Este documento lista y describe las herramientas comúnmente utilizadas para el mantenimiento de equipos de computo. Identifica 18 herramientas diferentes como destornilladores de varios tipos, alicates, cortafríos, crimpeadores, ponchadoras, multímetros, extractores de soldadura, manillas antiestáticas, cautines para soldar, sopladores, probadores de red y otras. Para cada herramienta se proporciona una breve descripción de su función principal en el mantenimiento de hardware.
Design patterns are acknowledged as powerful conceptual tools to improve design quality and to reduce the time and cost of design
by effect of the reuse of “good” solutions. In many fields such as software engineering, web engineering, and interface design,
patterns are widely used by practitioners and are also investigated from a research perspective. Still, the concept of design pattern
has received marginal attention in the arena of user interfaces (UIs) for Recommender Systems (RSs). To our knowledge, a little
is known about the use of patterns in this specific class of applications, in spite of their increasing popularity, and no RS
specific interface pattern is available in existing pattern languages. We have performed a systematic analysis of 28 real-world RSs in
a variety of sectors, in order to: (i) discover occurrences of existing general (i.e., domain independent) UI patterns; (ii)
identify recurrent UI design solutions for RS specific features; (iii) elicit a set of new UI patterns for RS interfaces. The analysis
of patterns occurrences highlights the degree at which “good” UI design solutions are adopted in RSs for the different sectors. The
new patterns can be used by UI designers of RSs to improve the UX of their systems.
User Personality and the New User Problem in a Context-Aware Point of Interes...University of Bergen
The new user problem is an important and challenging issue that Context-Aware Recommender Systems (CARSs) must deal with, especially in the early stage of their deployment. It occurs when a new user is added to the system and there is not enough information about the user’s preferences in order to compute appropriate recommendations. It is common to address this problem in the recommendation algorithm, by using demographic attributes such as age, gender, and occupation, which are easy to collect and are reasonably good predictors of the user preferences. However, as we show here, user’s personality provides even better information for generating context-aware recommendations for places of interest (POI), and it is still easy to assess with a simple questionnaire. In our study, using a rating data set collected by a mobile app called STS (South Tyrol Suggests), we have found that by considering the user personality the system can better rank the recommendations for the new users.
Empirical Evaluation of Active Learning in Recommender SystemsUniversity of Bergen
The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor quality data during training. Active learning aims to remedy this problem by focusing on obtaining better quality data that more aptly reflects a user’s preferences. In attempt to do that, an active learning strategy selects the best items to be presented to the user in order to acquire her ratings and hence improve the output of the RS.
In this seminar, I present a set of active learning strategies with different characteristics and the evaluation results with respect to several evaluation measures (i.e., MAE, NDCG, Precision, Coverage, Recommendation Quality, and, Quantity of the acquired ratings and contextual conditions).
The traditional evaluation of active learning strategies has two major flaws: (1) Performance has been evaluated for each user independently (ignoring system-wide improvements) (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition). Addressing these flaws, I present that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system-centric). Hence, I present a novel offline evaluation methodology and use it to evaluate some novel and state of the art rating elicitation strategies.
While the first set of experiments was done offline, the true value of active learning must be evaluated in an online setting. Hence, in the second part of the seminar, I present a novel active learning approach that exploits some additional information of the user (i.e. the user’s personality) to deal with the cold start problem in an up-and-running mobile context-aware RS called STS, that provides users with recommendations for places of interest (POIs). The results of live user studies, have shown that the proposed AL approach significantly increases the quantity of the ratings and contextual conditions acquired from the user as well as the recommendation accuracy.
Active Learning in Collaborative Filtering Recommender Systems : a SurveyUniversity of Bergen
In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.
Toward Building a Content based Video Recommendation System Based on Low-leve...University of Bergen
In this presentation, I briefly discuss the use of automatically extracted visual features of videos in the context of recommender systems that brings some novel contributions in the domain of video recommendations. The proposed content-based recommender system encompasses a technique to automatically analyze video contents and to extract a set of representative stylistic features (lighting, color, and motion) grounded on existing approaches of Applied Media Theory.
