of different systems.

Recent analysis by manufacturers and network operators has shown that current wireless networks are not very energy efficient, particularly the base stations by which terminals access services from the network. In response to this observation the Mobile Virtual Centre of Excellence (VCE) Green Radio project was established in 2009 to establish how significant energy savings may be obtained in future wireless systems. This article discusses the technical background to the project and discusses models of current energy consumption in base station devices. It also describes some of the most promising research directions in reducing the energy consumption of future base stations.