Sucker Punch Gameplay Engineer Questions

Sucker Punch Gameplay Engineer Questions
Sucker Punch Gameplay Engineer Questions
Question 1
Imagine that you're designing a new attack for an enemy in a melee–focused third–person action
game. Just to choose a random example, let's say it's a Minotaur in a God of War–style game. What
factors do you consider when designing the attack, and how do you prioritize these factors?
Basic factors about the attack:
1. Area: The area in which the attack could potentially fall upon
2. Duration: The duration of the attack
3. Frequency: How often this attack is used
4. Range: The maximum distance the attack can reach
5. Size: The area of effect of the attack
6. Damage: Damage per attack
7. Effects: Extra properties of attack, like poison or slow effect
8. Cooldown: How long does it take to ... Show more content on Helpwriting.net ...
For example, the attack speed will be compared to player's movement speed, and attack area will be
compared to the size of the fighting room.
Among these factors, the most important one should be Dogdgeability. Like in the famous action
game Dark Souls, bosses and enemies attack damage is usually quite high and cause players to die a
lot. However, the game still makes players think it is fair, because a lot of attacks are dodgeable by
skillful players. If the attack cannot be dodged systematically, we will be limited when adjusting
other factors of the attack, because the players will always receive damage from the attack. As a
result, the attack cannot be too strong, and the game will be less exciting. On the other hand, if the
attack can be dogdged, we can make it as strong as we like and with all kinds of effects. This
increases the feeling of achievement when player successfully dodged it.
After we design how it could be dodged, we can measure by playtesting how difficult it is to dodge
the attack. After that, we can assign the damage inversely proportional to the rate of successful
dodge. In summary, we could prioritize the factors by groups as following:
(Dodgeability: Area, Duration, Range, Size, Subattack Counts, Preparation, Room Coverage,
Smartness, Movement) > (Frequency, Damage, Effects, Cooldown, Animation) > (Properties,
Environment Destruction)
We could further prioritize the factors in dodgeability group as follows.
First we could decide (Area,
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Examples Of Teaching-Learning Based Optimization
CHAPTER 3
TEACHERING–LEARNING BASED OPTIMIZATION (TLBO)
3.1 Teaching –Learning Based Optimization: An overview
Teaching –learning–based optimization (TLBO) is a newly introduced by R.V. Rao in NIT–surat. It
is an evolutionary optimization algorithm that inspired from teaching –learning phenomenon of a
classroom. It is a novel population based algorithm with faster convergence speed without any
algorithm – specific parameters. In TLBO each potential problem is treated as a learner with certain
knowledge. The TLBO consist of two phases Teacher phase and Learner phase, in both phases
learner improves their knowledge from teacher (in teacher phase) and also from classmates (in
learner phase).
In section 3.2 Teacher–learning based optimization is described, Section 3.3 discusses the Teacher–
learning based optimization, Section 3.4 describes the flow of Teacher–learning based optimization
with the help of Flow Chart and finally section 3.5 concluded the chapter.
3.2 Teaching–Learning Based Optimization ... Show more content on Helpwriting.net ...
Inspired by the teaching – learning process in the classroom. It is a population –based evolutionary
computer algorithm that modeled on transferring knowledge in the classroom and use student result
to proceed on global solution. TLBO does not need any specific parameters; it only requires
common controlling parameters like population size and number of generations, so it is called
parameter less optimization algorithm. TLBO is divided into two phases: 'Teacher Phase' and
'Learner Phase'. In Teacher phase learners gain knowledge from teacher and then Learner also gain
knowledge from their classmates by mutual interaction, group–discussion etc. in 'Learning

Comparison Of Mean Square Error ( FFBP )
comparison of mean square error using feed forward back propagation (FFBP) and radial basis
function(RBF) neural network algorithms are given in table 5.2 for analysis of band pass FIR filter
with hanning window.
Table 5.2 Comparison of mean square error using FFBP and RBF neural network algorithms used
for cut off frequency calculation of band pass FIR digital filter with hanning window
Test input
(Filter
Coefficient) hanning
Window (actual cut off
Frequency) Output of Artificial Neural Network
(calculated cut off frequency) Mean Square Error
(MSE)
FFBP RBF FFBP RBF h(n) fc1 fc2 fc1 fc2 fc1 fc2 fc1 fc2 fc1 fc2 h1(n) .155 .355 .15528 .35528
.155 .355 .0000000784 .0000000784 0 0 h2(n) .215 .415 .21459 .41459 .215 .415 ... Show more
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Table 5.5 Comparison of mean square error using FFBP and RBF neural network algorithms used
for cut off frequency calculation of band pass FIR digital filter with kaiser window
Test input
(Filter
coefficient) Kaiser
Window (actual cut off
Frequency) Output of artificial neural network
(calculated cut off frequency) Mean Square Error FFBP RBF FFBP RBF h(n) fc1 fc2 fc1 fc2 fc1 fc2
fc1 fc2 fc1 fc2 h1(n) .155 .355 .15567 .35567 .155 .355 .0000004489 .0000004489 0 0 h2(n) .215
.415 .21418 .41418 .21498 .41498 .0000006724 .0000006724 .0000000004 .0000000004 h3(n) .275
.475 .27404 .47404 .27499 .47499 .0000009216 .0000009216 .0000000001 .0000000001 h4(n) .335
.535 .33475 .53475 .335 .535 .0000000625 .0000000625 0 0 h4(n) .395 .595 .39593 .59593 .395
.595 .0000008649 .0000008649 0 0 h5(n) .455 .655 .45541 .65541 .455 .655 .0000001681
.0000001681 0 0 h6(n) .515 .715 .5145 .7145 .515 .715 .00000025 00000025 0 0 h7(n) .575 .775
.57232 .77232 .575 .775 .0000071824 .0000071824 0 0 h8(n) .635 .835 .63531 .83531 .635 .835
.0000000961 .0000000961 0 0 h9(n) .695 .895 .69439 .89439 .695 .895 .0000003721 .0000003721
0 0
Table

