I. Presentation du traducteur PIAFTRAD : le système PIAF, adaptation du système PIAF à la traduction, fonctionnement du traducteur.<br />II. Description des problèmes de traduction : définitions et notations de base. Problèmes liés à la structure de la machine. Problèmes liés au langage. <br />III. Evaluation : résultats, perspectives d'optimisation

No description available.

The academic and business understanding of how Business Models through Service Logic co-creates, proposes, and captures value in extensive and complex Networked Systems is at its first daylight, specifically in the context of Service Systems with their Ecologies. With the complexity emerging in the Service Economies along with the advances in Information and Communication Technology such as the Cloud and Big Data, to describe and define the business operations, units, and value propositions consequently is done by business modeling and innovation of the company to acquire a current or new capitalization strategy, control and execution. Open Business Models such as the Business Model Canvas are easily integrated in existing or new Enterprises and Service Systems, and aim to facilitate the development of private as well as public entities in adapting, accessing, and integrating operant and operand resources by the ever-so-more used Service Logic. A Service Business Model has the academia and business recognized Service-Dominant Logic (S-D Logic) as a foundation for sensemaking in complex Networked Systems and Service Economies. The authors have conducted 20 face-to-face interviews with private and public company managers at all levels, review of literature in the Business Model and Service Logic fields, and also reviewed case studies from IBM on Business Models and its Leadership, Strategy and Technology (and Services) – which is a natural extension of our Interdisciplinary and Systems Sciences studies with S-D Logic at Karlstad Business School and Karlstad University for the past four years. The author’s research, interviews and IBM’s case studies show a need for further conceptualization and sensemaking of the Value Co-Creations and Propositions in Service Eco-System settings – and also decision-making assistance for managers designing, innovating and using Service Business Models to create sustainable Ecologies. Moreover, a Leadership perspective with a systems level strategy in Service Eco-Systems through externally-faced Value Propositions with the ability to create opportunities needs to be developed through a systems thinking. Furthermore, the quality of interaction, shared information, and influence in Dyad Perspective to facilitate Triad Relationships captures value – which is facilitated by the new Service Canvas Business Model. We argue for a multiple perspective in Service Business Models to cater both partner and customer perspective with internally- and externally-faced Value Propositions to Co-Create or Capture Value – we see that this requires an objective (objectification) foundation for consensus; the 4C model. We contend that Service Eco-Systems cannot scale or sustain without the proper use of Technology specifically Communication but also Information, which determine most of the quality in modern and digital service interactions and perspectives. Our interviews, reviews, and cumulative research in Service Business Models and Eco-Systems with IBM case studies are all strong foundations for current and future research but also for business practice today.

The thesis is focused on the analysis of presently offered products supporting Business Intelligence (BI) which are affordable for small and medium-sized enterprises (SMEs). Current BI solutions available to SMEs are mostly offered via Cloud computing, specifically in the form of Software as a Service (SaaS) as it requires low initial acquisition costs. The objectives of this thesis are to analyse the work in applications for BI in cloud that can be used by SMEs and to analyse in detail the comparison the worldwide extended reporting tools distributed as SaaS in the lower price category. The theoretical part provides a description of the Cloud computing and the BI system. In the practical part are selected following products: IBM Watson Analytics, Qlik Sense Cloud, Zoho Reports, Tableau Public and Microsoft Power BI. Practical testing of these applications was based on evaluation of the selected metrics with weights calculated by using the Fuller's triangle. Analyses and the information form the basis for comparison of selected applications. The contribution of this thesis is in discovering the strengths and weaknesses of these BI solutions. The output of this thesis can be used as a source for the selection of BI applications for SMEs.

Social networks like Facebook, Twitter or LinkedIn offer the opportunity to communicate and collaborate with friends and family in our personal life through the social technology without seeing each other on daily bases. This different approach is already common to be used for the majority of people and brings new benefits to now-a-day global environment. The same users of these external social networks are working in companies with various internal collaboration and communication tools. This fact has become stimulus for the theory of using social network technologies inside of the organization called Social Business. Social Business brings multiple new benefits for the organization, which were not possible to be gain through the previous approaches of communication and collaboration. In the present work we study the process of transforming organization into Social Business.

