Increasing the Intelligence of Virtual Sales Assistants through Knowledge Modeling Techniques

Shopping agents are web-based applications that help consumers to find appropriate products in the context of e-commerce. In this paper we argue about the utility of advanced model-based techniques that recently have been proposed in the fields of Artificial Intelligence and Knowledge Engineering, in order to increase the level of support provided by this type of applications. We illustrate this approach with a virtual sales assistant that dynamically configures a product according to the needs and preferences of customers.

Modeling Commercial Knowledge to Develop Advanced Agent-based Marketplaces for E-commerce

This paper argues about the utility of advanced knowledge-based techniques to develop web-based applications that help consumers in finding products within marketplaces in e-commerce. In particular, we describe the idea of model-based approach to develop a shopping agent that dynamically configures a product according to the needs and preferences of customers. Finally, the paper summarizes the advantages provided by this approach.

Group Dynamics in Sex Offender Treatment Involving Individuals with Personality Disorders

This qualitative investigation primarily employing a phenomenological perspective and psychoanalytic interview approach intends to provide contextual understanding of group dynamics in sex offender treatment involving individuals with strong features of personality disorders or Axis II psychopathology according to the Diagnostic and Statistical Manual of Mental Disorder (4 ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000). Of note, this study particularly focuses on the cluster B type (Narcissistic, Borderline, Histrionic, and Antisocial Personality Disorders), based on the assumption that this type is more interpersonally operational in its nature. The present study is based on semi-structured interviews of three clinicians who arecurrently providing group treatment for sex offenders. The interview was designed to elicit the participants' clinical observations of group dynamics involving group members with features of the Axis II, Cluster B type. In this study, 11 therapeutic factors postulated by Yalom (2005) were utilized to qualitatively investigate group dynamics. Analyses of qualitative data highlighted how group members with features of the Axis II, Cluster B type may distinctively affect group dynamics. Based on the results, group members with Axis II diagnoses, as reported bythe therapists who responded to this study, were observed to present with altruistic behaviors in group. In addition, motivation appeared to be one of the most influential factors in promoting and maintaining therapeutic group behaviors. Group members with antisocial features appeared to present with low motivation for treatment, and individualswith a pervasive history of criminal institutionalization seemed more prone to disengagement in group. Individuals with borderline and histrionic traits seemed to be interpersonally oriented and affectively engaged in group process. Persons with a narcissistic tendency also appeared to be interpersonally invested and showed altruistic behaviors, yet the importance of confirming their superiority seemed to outweigh the need for acceptance or approval from other group members. As briefly discussed above, the qualitative analyses of the current data showed that individuals with Axis II disorders, Cluster B type uniquely affect group dynamics, which suggest clinical considerations foreffective treatment planning, maintenance, and outcomes.

Competências digitais para o trabalho na sociedade conectada: estudo de caso em uma organização pública

O objetivo deste trabalho é identificar a inserção das competências digitais no modelo de gestão de competências de uma organização pública brasileira de abrangência nacional. Utilizou-se, para tanto, de um conjunto de técnicas de investigação. Inicialmente, realizou-se a análise documental do modelo de competência da organização pública, objetivando relacioná-lo ao quadro de competências digitais proposto no âmbito do projeto DigComp, desenvolvido pela European Community e que, neste trabalho, é tomado como referência teórica para a análise dos dados coletados. A análise de conteúdo nos moldes propostos por Bardin (1977) permitiu identificar se esse documento trazia vestígios das competências digitais do framework utilizado. Em seguida, por meio do aplicativo Google Forms, um questionário estruturado foi aplicado a 27 respondentes, lotados em uma gerência estadual da organização. Os dados coletados permitiram identificar a importância atribuída pelos respondentes às competências digitais do framework e foram mais intensamente explorados em um grupo focal conduzido junto a quatro empregados que exercem a função de liderança na mesma unidade. Os resultados apontam para uma percepção da importância das competências digitais pelos empregados e líderes, mesmo que sua presença não tenha sido identificada no modelo de gestão de competência da organização estudada. Por outro lado, apontam também para a dificuldade desses empregados e líderes na aplicação dessas competências nas tarefas cotidianas do trabalho.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