Evaluation Of A New Hybrid Strategy
Abstract
Testing is an important process in software development and widely used by developers to reveal
bugs. Often, software failures can lead to disastrous consequences such as loss of data, fortune and
lives. Test engineers have generated a set of test cases to validate against the user requirements. The
highest number of reduction can save a cost of testing. When dealing with a large line of code
(LOCs) there still has redundancy happened. Our experiment studies about hybrid tReduct GNA_SA
and the existing strategies to counter test redundancy reduction issues. In our experiment, we apply
four (tReductLAHC, tReductSA, tReductGNA and tReduct GNA_SA) test–suite reduction
strategies for six different situation. After that, we then measure the effectiveness of the diversified
solutions of four test suite reduction strategies. Our experiment shows that a new hybrid strategy
tReduct GNA_SA offers more effectiveness to reduce redundancy compared to existing work.
Keywords: Test suite redundancy reduction. Search based software engineering. Global
Neighborhood Algorithm .Optimization . Simulated Annealing
1.0 INTRODUCTION
Software testing refers to the process of finding an error of ensuring that particular software meets
its specification. The growing complexity of software division into various application demands
highly skilled engineers with the appropriate mix of expertise. Testers need to do sampling such as
generates a set of test cases to validate against the user

My Statement Of Purpose For Computer Science
Education is a companion which no future can depress, no crime can destroy, no enemy can alienate
it and no nepotism can enslave. It is this maxim that urged me to adhere to my interests and think
higher right from my young age. Being innovative and combining it with the knowledge gained
from education has helped me face challenges in life. That is the sole reason why I intend to pursue
my Masters at this juncture of my life. Education has always been the topmost priority in my life. I
finished my schooling under Karnataka Secondary Education Examination Board and passed out
with an excellent result of 89.60%. As I was fascinated with mathematics and science concepts I
decided to take up science stream at the pre university level. I passed out with a result of ... Show
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With the advanced facilities, qualified faculty, I am confident that I will be exposed to the best
concepts and industry insights. This will help broaden my perspective and induce a sense of inquiry
in me. Coupled with genuine interest and an insatiable desire for computer software development
and programming, I am willing to dedicate myself towards the graduate program. My in depth
research on the US universities led me to (univ___name)__________ .Through various forums,
brochures , and the department website I have a definitive understanding about the courses offered
like _______________, _______________ and _________________ and undergoing research
which matches my area of interest. I have been especially impressed by the research being carried
out by renowned faculty members Dr. _____________ in _________ and Dr. ______________in
_____________. All of the above reasons have encouraged me to submit my application to your
university and get all the guidance and infrastructure to pursue my graduate studies and gain incisive

Using Aweighted Graph Of A Graph
INTRODUCTION– we define a graph as a collection of a number of vertices and edges, and each of
its edge basically connects a pair of its vertices. Whereas a tree can be defined as an acyclic graph
that is connected. The edges of a graph are assigned with some numerical valuethat may represent
the distance between the vertices, the cost or the time etc. that is why it is called aweighted graph.
An acyclicgraph that is weightedis known as a weighted tree. The minimum spanning tree (MST) in
a weighted graph is called aspanning tree. In this graph the sum of the weights of all the edges is
minimum. Multiple MST are present in a graph, but all of theseneedto have unique sum total
cost.The problem in constructing MST in an undirected, connected, weighted graph is one of the
most known classic optimization problems.Such problems can be solved by greedy or dynamic
algorithms within polynomial time.In 1926, first practical problem related to the MST was identified
by Boruvka. But now, there are several practically relatedalternatives of the MST problem that were
verified to belong to the NP–hard class. For an instance the Degree–Constrained MST problem
[2],Bounded Diameter MST (BDMST) problem framed by the researchers named: Nghia and Binh
[2], and the Capacitated Minimum SpanningTree problem [2]. Another one called the deterministic
MST problem has also been well calculated and many effective algorithms have beenintroduced by
many researchers. However, the Kruskal's algorithm and

