SPSS 的全稱是：Statistical Program for SOCIAL Sciences，即社會(huì)科學(xué)統(tǒng)計(jì)程序。該軟件
是公認(rèn)的最優(yōu)秀的統(tǒng)計(jì)分析軟件包之一。SPSS 原是為大型計(jì)算機(jī)開(kāi)發(fā)的，其版本為SPSSx，80 年代
初，微機(jī)開(kāi)始普及以后，它率先推出了微機(jī)版本（版本為SPSS/PC+ x.x），占領(lǐng)了微機(jī)市場(chǎng)，大大
地?cái)U(kuò)大了自己的用戶量，我國(guó)目前正在使用的用戶中，絕大部分是使用3.0—4.0 版本。
PSO’s precursor was a simulator of SOCIAL behavior, that was used to visualize
the movement of a birds’ flock. Several versions of the simulation model
were developed, incorporating concepts such as nearest-neighbor velocity
matching and acceleration by distance
When digital media is perceived only as a tool to deliver content the potential for
using its affordances to explore meaning is lost. Rather than seeing media only as
an access point, we can view it as a way to enhance the expressiveness of content.
Today blogs, wikis, messaging, mash-ups, and SOCIAL media (Facebook, Twitter,
YouTube and others) offer authors ways to create narrative meaning that refl ects
our new media culture. We can look to the past for similarities and parallels to
better understand how to use SOCIAL media as a creative tool with which to
dialogue, collaborate, and create interactive narratives.
I believe that technology has the capacity to fundamentally improve people’s lives, and
improve the world in which we live.We are now two years into what my company have
called the ‘Digital Decade’.We think that by 2010 a combination of hardware and software
innovation with broader SOCIAL trends will change the way computing fits into our society.
Mobile technology is a central part of this vision.
Swarm intelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
stochastic search and optimization tool. Changes in the
PSO parameters, namely the inertia weight and the
cognitive and SOCIAL acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO parameters.
Swarm intelligence is an innovative computational way to solving hard problems.
This discipline is inspired by the behavior of SOCIAL insects such as fish
schools and bird flocks and colonies of ants, termites, bees and wasps. In general,
this is done by mimicking the behavior of the biological creatures within
their swarms and colonies.
Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and
Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The
PSO is a population based search algorithm based on the simulation of the SOCIAL behavior of
birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the
graceful and unpredictable choreography of a bird folk. Each individual within the swarm is
represented by a vector in multidimensional search space.
The status quo of digital cities is analysed in detail, using six different
perspectives:
1. SOCIAL interaction
2. Safety
3. Data management and analytics
4. Mobility
5. Consumer Lifestyle
6. Crowd-based services