Evaluating the effects of initial participation in the group therapy process

Although the effectiveness of group therapy has been highlighted, the underlying mechanisms involved in the group process have been under studied. The purpose of this study is twofold. First, the current study utilized an outcome mediation model to examine whether initial level of participation in the intervention (Control/No intervention, non-participatory, participatory) predicted change in Identity Conflict Resolution (IDCR), Personal Expressiveness (PE) and Informational Identity Style (INFO) at posttest, and Internalizing (INT) and Externalizing (EXT) behaviors at post and follow-up assessment. Secondly, the current study examined whether relationships between variables varied as a result of group differences in initial participation. The study utilized an archival sample of 234 high school students, ages 14 to 18, who participated in the Changing Lives Program of the Youth Development Project (YDP) since 2003. Structural equation modeling (SEM) was used to examine differences in direct effects as a result of initial participation on an outcome meditational model. To further analyze this model, SEM was utilized to conduct a multi-group solution to examine whether group differences based on level of initial participation in the variables^

The state of the nations:current and future developments in technology enhanced learning

Right across Europe technology is playing a vital part in enhancing learning for an increasingly diverse population of learners. Learning is increasingly flexible, social and mobile and supported by high quality multi-media resources. Institutional VLEs are seeing a shift towards open source products and these core systems are supplemented by a range of social and collaborative learning tools based on web 2.0 technologies. Learners undertaking field studies and those in the workplace are coming to expect that these off-campus experiences will also be technology-rich whether supported by institutional or user-owned devices. As well as keeping European businesses competitive, learning is seen as a means of increasing social mobility and supporting an agenda of social justice. For a number of years the EUNIS E-Learning Task Force (ELTF) has conducted snapshot surveys of e-learning across member institutions, collected case studies of good practice in e-learning see (Hayes, et al., 2009) in references, supported a group looking at the future of e-learning, and showcased the best of innovation in its e-learning Award. Now for the first time the ELTF membership has come together to undertake an analysis of developments in the member states and to assess what this might mean for the future. The group applied the techniques of World Café conversation and Scenario Thinking to develop its thoughts. The analysis is unashamedly qualitative and draws on expertise from leading universities across eight of the EUNIS member states. What emerges is interesting in terms of the common trends in developments in all of the nations and similarities in hopes and concerns about the future development of learning.

A low overhead error confinement method based on application statistical characteristics

Reliability has emerged as a critical design constraint especially in memories. Designers are going to great lengths to guarantee fault free operation of the underlying silicon by adopting redundancy-based techniques, which essentially try to detect and correct every single error. However, such techniques come at a cost of large area, power and performance overheads which making many researchers to doubt their efficiency especially for error resilient systems where 100% accuracy is not always required. In this paper, we present an alternative method focusing on the confinement of the resulting output error induced by any reliability issues. By focusing on memory faults, rather than correcting every single error the proposed method exploits the statistical characteristics of any target application and replaces any erroneous data with the best available estimate of that data. To realize the proposed method a RISC processor is augmented with custom instructions and special-purpose functional units. We apply the method on the proposed enhanced processor by studying the statistical characteristics of the various algorithms involved in a popular multimedia application. Our experimental results show that in contrast to state-of-the-art fault tolerance approaches, we are able to reduce runtime and area overhead by 71.3% and 83.3% respectively.

Digital predistortion for compensation of nonlinearities in Radio over Fiber Links

In order to cope up with the ever increasing demand for larger transmission bandwidth, Radio over Fiber technology is a very beneficial solution. These systems are expected to play a major role within future fifth generation wireless networks due to their inherent capillary distribution properties. Nonlinear compensation techniques are becoming increasingly important to improve the performance of telecommunication channels by compensating for channel nonlinearities. Indeed, significant bounds on the technology usability and performance degradation occur due to nonlinear characteristics of optical transmitter, nonlinear generation of spurious frequencies, which, in the case of RoF links exploiting Directly Modulated Lasers , has the combined effect of laser chirp and optical fiber dispersion among its prevailing causes. The purpose of the research is to analyze some of the main causes of harmonic and intermodulation distortion present in Radio over Fiber (RoF) links, and to suggest a solution to reduce their effects, through a digital predistortion technique. Predistortion is an effective and interesting solution to linearize and this allows to demonstrate that the laser’s chirp and the optical fiber’s dispersion are the main causes which generate harmonic distortion. The improvements illustrated are only theoretical, based on a feasibility point of view. The simulations performed lead to significant improvements for short and long distances of radio over fiber link lengths. The algorithm utilized for simulation has been implemented on MATLAB. The effects of chirp and fiber nonlinearity in a directly modulated fiber transmission system are investigated by simulation, and a cost effective and rather simple technique for compensating these effects is discussed. A detailed description of its functional model is given, and its attractive features both in terms of quality improvement of the received signal, and cost effectiveness of the system are illustrated.