Personal Statement: My Story With Computer Science
About Me :
My story with computer science began since I had graduated from the preparatory classes after two
years of intense undergraduate studies in mathematics and physics, I had the choice from a wide
range of engineering schools in different fields, I chose computer science for two main reasons, the
first one is that I was amazed by the stories of Steve Jobs, Mark Zuckerberg, Bill Gates.. And
inspired by those guys that were in the university, and with no money just with a computer start a
superstar company that rattles the lives of millions around the globe. And the second reason is my
love of mathematics and logics, because I think that The development of computer science is
basically mathematical. For programming language, you need automata theory and computation
theory to understand it. In data analysis and machine learning, the state of art techniques is
dominated by statistical learning theory.
So, I integrated the National school of computer science and system analysis ( ENSIAS ), the most
renowned school of computer science in Morocco. My passion for informatics was clear, I could
stay hours and hours in front of my laptop programming software and web based apps. After three
years I ... Show more content on Helpwriting.net ...
Being part of AIESEC allowed me to share my passion for international travel by transferring the
same interest and excitement I felt as a traveler to others by increasing their international exposure.
Beside this, I was also a Resident Assistant for two semesters, I was responsible for the care and co–
management of a building of 200 plus students. I gained valuable skills in planning events, being the
leader of a floor and working as a team to effectively manage resident

Importance Of Education In Computer Science
"Ultimately, education in its real sense is the pursuit of truth. It is an endless journey through
knowledge and enlightenment."
–Dr. A.P.J. Abdul Kalam.
I truly adhere to the above mentioned axiom, which made me to realize and understand the
importance of continuous learning and helped me in reaching greater heights. The inherent curiosity
for the field of computers started, right from childhood when I was able to perceive that this magical
machine possesses the potential to simplify the lives of people and empower them. Initially, to my
mind, this machine is a huge calculator, in which the advanced calculations are performed, that
cannot be done using normal calculator. This idea led me to initiate the learning about computers.
Tally is the first software which proved me that I was wrong about this intelligent machine and it is
capable of solving many complex problems that are plaguing in the society. My strong affinity
towards latest technologies, subsequent industry experience and a strong–will to reinvent myself
through my coding skills helped me to go beyond horizons. I believed a Master's Program could be
a solution for my never quenching quest for knowledge. Also, the four years worth experience in
various application developments, boundless potential in programming and interest in coding
industry drove me to make a commitment to embark a Master's Program in Computer Science.
Serious interests evolved in me to learn more about science and computers when I was in

Laona??on Modified Spider Monkey Algorithm
In 2015, K. Lenin et. al. [44] in their study "Modified Monkey Optimization Algorithm for Solving
Optimal Reactive Power Dispatch Problem" expressed that to reduce the real power loss,
modifications were required in local and global leader phase and a Modified Spider Monkey
Algorithm (MMO) was introduced. Paper also upheld that MMO is more favorable for dealing with
non–linear constraints. The algorithm was examined on the IEEE 30–bus system to minimize the
active power loss.
H. Sharma, et al. [45] in 2016, discussed in "Optimal placement and sizing of the capacitor using
Limaçon inspired spider monkey optimization algorithm" that to limit the losses in distribution and
transmission, capacitors of definite sizes are should have been ... Show more content on
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In 2016, A. Sharma et. al. [48] presented a paper "Optimal power flow analysis using Lévy flight
spider monkey optimization algorithm" in which a Lévy flight spider monkey optimization
(LFSMO) algorithm was proposed to solve the standard Optimal power flow (OPF) problem for
IEEE 30–bus system. The exploitation capacity of SMO was increased in the proposed algorithm.
LFSMO was tested over 25 benchmark functions and its performance was examined. It was found
that LFSMO gave desirable outcomes than the original SMO.
In 2017, S. Kayalvizhi et. al. [49] presented a paper "Frequency Control of Micro Grid with Wind
Perturbations using Levy Walks with Spider Monkey Optimization Algorithm." In this paper, a new
eagle strategy, which is a combination of levy flights and SMO, is utilized in the optimization of the
gains of PI controllers which helps in regulating the frequency of the micro grid. A typical micro
grid test system and a real time micro grid setup at British Columbia are the two case studies
considered, in which the frequency control is implemented. The implementation is done in two–step
search process; in the first place, levy flights do the random search and after that SMO does a
thorough local search. Results demonstrate that the proposed method outperforms the results of
other well–known algorithms and is

