By Carlos Ramos, Paulo Novais, Céline Ehrwein Nihan, Juan M. Corchado Rodríguez
Ambient Intelligence (AmI) is a contemporary paradigm rising from man made Intelligence (AI), the place pcs are used as proactive instruments aiding individuals with their day by day actions, making everyone’s lifestyles extra comfortable.
Another major situation of AmI originates from the human laptop interplay area and specializes in providing how one can engage with structures in a extra typical means by means of capability consumer pleasant interfaces. This box is evolving quick as will be witnessed by way of the rising common language and gesture dependent sorts of interaction.
The inclusion of computational strength and communique applied sciences in daily gadgets is turning out to be and their embedding into our environments could be as invisible as attainable. to ensure that AmI to achieve success, human interplay with computing strength and embedded structures within the atmosphere might be delicate and occur with no humans truly noticing it. the one information humans must have arises from AmI: extra safeguard, convenience and wellness, rising in a usual and inherent way.
ISAmI is the overseas Symposium on Ambient Intelligence and aiming to assemble researchers from quite a few disciplines that represent the medical box of Ambient Intelligence to give and speak about the most recent effects, new principles, tasks and classes discovered, particularly by way of software program and functions, and goals to compile researchers from a number of disciplines which are attracted to all points of this area.
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Additional resources for Ambient Intelligence - Software and Applications: 5th International Symposium on Ambient Intelligence (Advances in Intelligent Systems and Computing)
The system is adapted to my learning degree IT08. I would use the system again Learning contents and didactic potential LD01. The questions were easy to understand LD02. The questions were easy to answer LD03. The system help me to learn new things LD04. The activities support signiﬁcant learning LD05. The feedback provided by the agent improves learning LD06. The system encourages continuing learning after errors LD07. The system made me appreciate my skills for learning English Table 2. Results of the evaluation of the system IT01 IT02 IT03 IT04 IT05 IT06 IT07 IT08 LD01 LD02 LD03 LD04 LD05 LD06 LD07 Min / max Average Std.
Every node has an outdegree equal to 2: edges with weight 1 denote the ﬁrst option of the girl, and edges with weight 2 denote her second option. We could assume that a girl has some ability to inﬂuence another one which has chosen her as a partner. Figure 2 (on right) shows the corresponding network of this inﬂuence game, reversing each arc (i, j) by (j, i), so that a node points to another when the ﬁrst one has some inﬂuence over the second one. Further, the weights of the edges must be exchanged, so that an original edge (i, j) with weight 1 now becomes in an edge (j, i) with weight 2, and viceversa.
2. 2 Component Agent (Filtering and Translation Phase) The functions of the Component Agent are as follows; It gathers the information provided by the Monitoring Agent. It filters error context from normal log context File (as shown in Fig 3). As can be seen, the error context is filtered by means of designated keywords, such as “not”, “error”, “reject”, “notify”, etc. It also translates the filtered error context into the CBE format. Using the syslog service existing in the operating system, as shown in Table1, it classifies the Error Event, and sets up the priorities.