2. Computerscience studies many objects in order to understand them better. This is the case with hardware (CPU, disks, etc.), programs, data, protocols, algorithms, networks, and so forth. As the technology develops, these objects become increasingly complex. For instance, a distributed-computing infrastructure is composed of several layers (hardware, runtime system, programming environments, applications, etc.) built on top of each other. Understanding such a system requires careful modeling of each layer and the interactions between them. Since the complexity of each layer is already extremely high, it is not feasible to build a precise model of the whole environment. In this case, experiments are necessary to isolate parts of the holistic behavior in order to understand a specific portion of the whole.
as nanorobots, comprising an outer membrane, a metabolism, and peptide-DNA to encode information. Evolutionary modelling is being used extensively in PACE, to analyse real and simulated protocell dynamics, their possible evolution, and the evolution of (potentially noisy) protocellular networks. In addition to this work, computer modelling of embryogenesis and developmental systems is becoming increasingly popular in computerscience . Should artificial cells become a reality, such models will provide a method for their genes to be programmed in order to enable the growth of larger, multicellular forms. Apart from genetic algorithms and other evolutionary algorithms that have promising potential for a variety of problems (including automatic system design for molecular nanotechnology ), another emerging technique is swarm intelligence, which is inspired by the collective intelligence in social animals such as birds, ants, fish and termites. These social animals require no leader. Their collective behaviours emerge from interactions among individuals, in a process known as self- organisation. This collective intelligence in social animals often cannot emerge from direct interaction among individuals. Instead, indirect social interaction (stigmergy) must be employed. Each individual may not be intelligent, but together they perform complex collaborative behaviours. Typical uses of swarm intelligence are to assist the study of human social behaviour by observing other social animals and to solve various optimisation problems [28,29]. There are three main types of swarm intelligence techniques: models of bird flocking, the ant colony optimisation (ACO) algorithm, and the particle swarm optimisation (PSO) algorithm. Different techniques are suitable for different problems.
Teaching computing professionals HCI concepts is important and should be included as part of computerscience courses at all levels (ACM & IEEE, 2008; ACM SIGCHI, 2009; Pow-Sang, Rusu, Zapata, & Roncagliolo, 2009; Rusu & Rusu, 2007). Usability is a core concept of HCI, and one of the most common definitions of usability that is appropriate for this concept is defined by ISO 9241-11 (2010): "the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use." Usability refers to a set of concepts such as time of execution, performance, user satisfaction, and learning facility (Abran, Khelifi, Suryn, & Seffah, 2003). Activities designed to promote usability are studied for usability engineering; these activities are carried out in the process of software development, which creates a connection between users and developers (ACM & IEEE, 2008; Cooke & Mings, 2005; Mayhew, 1999).
College life is another milestone for an individual. Students are confronted with situations different from their high school life. Situations in school combine both favorable and adverse. To whatever extent each situation is being encountered by a contributes to his or her personality. Like any other students, students in Bachelor of Science in ComputerScience (BSCS) at Eastern Samar State University (ESSU) Salcedo Campus are confronted with varying situations. Stoltz (2001) Adversity Quotient (AQ) of an individual on how to resolve such challenge and strive to overcome it so as not to affect deeply what he/she will accomplish in his/her work and towards life. He defines adversity quotient as the measure of one’s resilience and ability to persevere in the face of constant change, stress, and or AQ is simply a measure of how you respond to adversities that now comprise a typical day. Adversity ) is the science of human resilience. According to eople who successfully apply this perform optimally in the challenges, big and small that confront us each day. In fact, they not only learn from these challenges, but they also respond to them better and faster
I’m sure the ComputerScience department has come a long way since the beginnings in the early 1980s that I have described here. PCs are everywhere and now we couldn’t get along without the Internet available to us at all times. It’s interesting that when I look at a list of the current faculty I still see many of my former professors even though it’s been over 30 years since I attended my first course at Fisher. Not only is it a great place for students to attend, but it must be a great place for faculty to work. I enjoyed my time at Fisher. I left Fisher prepared to continue on my studies at the graduate level and to succeed in a competitive working environment.