What Is An Algorithm?
What is an algorithm? According to the textbook, an algorithm is a set of specific sequential steps
that describe exactly what the computer program must do to complete the required work. Figure
10.4, on page 426, shows the stages that each programming project follows. Step two is where the
algorithm is, which is "making the plan." The example the book uses is the pharmaceutical
companies designing new drugs. When they design new drugs, they have to use complex computer
programs that model molecules. The computer software programs help chemists create the new drug
in a quicker manner that give the desired pharmacological effects.
Some other things that may be used in the medical field are calculators, flowcharts, look–up tables,
and nomograms (Medical Algorithm). According to the medical dictionary, an algorithm is a model
for making decisions. It shows the step–by–step process that a doctor or pharmacist would use. The
process begins with a patient describing symptoms or problems that he or she has to a doctor. A
doctor's first instinct is to gather all the information and then analyzes the symptoms or problems.
The next step is for the doctor to run a diagnostic test after gathering all of the information in order
to recommend the best solution for the patient's symptoms or problems. There are two different
ways that the diagnostic test can go. The first option is the diagnostic test will tell exactly what is
wrong with the patient and will recommend specific treatments

Complexity And Algorithms That Can Take An Impossible...
Complexity and Tractability
The area of tractability explores problems and algorithms that can take an impossible amount of
computation to solve. Tractable problems can be roughly defined as problems that can be solved in a
reasonable amount of time, while intractable problems are those which take too long to solve to be
useful. Finding solutions to these problems is a key problem in computer science today and has been
for many years. It is also important for a computer scientist to recognise intractable problems as it
would be a huge waste of time trying to write a program to an intractable problem. It would be
better to recognise this and take a different more suitable approach that would save time and effort
but also would come up with a valid solution. However there are advantages to making a problem
intractable though as when securing things using intractable codes which cannot be hacked into
easily. Complexity can be simply defined as a rough estimation of amount of time it takes a
particular algorithm to solve a problem. This is not only measured in time but can be expressed as
the number of steps taken in order to solve a problem. It is important to be able to measure the
complexity of a problem as this can help to indicate how good/effective an algorithm will be and
whether it is tractable or not.One of the most popular intractable problems that has been found is the
Traveling Salesman problem. This problem represents a salesman who wants to find the shortest

Comparision Of P Self Protection Problem Algorithms For...
Comparision of p–self Protection Problem Algorithms for Static Wireless Sensor Networks Nikitha
Gullapalli
Graduate Student, Dept. of Computer Science and Engineering, University of South Florida
nikitha@mail.usf.edu Abstract – Wireless sensor networks are being widely used in many
surveillance applications. Since sensor nodes are a critical part of sensor networks, certain level of
protection needs to be provided to them. The self–protection problem focuses on using sensor nodes
to provide protection to themselves instead of the target objects or certain target area so that the
sensor nodes can resists the attacks targeting to them directly. In this paper we compare paper [1]
and paper [2]. The key research question being asked, how ... Show more content on Helpwriting.net
...
KEY RESEARCH QUESTIONS
1. How to determine the minimum set of sensors for covering problems of sensor networks?
Efficient centralized and distributed algorithms with constant approximation ratio for the minimum
p–self–protection problem in sensor networks when all sensors have the same sensing radius.
2. Is finding minimum 1–self–protection a NP–complete?
Yes, it is proved that finding minimum 1–self–protection is NP–complete by reducing the minimum
set cover problem.
3. If MIS is selected to provide certain protection to nodes, what happens after some rounds when it
already has p–protections?
Purpose of selecting MIS is to provide certain protections to nodes that are not selected into the
MIS. However, this may not be necessary after some rounds for some nodes when it already has p
protections from selected active nodes Thus, for each node u, we again use p(u) to denote the
protection level (i.e., the number of active sensors that can sense this node) that it already has
achieved via previously activated sensors from MIS's.
4. Is there a smarter way to select the nodes instead of randomly?
Instead of random selection of a sensor to cover each active sensor in MIS, we can use a smarter
method to select the nodes to protect the MIS nodes with less than p protectors in the last steps of
our algorithm.
5. As each sensor has limited power and resources, how to balance the energy consumption?
To balance the energy consumption, one simple method proposed in this paper is

Marketing Of An Financial Institution
2. Literature Review
2.1 Introduction
Marketing of an financial institution is different from other industries. A Financial institution has a
very unique product range. Lets take a credit card as an example: You have done the market
research, using focus groups, surveys and on–line market–research to identify the features that will
be of value to your target market. To differentiate your product from competitors you will identify
the advantages and communicate them to the prospective clients. This is all classic product
marketing.
The internet has changed the way consumers went about deciding on a service provider or product.
In the past informal opinions were exchanged with in a trusted circle of family and friends to reach a
final ... Show more content on Helpwriting.net ...
This new media may be disruptive to old business models and practices but opens the door to an
enhanced and real–time consumer relations (Henning–Thurau, et al., 2010). Social media popularity
is growing at an exponential rate and consumers give their opinion on products and services on
electronic channels. There is no need for surveys and pols, marketers need to adapt to the new forms
of consumer conversations. In the past collecting consumer opinion was timely and expensive using
focus groups and surveys. Today the daunting amount of data gathered on social media is
inexpensive to collect but sometimes a challenge to derive insight from. The consumer data is
unstructured and thus hard to classify, summarise and derive meaning from.
With the advent of cognitive computing and the technology platforms like IBM Watson, these type
of problems seems surmountable.
2.2 Evolution of Data Analytics
As shown in Fig 2 data volume has grown exponentially in recent years. Not only did the volume of
data grow, but also the ways in which data is collected as well as the type of data collected have
grown immensely. This called for much more sophisticated analytics technology and processing
power in order to gain insight from disparate data sources and big data volumes. Figure 2 The
exponential growth of data volume and type (Source: Dell, 2015:2)
During the last few years, analytics progressed through three distinguishable stages, bringing us to
the current era of