The Standish Group Study of 1994 showed that 53 percent of software projects failed outright and another 31 percent were challenged by extreme budget and/or time overrun. Since then different responses to the high rate of software project failures have been proposed. SEI’s CMMI, the ISO’s 9001:2000 for software development, and the IEEE’s JSTD-016 are some examples of such responses. Traceability is the one common feature that these software development standards impose. Over the last decade, software and system engineering communities have been researching subjects such as developing more sophisticated tooling, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, applying traceability in specific domains such as Model Driven Development, product line systems and agile project environment. These efforts have not been in vain. The 2012 CHAOS results show an increase in project success rate of 39% (delivered on time, on budget, with required features and functions), and a decrease of 18% in the number of failures (cancelled prior to completion or delivered and never used). Since research has shown traceability can improve a project’s success rate, the main purpose of this thesis is to demonstrate traceability for a small, real-world software development project using IBM Collaborative Lifecycle Management. The objective of this research was fulfilled since the case study of traceability was described in detail as applied to the design and development of the Value Adjustment Board Project (VAB) of City of Jacksonville using the scrum development approach within the IBM Rational Collaborative Lifecycle Management Solution. The results may benefit researchers and practitioners who are looking for evidence to use the IBM CLM solution to trace artifacts in a small project.

Chat-bots are computer programs designed to conduct textual or audible conversations with a single user. The job of a chat-bot is to be able to find the best response for any request the user issues. The best response is considered to answer the question and contain relevant information while following grammatical and lexical rules. Modern chat-bots often have trouble accomplishing all these tasks. State-of-the-art approaches, such as deep learning, and large datasets help chat-bots tackle this problem better. While there is a number of different approaches that can be applied for different kind of bots, datasets of suitable size are not always available. In this work, we introduce and evaluate a method of expanding the size of datasets. This will allow chat-bots, in combination with a good learning algorithm, to achieve higher precision while handling their tasks. The expansion method uses the continual learning approach that allows the bot to expand its own dataset while holding conversations with its users. In this work we test continual learning with IBM Watson Assistant chat-bot as well as a custom case study chat-bot implementation. We conduct the testing using a smaller and a larger datasets to find out if continual learning stays effective as the dataset size increases. The results show that the more conversations the chat-bot holds, the better it gets at guessing the intent of the user. They also show that continual learning works well for larger and smaller datasets, but the effect depends on the specifics of the chat-bot implementation. While continual learning makes good results better, it also turns bad results into worse ones, thus the chat-bot should be manually calibrated should the precision of the original results, measured before the expansion, decrease.

No description available.

Cieľom práce je navrhnúť a vytvoriť hlasom ovládaného asistenta(voicebota), ktorý bude ľahko nasaditeľný na webovú stránku. Používateľom tak bude poskytnutý moderný spôsob, ako prirodzene komunikovať cez internetový prehliadač. Hlavný dôraz je kladený na synchronizáciu medzi hlasovým asistentom a obsahom na webovej stránke. Synchronizácia je dosiahnutá obojsmerným prenosom hlasu a textových príkazov medzi klientom a serverom. Na to je použitá technológia WebRTC v kombinácií so signalizačným protokolom SIP. Práca sa zaoberá oblasťami ako VoIP telefonovanie, počítačové siete a strojové učenie(proprietárne rečové technológie od Phonexie). Benefitom nasadenia hlasového asistenta je zníženie nákladov na odchádzajúce hovory pre klientov, odľahčenie agentov na call centrách pri odpovedaní na často kladené otázky a zvýšenie záujmu zákazníkov vďaka použitiu nových technológií.

In automatic speech recognition systems (ASRs), training is a critical phase to the system?s success. Communication media, either analog (such as analog landline phones) or digital (VoIP) distort the speaker?s speech signal often in very complex ways: linear distortion occurs in all channels, either in the magnitude or phase spectrum. Non-linear but time-invariant distortion will always appear in all real systems. In digital systems we also have network effects which will produce packet losses and delays and repeated packets. Finally, one cannot really assert what path a signal will take, and so having error or distortion in between is almost a certainty. The channel introduces an acoustical mismatch between the speaker's signal and the trained data in the ASR, which results in poor recognition performance. The approach so far, has been to try to undo the havoc produced by the channels, i.e. compensate for the channel's behavior. In this thesis, we try to characterize the effects of different transmission media and use that as an inexpensive and repeatable way to train ASR systems.