Proposed recommender can be used in combination with more traditional content-based recommendation techniques that exploit explicit content features associated to video files, in order to improve the accuracy of recommendations. Proposed recommender can also be used alone, to address the problem originated from video files that have no meta-data, a typical situation of popular movie-sharing websites (e.g., YouTube) where every day hundred millions of hours of videos are uploaded by users and may contain no associated information. As they lack explicit content, these items cannot be considered for recommendation purposes by conventional content-based techniques even when they could be relevant for the user.
Un libro sin recetas, para la maestra y el maestro Fase 3.pdfsandradianelly
Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestra y el maestro Fase 3Un libro sin recetas, para la maestr
1. APELLIDOSY NOMBRES: HeidyYulianaCastañoFranco
GRADO: 10-3
HERRAMIENTAS PARA EL MANTENIMIENTO DE LOS EQUIPOS DE COMPUTO
Identifique cada Herramienta que se muestra en la foto y escriba en la columna
correspondiente el nombre de dicha herramienta y para que se utiliza.
Puede apoyarse en estos links y otros recursos que encuentres en la web
https://sites.google.com/site/mantenimientodelhardwaredelpc/herramientas-para-el-
mantenimiento-del-pc
http://jacoma6321.blogspot.com/p/las-principales-herramientas-para.html
Foto Nombre Función
1. Destornillador
hexagonal.
2. Destornillador de
pala.
3. Destornillador de
estrella.
4. destornillador torx.
1). Se utiliza para
ajustas etas de el
mismo modo.
2). Apretar y
desapretar
Tornillos.
3). se utiliza para
apretar y aflojar
tornillos que
requieren poca
fuerza de apriete.
4). Es un
destornillador de 6
puntas que se
utiliza para apretar
y desaflojar
tornillos.
2. 1.PINZA
2. ALICATE
3.CORTAFRIO
1). Es para doblar
alambre y formar
muelles de
alambre; de pico
largo, sea de pato
o de nariz plana.
2).Cortar distintos
tipos de cable.
3). Se utilizan de
forma continuada
hay que poner
una protección
anular para
proteger la mano
que las sujeta
cuando se golpea.
CRIMPEADOR
Se desplaza con
frecuencia y exige
resultados de
calidad, esta
herramienta de
crimpado de gran
versatilidad
cumplirá la
inmensa mayoría
de sus
necesidades de
crimpado
PONCHADORA
DE IMPACTO
Una ponchadora
de impacto es una
herramienta de
punción con carga
de resorte
utilizado para
empujar los hilos
entre los pins de
metal, permitiendo
pelar al mismo
3. tiempo el
revestimiento del
cable.
MULTÍMETRO
La posición del
mando sirve para
medir intensidad y
además mide la
carga de pilas de
diferentes tipos.
EXTRACTOR
DE
SOLDADURA
Esta herramienta
no necesita
baterías ni
alimentación
eléctrica alguna,
es completamente
manual y el único
mantenimiento
que necesita es
limpieza general
cada mes
aproximadamente.
MANILLA
ANTIESTÁTICA
Es muy
indispensable
cuando estás
reglando PC,
haciendo Network
testing o sólo
trabajando con
componentes
electrónicos
sensibles
(circuitos
integrados,
transistores, etc.)
4. CAUTÍN PARA
SOLDAR
Tener la
temperatura
adecuada para
sistemas.
SOPLADOR O
ASPIRADOR
Sirve para soplar
o aspirar estos
lugares donde
muchas veces no
alcanzamos con
nuestras manos o
utensilios de
limpieza ya que
este viene con
una práctica
boquilla de
caucho fácil de
doblar para
aquellos
incómodos y
estrechos lugares.
PROBADOR
DE RED
Para probar si la
red está bien
conectada al
dispositivo y mirar
que falla tiene.