Timetable Management System Using Java
MCA
Semester – I
S.No.
Course Code
Course Name
1
2
3
4
5
COMP 712
Programming & Problem Solving with C
COMP 714
COMP 715
MAS 621
BAM 752
Introduction to Softwares
Computer Organization and Architecture
Discrete Mathematics
Business Communication
Total Credits
Semester – II
S.No.
Course Code
Course Name

6
COMP 723
Operating System
7
8
9
10
COMP 724
COMP 725
COMP 726
MAS 661
Data Structures using C++
Information System Analysis and Design
Web Technologies
11
BAM 753
Essentials of Management
Computer based Numerical and Statistical
Techniques
Total Credits
Semester – III
S.No.
Course Code
12
13
14
15
16
17
COMP 731
COMP 732
COMP 733
COMP 736
COMP 837
BAM 796

Semester – IV
S.No.
Course Code
18
COMP 842
19
COMP 843
20 ... Show more content on Helpwriting.net ...
Unit 3: Software
System software, Operating System, Functions of OS, Overview of DOS,
Windows and Unix.
Application software (Word Processor, MS–Excel, MS–PowerPoint)
Unit 4: Programming Languages and Software Development
Generation of Languages, Compiler, Assembler, Linker, Loader, Software
Development Methodology, Software Development Life Cycle
Programming Languages: Programming Language Paradigm, Procedure–Oriented
Language, Object– Oriented Language, Web Based Languages
Unit 5: Network and Data Base Concepts
Definition and Types of Network, Introduction to Internet– Search Engine, Web
Page, Web Browser, Introduction to E–Commerce.
Data Base definition, Data Base Management System, overview of MS–Access
Text Books:
1. Fundamentals of Computer: – V. Raja Raman
2. Fundamentals of Computer: – P. K. Sinha
Reference Books:
1. Microsoft Office Black Book
2. UNIX: The Ultimate Guide: – Sumitabha Das
3. PC Software: – V.K. Jain "O Level"
Computer Organization & Architecture
Code: COMP–715
Credits: 4(3+1+0)
UNIT–1
Introduction: Types of computers: Analog, Digital and Hybrid Computers, Modern Digital
Computer,
Number systems– Binary, Octal, Decimal, Hexadecimal , 1's & 2's Complement.
Digital logic circuits and Components: Logic gates, Boolean Algebra, K–Map Simplification, Half
Adder, Full Adder, Decoder, Encoders, Multiplexers, Demultiplexer, Flip Flops, Registers, Binary
Counters.

Project Proposal ( Option 3 )
Project Proposal (option 3)
Name: Yao Yang UNI: yy2641
The common theme my project will be focuses on is K–means clustering.
The k–means clustering problem can be described as following:
Given a set of n data points in d–dimensional space R^d and an integer k. Find a set of k
points/centers in R^d, such that the mean squared distance from each data point to its nearest centre
(one of the k centers) is minimized.
Paper Summaries:
A local search approximation algorithm for k–means clustering (2004)
The paper considered whether there exists a simple and practical approximation algorithm for k–
means clustering.
It brings up the classical tradeoff between run time and approximation factor.
A local improvement heuristic based on swapping centers in and out that yields a (9 + ϵ)–
approximation algorithm is presented. The paper also shows that any approach based on performing
a fixed number of swaps achieves an approximation factor of at least (9–ϵ) in all sufficiently high
dimensions by providing example.
In summary, the approximation factor is almost tight for algorithms based on methods that perform
fixed number swaps as shown by the paper.
The effectiveness of Lloyd–type methods for the k–means problem (2006)
This paper show that if the data satisfies a natural condition on separation/clusterability, then a near–
optimal clustering result can be expected as various Lloyd–style methods would have great
performance in such setting.
The major algorithmic contribution from