My thesis shows that computerscience has created a new way to understand structure. Therefore, the practices of computerscience could be viewed as a new way of doing science. What kind of issues did computer scientists encounter when pioneering with this new way of doing science? How do these issues relate to the nature of their ‘model’? And how did they find ways to deal with those issues? Also, this new method of doing science is hard to grasp for many people. Many even contest that computerscience is a science. Somehow, we do not clearly see what computerscience is and does. The methodology of other sciences, such as biology and physics, seems much more straightforward.
This influence was initially made possible through the importance of cognitive science in HCI’s formation. Conceptually the prospect of transforming design problems into reductive computational spaces (to be addressed by scientific methods) was facilitated by the central implications of a ‘strong’ cognitive science position. Specifically this position offers an isomorphism between the computer and the human (i.e., as a cognitive object, with various input / output modalities). There are two key conflations that follow: firstly that the human is computational, or can be described with computational concepts; and secondly that the designer and therefore design itself is computational. This underlying idea in HCI has enabled the adoption process of the design space model. The practical expression of these ideas may be readily found the research outputs of many HCI conference venues like CHI and UIST, but also in those of related communities such as Ubicomp. Following Card and colleagues, it has become the approach of choice for work that evaluates novel input or output devices, but also for innovative interaction techniques for established interface forms (e.g., GUIs, touch and gestural interfaces, etc.). The ‘ideal expression’ of the scientific design space is often conducted under the broader glossed label of psychology. It characteristically involves engaging in task-oriented interface evaluations via a hypothesis-driven experimental format—being often classed as ‘usability evaluation’ . In building hypotheses and delivering their results, classic features of cognitive science theory are recruited, for example cognitive objects like memory, task load and so on. This might also include methods where the rationale is grounded in cognitive science reasoning, such as ‘think aloud’ techniques. Hypothesis testing enables an organised and systematic traversal of the design space particular to the class of device and interface under investigation [6, 7]. As part of this, cumulative, replicable, generalisable and theoretically-informed findings are delivered both to HCI as a whole but also potentially back to cognitive science as instances of applied research. It is also possible that attempts to form novel cognitive theory specific to interactive systems may result, for example Information Foraging theory presents one well-known instance of this (also note its relationship to the ACT-R cognitive architecture ).
Computerscience education teaching methods: An overview of the literature Articles from LOG IN magazine - Several articles in the computerscience educational magazine LOG IN are interesting from methodological and practical teaching standpoints. LOG IN already raised awareness of the necessity of new methods in computerscience education ten years ago (Seiffert & Koerber, 2003). Among the writings found in the LOG IN heading “Praxis & Methodik” (‘Practice & Methodology’) there are reports featuring the following teaching methods: direct instruction (Tiburski, 2010), inductive approaches (Müller, 2008), research-based learning and experiment (Müller, 2006a, 2006b, Müller, 2010; Schulz & Witten 2010), concept mapping (Ertl & Mok, 2010), discovery learning (Hromkovic, 2011), problem solving (Baumann 2007; Thiele, 2008), self-directed learning (Homberg, 2006), project teaching (Ambros, 1992; Müller 2011); simulation and modeling (Steinkamp, 2004; Bierschneider-Jakobs, 2005; Wiesner, 2008; Vollmer, 2011), and role play (Fothe, 2006; Tiburski, 2010; Baumann, 2010; Link, 2011). Attention should be drawn to another aspect in connection with LOG IN magazine; namely the context-orientation in computerscience education, a concept for the planning and arrangement of computerscience education oriented on the everyday experiences that school pupils inhabit (Diethelm, 2011; Dietz, & Oppermann, 2011; Diethelm, Koubek, & Witten, 2011).