A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.

Components and circuits for tunneling diode based high frequency sources

Terahertz (THz) technology has been generating a lot of interest because of the potential applications for systems working in this frequency range. However, to fully achieve this potential, effective and efficient ways of generating controlled signals in the terahertz range are required. Devices that exhibit negative differential resistance (NDR) in a region of their current-voltage (I-V ) characteristics have been used in circuits for the generation of radio frequency signals. Of all of these NDR devices, resonant tunneling diode (RTD) oscillators, with their ability to oscillate in the THz range are considered as one of the most promising solid-state sources for terahertz signal generation at room temperature. There are however limitations and challenges with these devices, from inherent low output power usually in the range of micro-watts (uW) for RTD oscillators when milli-watts (mW) are desired. At device level, parasitic oscillations caused by the biasing line inductance when the device is biased in the NDR region prevent accurate device characterisation, which in turn prevents device modelling for computer simulations. This thesis describes work on I-V characterisation of tunnel diode (TD) and RTD (fabricated by Dr. Jue Wang) devices, and the radio frequency (RF) characterisation and small signal modelling of RTDs. The thesis also describes the design and measurement of hybrid TD oscillators for higher output power and the design and measurement of a planar Yagi antenna (fabricated by Khalid Alharbi) for THz applications. To enable oscillation free current-voltage characterisation of tunnel diodes, a commonly employed method is the use of a suitable resistor connected across the device to make the total differential resistance in the NDR region positive. However, this approach is not without problems as the value of the resistor has to satisfy certain conditions or else bias oscillations would still be present in the NDR region of the measured I-V characteristics. This method is difficult to use for RTDs which are fabricated on wafer due to the discrepancies in designed and actual resistance values of fabricated resistors using thin film technology. In this work, using pulsed DC rather than static DC measurements during device characterisation were shown to give accurate characteristics in the NDR region without the need for a stabilisation resistor. This approach allows for direct oscillation free characterisation for devices. Experimental results show that the I-V characterisation of tunnel diodes and RTD devices free of bias oscillations in the NDR region can be made. In this work, a new power-combining topology to address the limitations of low output power of TD and RTD oscillators is presented. The design employs the use of two oscillators biased separately, but with the combined output power from both collected at a single load. Compared to previous approaches, this method keeps the frequency of oscillation of the combined oscillators the same as for one of the oscillators. Experimental results with a hybrid circuit using two tunnel diode oscillators compared with a single oscillator design with similar values shows that the coupled oscillators produce double the output RF power of the single oscillator. This topology can be scaled for higher (up to terahertz) frequencies in the future by using RTD oscillators. Finally, a broadband Yagi antenna suitable for wireless communication at terahertz frequencies is presented in this thesis. The return loss of the antenna showed that the bandwidth is larger than the measured range (140-220 GHz). A new method was used to characterise the radiation pattern of the antenna in the E-plane. This was carried out on-wafer and the measured radiation pattern showed good agreement with the simulated pattern. In summary, this work makes important contributions to the accurate characterisation and modelling of TDs and RTDs, circuit-based techniques for power combining of high frequency TD or RTD oscillators, and to antennas suitable for on chip integration with high frequency oscillators.

Microwave-assisted synthesis and local analyses of positive insertion electrodes for Li-ion batteries