General Approaches For Feature Selection
General Approaches for Feature Selection There are 3 types of approaches for feature selection
namely filter, wrapper, embedded method.
Filter method: Filter method does not involve a learning algorithm for measuring feature subset [6].
It is fast and efficient for computation .filter method can fail to select the feature that are not
beneficial by themselves but can be very beneficial when unite with others. Filter method evaluates
the feature by giving ranks to their evaluation value. In filter method it evaluates the correlation
between the features by using criteria such as, mutual information, maximum length, maximum
relevance min redundancy (mRMR), PCA.
Figure 1.2 Filter approach
Wrapper method: wrapper method involve learning algorithm and search for optimal feature subset
from original feature set which discover the relationship between relevance and optimal subset
selection. It performed better than the filter method. The specific training classifier is used to
evaluate the performance of selected features. Figure 1.3 Wrapper approach
Embedded method: Embedded method is a combination of wrapper method and embedded method.
This decreases the computational cost than wrapper approach and captures feature dependencies. It
searches locally for features that allow better discrimination and also the relationship between the
input feature and the targeted feature. It involves the learning algorithm which is used to select
optimal subset among

Basic C Input / Output, Expressions And Functions
Basic C Input/Output, expressions and functions
Part 1 – Research
What are embedded Systems?
An embedded systems is a computer system with one or more specific tasks within a larger machine
or system. Generally at the heart of an embedded system is a microprocessor that would control
some input and output devices. Embedded systems often face much stricter constraints than most
general purpose computer systems so they are much harder to program[1].
Fig 0 [3]
The main constraints an embedded system may face are[2]:
1)Real time –
Real time is when a system must respond to an event within a strict deadline that would affect the
safety or usability of the device.
2)Low power –
Most embedded systems have a limited power supply so they have to save energy while doing their
tasks.
3)Low cost –
Some embedded systems are intended to be mass produced and therefore the cost per unit must be
optimized often this means getting a 'less sophisticated' processor or interface components to do the
work needed.
4)Reliability –
Embedded systems are often ingrained deep inside a complex system and it may expensive,
impractical or dangerous to repair these devices frequently. Therefore some embedded systems must
be as reliable as possible.
5)Computing performance –
6) Size and weight– some embedded systems
Since embedded system are targeted at very specific tasks this makes it much easier to optimize for
the particular application than you could with general purpose computer[3].
Uses

Kruskal Algorithm Essay
Kruskal's Algorithm
Introduction:
In 1956, the minimum spanning tree algorithm was firstly described by Kruskal. In same paper
where he rediscovered the Jarnik's algorithm. The algorithm was rediscovered in 1957 by Loberman
and Weinberger, but avoided to be renamed after them. The main idea of Kruskal's algorithms is as
follows:
Scan all the edges in the increase of weight order;
[If any edge is safe, keep it (i.e. add it to the set A)]
Kruskal's Algorithm is directly based on generic MST algorithm. It builds MST in the forest. On the
initial level, each of the vertex is in its own tree in the forest. Then the algorithm consider each of
the edge in turn, order by the increase of weight. If any edge (u, v) connects two of the different ...
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We assume that we use the disjoint–set–forest implementation. With the union by rank and path
compression heuristics, since it is the asymptotically fastest implementation known. Initializing the
first set in line 1 takes O (1) time, and the time to sort the edges in the line 4 is O (E lg E).The for
loop of lines 5–8 performs O (E). Find–set and Union operations on the disjoint–set forest. Along
with the make–set operations, these take total O ((V+E)a(V)) time, where 'a' is the very slowly
growing function. Because we assume that G is connected, we have |E| >=

Personal Statement Essay
People who know me typically find out pretty quickly that next to my passion for medicine, I am
actually one of the world's hugest computer geeks. In fact, when I first entered college, I toyed with
the idea of studying computer science before I realized that the study of the human body was so
much more exciting because it allows me to combine my fervor for computers and merge it with my
interest in medicine.
Once I began medical school, I started to see the human body like the hardware of a computer
system. For example, the nervous system – the midbrain is the CPU and the cortex acts as RAM.
The spinal cord acts as the motherboard, creating a system for impulses to travel back and forth. The
eyes and ears act like the mouse and ... Show more content on Helpwriting.net ...
Once that diagnosis has been hypothesized, appropriate treatment acts as the debugging phase. If a
treatment plan is not working, then another diagnosis needs to be developed. A patient's objective is
fulfilled when the appropriate illness has been revealed and eliminated.
With the ability and appreciation for contemplating a particular pathway from my computer science
background, I found the task of organizing a patient's multiple complaints into a particular
pathology to be most fascinating. This obsession has sparked anticipation to specialize in
endocrinology, diabetes and metabolism. Similar to how a computer algorithm may flow from one
level down to the next, the endocrine system involves a delicate system of hormones in the
hypothalamic–pituitary axis. Any disruption in the pathway resulted in a symptomatic change that
can be elicited from the patient. The objective simply becomes identifying these symptoms and
associating it with the appropriate disruption in the pathway.
Methodical thinking has also helped me to comprehend the careful equilibrium of the body's
metabolism. I used to be one who paid very little attention to my own health, especially when I was
more focused on a particular activity. By the time I had reached my second year of medical school, I
noticed my weight had increased to about 210 pounds. With a height of 69 inches, my BMI was 31
and I was categorized as obese. While studying