It is now easier to see how the methods which Frege used in his search for certainty in mathematics created a system suitable for use in computerscience. What Frege is doing is in effect mechanising the process of checking the validity of a proof. If a proof is written out in the characteristic human semi-formal style, then its validity cannot be checked mechanically. One needs a skilled human mathematician to apply his or her intuition to ‘see’ whether a particular line follows from the previous ones. Once a proof has been formalised, however, it is a purely mechanically matter to check whether the proof is valid using the prescribed set of rules of inference. Thus Frege’s work can be seen as replacing the craft skills of a human mathematician with a mechanical process.4
“weak” is used in an axiomatic sense like semi-groups vs. groups, distributive lattices vs. Boolean algebras, projective vs. Eucledian geometry. These topics, perhaps due to lack of stimulating applications, have always existed as topics of peripheral interest within mathematics. The requirements of ComputerScience completely changed the situation. ComputerScience needed ideas from these topics and in turn stimulated the development within these topics by posing questions which would not have been posed otherwise. Right from the days of germination of ENIAC/ EDSAC, John von Neumann had been advocating that computers would not be just a tool for aiding science but a way of doing science. With the computing reaching a stage of robustness in terms of hardware, software and user interface by early 1970s (time around which ComputerScience germination happened in India — thanks to TIFR and IIT Kanpur) and the use of computers in science & engineering gained momentum. Ken Wilson, a Nobel Laureate in Physics, promoted an idea that simulation on computers was a way to do science and scale-up discoveries and inventions. It may be noted that Wilson’s breakthroughs were realized through computational models whose simulations produced radical understanding of phase changes in materials. In fact, he championed the promotion of computational science saying that grand challenges in science could be cracked through computers. He went on to call that computation has become a third leg of science. His promotions lead to formal streams under “Computational Sciences” and also government funding for building computers increased quite substantially leading to further technological
Mainstream computerscience research has the potential to drive ICTD innovation, while at the same time contributing to mainstream “First World” research and development efforts. There are few limits to the hardware and software systems that computerscience can bring to bear upon the seemingly-limitless problems that result from sustained community and regional under- development. The current approach – creating technologies based primarily upon our understand- ing and standpoint – perpetuates a model of ICT and Development, where we are technical experts whose talents can be used in development interventions. In contrast, creating technologies that have the potential to catalyze social change, and mapping human needs to technologies that di- rectly respond to specific development problems represents ICT for Development.
When a variable contains the address of some memory block location it is called a "pointer" because it is "pointing" at that particular block of memory. All programming languages contains variables of some kind, but only few contains pointers. That's because pointers gives direct access to physical memory locations anywhere inside the computer. With pointers it is possible to access any memory location and change the data at that location. A pointer is a variable which contains the address in memory of another variable. We can have a pointer to any variable type. The unary or monadic operator & gives the ``address of a variable''. The indirection or dereference operator * gives the ``contents of an object pointed to by a pointer''.
subay düĢ qurtar iĢlҽt lap A lot of words entered to both languages from Arabic and Persian which have same meanings. Since 1929 for Neighbors’ Turkish languages and since 1928 for Turkish a lot of foreign words have been entered mostly for new equipment and science for Neighbors’ Turkish languages foreign words and for Turkish English; Italian words. 3.3 SENTENCES In both of languages making sentences mostly are same the place of verbs nouns adverbs etc. By applying the above rules we can get the acceptable result. In following examples only we use the words started with the character “I”:Istifa ibraz iblis icraya ihtimal ihtiyaç ihdas ihya icat ihtilaf ihtar iddia irsı izdiham iskelet iskele islam isim ismet ishal iĢtah iĢgal isabet israr ıstılahıslah ıslahat itaat iade itibar itiraz itimat itina idam iftar ikamet iktidar iktisat ıtiraf iklim imam imtihan imza imkan imla intihar intikam incir insan insaf inkar icap icat ihanet.In following examples only we use the words started with the character “b”:Bağ dahçe bahçivan bakla baklava balans bamya bahis buhar baht bahtiyar bedel beden basıret batın bazı bazen bakkal bohça bakiye bela bülbül büluğ bend bünye bahar bahane bühtan basiret beyit bayat biçare bel beyhude. Here we try to give more explanations suppose The number of characters in one word is equal to n The number of rules apply at same time is equal to m If the word has any characters like “ q”, ” x”, ” ҽ” which does not exist in Turkish the word called xw words. If the word has not any characters like “q”,” x”, ҽ” which does not exist in Turkish the word called yw words. q Is constant value related to xw and yw such that almost q yw =1.5*q xw Then easily