Efficient energy storage holds the key to reducing waste energy and enabling the use of advanced handheld electronic devices, hydrid electric vehicles and residential energy storage. Recently, Li-ion batteries have been identified and employed as energy storage devices due to their high gravimetric and volumetric energy densities, in comparison to previous technologies. However, more research is required to enhance the efficiency of Li-ion batteries by discovering electrodes with larger electrochemical discharge capacities, while maintaining electrochemical stability. The aims of this study are to develop new microwave-assisted synthesis routes to nanostructured insertion cathodes, which harbor a greater affinity for lithium extraction and insertion than bulk materials. Subsequent to this, state-of-the-art synchrotron based techniques have been employed to understand structural and dynamic behaviour of nanostructured cathode materials during battery cell operation. In this study, microwave-assisted routes to a-LiFePO4, VO2(B), V3O7, H2V3O8 and V4O6(OH)4 have all been developed. Muon spin relaxation has shown that the presence of b-LiFePO4 has a detrimental effect on the lithium diffusion properties of a-LiFePO4, in agreement with first principles calculations. For the first time, a-LiFePO4 nanostructures have been obtained by employing a deep eutectic solvent reaction media showing near theoretical capacity (162 mAh g–1). Studies on VO2(B) have shown that the discharge capacity obtained is linked to the synthesis method. Electrochemical studies of H2V3O8 nanowires have shown outstanding discharge capacities (323 mAh g–1 at 100 mA g–1) and rate capability (180 mAh g–1 at 1 A g–1). The electrochemcial properties of V4O6(OH)4 have been investigated for the first time and show a promising discharge capacity of (180 mAh g–1). Lastly, in situ X-ray absorption spectroscopy has been utilised to track the evolution of the oxidation states in a-LiFePO4, VO2(B) and H2V3O8, and has shown these can all be observed dynamically.

Fluorescence of nitrobenzoxadiazole (NBD)-labeled lipids in model membranes is connected not to lipid mobility but to probe location

Nitrobenzoxadiazole (NBD)-labeled lipids are popular fluorescent membrane probes. However, the understanding of important aspects of the photophysics of NBD remains incomplete, including the observed shift in the emission spectrum of NBD-lipids to longer wavelengths following excitation at the red edge of the absorption spectrum (red-edge excitation shift or REES). REES of NBD-lipids in membrane environments has been previously interpreted as reflecting restricted mobility of solvent surrounding the fluorophore. However, this requires a large change in the dipole moment (Dm) of NBD upon excitation. Previous calculations of the value of Dm of NBD in the literature have been carried out using outdated semi-empirical methods, leading to conflicting values. Using up-to-date density functional theory methods, we recalculated the value of Dm and verified that it is rather small (B2 D). Fluorescence measurements confirmed that the value of REES is B16 nm for 1,2-dioleoyl-sn-glycero-3- phospho-L-serine-N-(NBD) (NBD-PS) in dioleoylphosphatidylcholine vesicles. However, the observed shift is independent of both the temperature and the presence of cholesterol and is therefore insensitive to the mobility and hydration of the membrane. Moreover, red-edge excitation leads to an increased contribution of the decay component with a shorter lifetime, whereas time-resolved emission spectra of NBD-PS displayed an atypical blue shift following excitation. This excludes restrictions to solvent relaxation as the cause of the measured REES and TRES of NBD, pointing instead to the heterogeneous transverse location of probes as the origin of these effects. The latter hypothesis was confirmed by molecular dynamics simulations, from which the calculated heterogeneity of the hydration and location of NBD correlated with the measured fluorescence lifetimes/REES. Globally, our combination of theoretical and experiment-based techniques has led to a considerably improved understanding of the photophysics of NBD and a reinterpretation of its REES in particular.

Constraint Programming-based Job Dispatching for Modern HPC Applications

A High-Performance Computing job dispatcher is a critical software that assigns the finite computing resources to submitted jobs. This resource assignment over time is known as the on-line job dispatching problem in HPC systems. The fact the problem is on-line means that solutions must be computed in real-time, and their required time cannot exceed some threshold to do not affect the normal system functioning. In addition, a job dispatcher must deal with a lot of uncertainty: submission times, the number of requested resources, and duration of jobs. Heuristic-based techniques have been broadly used in HPC systems, at the cost of achieving (sub-)optimal solutions in a short time. However, the scheduling and resource allocation components are separated, thus generates a decoupled decision that may cause a performance loss. Optimization-based techniques are less used for this problem, although they can significantly improve the performance of HPC systems at the expense of higher computation time. Nowadays, HPC systems are being used for modern applications, such as big data analytics and predictive model building, that employ, in general, many short jobs. However, this information is unknown at dispatching time, and job dispatchers need to process large numbers of them quickly while ensuring high Quality-of-Service (QoS) levels. Constraint Programming (CP) has been shown to be an effective approach to tackle job dispatching problems. However, state-of-the-art CP-based job dispatchers are unable to satisfy the challenges of on-line dispatching, such as generate dispatching decisions in a brief period and integrate current and past information of the housing system. Given the previous reasons, we propose CP-based dispatchers that are more suitable for HPC systems running modern applications, generating on-line dispatching decisions in a proper time and are able to make effective use of job duration predictions to improve QoS levels, especially for workloads dominated by short jobs.