Questions On Advanced Discrete Math And Algorithms
CS7800: Advanced Algorithms. Fall 2016 Homework 1 Solutions Author: Aditeya Pandey,
Collaborators: Micha Schwab,Supraja Krishnan Problems 1–3 are meant as a review of
undergraduate discrete math and algorithms. They shouldn't take you too long, but I recommend
starting these right away to make sure that you have the appropriate background for this course. You
must type your solutions using L A TEX. Please submit both the source and PDF files using the
naming conventions lastname hw1.tex and lastname hw1.pdf. Strive for clarity and conciseness in
your solutions, emphasizing the main ideas over low–level details. I recommend looking at the
introduction in Jeff Erickson's textbook for advice on writing up solutions to algorithms problems.
Do not share written solutions, and remember to cite all collaborators and sources of ideas. Sharing
written solutions, and getting solutions from outside sources such as the Web or students not
enrolled in the class is strictly forbidden. 1Review Problems Problem 1 (Review of Asymptotic
Growth). Arrange the following list of functions in ascending order of growth rate. That is, if
function g(n) immediately follows function f (n) in your list, then it should be the case that f (n) is
O(g(n)). (You do not need to provide proofs.) 2 f 1 (n) = 4n 2 + n log 28 (n) √ f 4 (n) = n + 40 n f 2
(n) = 2 n f 3 (n) = 1024n log 2 (n) f 5 (n) = 10 n f 6 (n) = 3n log 2 (n) f 7 (n) = n 2 log 8 n f 8 (n) =
4096 log 42 (n) f 9 (n) = n log 2 (3) Solution

A Comparative Analysis Of Force Directed Layout Algorithms...
Lauren Peterson
6 December 2016
Term Paper 3 Page Update
Bioinformatics Algorithms: Dr. Kate Cooper
A Comparative Analysis of Force Directed Layout Algorithms for Biological Networks
Brief Description:
I will conduct a comparative analysis of multiple force–directed algorithms used to identify clusters
in biological networks. The analysis will consider topics such as the algorithm process, amount of
preprocessing, complexity, and flexibility of the algorithms for different types and sizes of data. K–
Means, SPICi, Markov Clustering, RNSC, and PBD will be used for the comparison. I will identify
the best algorithm according to my analysis for each type of input data studied.
Background: how to determine if a clustering algorithm is good/if a cluster is good→ modularity
Proteins control all processes within the cell. Though some proteins work individually, most work in
groups to participate in some biochemical event. Examples of these processes include protein–
protein interaction networks, metabolome, correlation/co–expression values, synthetic lethality, and
signal transduction (Cooper, lecture). The study of proteins that work together can allow a greater
understanding of cellular processes. New pathways, proteins, or systems can be identified via
network analysis. In order to recognize groups of proteins that work together, a biological network,
called a graph, is formed.
The study of graphs has a prominent history in mathematics and statistics. Graph Theory

Personal Statement: My Interest In Science
My interest in science was kindled when I was just a child. I was a very enthusiastic reader of
science books. Those books introduced me to physics ,chemistry and other sciences. But my
relationship with computers started when I first came across the computers in my school days. I was
so very amused with computers that I fell in love with computers. I was very fascinated with the
way the computers performed every task so effortlessly. The first calculation, the first game I
played, the first mouse cursor movement I experienced were all just amazing. I still remember every
bit of those cherished memories. As the days went by my interest in the field of computers grew
more and I started performing small experiments in my institute by writing simple programs. I wrote
a program to find a factorial of a numbers and was excited to get the results; I then wrote a program
to find the square of a number and was thrilled to get the square of a number in a fraction of ... Show
more content on Helpwriting.net ...
Furthermore, I am very keen on giving private lectures to students younger to me to help them in
understanding courses and in forming careers. I have given many seminars with respect to the
subjects of my course in under–graduation study and was pretty good in convincing fellow students
about the topics and I was always the first choice of professors if they had to assign someone to give
away the seminars, I have the capability to prepare for the seminar in the shortest of the time and
deliver in a convincing manner. And as far as sports are concerned, I am interested in playing
badminton, cricket, table–tennis, volleyball, I have been playing cricket & volleyball since
childhood and I have also represented my class in high–school and under–graduation, if had a
chance I would surely represent the university in which I

What Is The Algorithm For Multi-Networking Clustering...
with most extreme number of sensor nodes in each cluster could be accomplished. The weight
capacities at every sensor node, which is a blend of various parameters including: residual energy,
number of neighbors and transmission control. Basically CFL clustering algorithm is designed for
localization in WSNs. It is unable to work when the distribution of sensor nodes are not good.
3.2.4 FoVs: Overlapped Field of View Authors proposed a clustering algorithm for wireless sight
and sound sensor networks in light of covered Field of View (FoV) areas. The fundamental
commitment of this calculation is finding the convergence polygon and figuring the covered
territories to build up clusters and decide clusters participation. For dense networks, ... Show more
Along these lines CHs (cluster heads) closest to the BS (base station) can protect more vitality for
between energy transmission. PEZCA give more adjust in energy consumption and and life time of
network correlations with LEACH.
3.2.7 VoGC: Voting–on–Grid clustering In this creator joined voting technique and clustering
algorithm, and grew new clustering plans for secure localization of sensor networks. Authors
likewise found that the recently proposed approaches have great exhibitions on limitation exactness
and the discovery rate of malevolent guide signals. In this plan, malicious guide signals are sifted
through as per the clustering consequence of crossing points of area reference circles. Authors
utilized a voting–on– grid (VOGC) strategy rather than customary clustering calculations to lessen
the computational cost and found that the plan can give great limitation exactness and recognize a
high level of malicious beacon signals. 3.2.8 BARC: Battery Aware Reliable Clustering In this
clustering algorithm authors utilized numerical battery demonstrate for execution in WSNs. With
this battery show authors proposed another Battery Aware Reliable Clustering (BARC) calculation
for WSNs. It enhances the execution over other clustering calculations by utilizing Z–MAC and it
pivots the cluster makes a beeline for battery recuperation plans. A BARC

A Decision Tree Based Rule Formation With Combined Pso...
CHAPTER 3
A DECISION TREE BASED RULE FORMATION WITH COMBINED PSO–GAALGORITHM
FOR INTRUSION DETECTION SYSTEM
3.1 INTRODUCTION The increase in the usage of the computer networks leads to the huge rise in
the threat and attacks. These attackers change, steal and destroy the valuable information and finally
cause complete damage to the computer system of the victim. They affect the performance of the
computer system through the misconfiguration activities and generation of software bugs from
internal and external networks. Irrespective of the existence of various security mechanism,
attackers often attempt to harm the computer system of the intended legitimate users. Hence,
security is a main factor for the efficient operation of the network in various applications such as
healthcare monitoring, military surveillance, etc. The most common security mechanisms are
firewalls, antivirus programs and Intrusion Detection System (IDS).
Firewalls (Fehr, 2013) are the commonly used mechanism for securing the corporate network or
sub–network. The firewall is operated based on a set of rules that can protect the system from the
flooding attacks. The main function is sorting of the packets according to the allow/deny rules,
based on the header–filed information. But the firewalls cannot ensure complete protection of an
internal network, since they are unable to stop the internal attacks. The computer viruses can cause
damage to the computer data that leads to the complete failure of the

Seeking Admission to a Computer Science Graduate Program...
I wish to introduce myself as Mr.John Doe, a prospective student aspiring to enroll into the graduate
program in Computer science at The University of Mexico 2015. The post–graduate program in
Computer science offered by your esteemed University appears very righteous to me. It would
enlighten me further with significant theoretical and practical knowledge that would update me with
the current trends and advancing necessities of the IT industry with the latest technologies.
My aim is to acquire sufficient knowledge and experience so that I contribute to the development of
the IT sector world over. I consider myself as the citizen of the future making a difference to the
global IT industry. Coming from an entrepreneurial family with a ... Show more content on
Helpwriting.net ...
This system implemented a screen positioned at eye–level on the wall next to customers.
The arena of computer applications is an ever expanding one where the boundaries between its
various fields are becoming increasingly blurred. To have a multidisciplinary approach is the way
forward. Consequently, I have been deeply involved in hardware and software, installation and
Maintenance and also other computer languages as a part of undergraduate studies. My interest in
these areas has been enhanced by the subjects I have learned.
I would very much like to work in the area of computer based information systems. I want to invent
some cutting edge technology that will be beneficial improving people's lives. I consider this as a
highly responsible and demanding job and consider myself as a suitable candidate willing to put my
earnest efforts in this field. In this endeavor, I hope to contribute significantly to the human society
by improving the quality of life and standard of living.
Over the past three years, I have become absolutely convinced that the path towards my goal will
involve leveraging my current skills and abilities to make meaningful contributions in Information
systems. I am sure that it will be an exhilarating experience even though I will have to make
personal sacrifices in terms of time and immediate reward.
Six or seven years

Implementing A Proactive Push Scheme Based On The Http / 2...
The first paper proposed a proactive push scheme based on the HTTP/2 server–push feature, the
second paper, on the other hand, improved the push method from fixed numbers into adaptive
numbers based on multiple factors.
In this section, we will compare two outstanding papers with different parameters in the following
four metrics:
(a) Number of requests
The definition of number of requests is the total number of HTTP requests sent by the client during
the streaming of the video. This metric measures the protocol overhead generated on the network.
Before HTTP/2, each packet required by the client needs a separate HTTP request sent from client
to server. The server will then send the HTTP response with the video segment as the payload. With
HTTP/2, the it is not needed any more that the client sends a new request for each segment. The
server will push the subsequent segments to the clients and therefore reduce the overhead to the
network. So, we expect an as lowest as possible number of this metric.
From the experiment results of the two selected paper, we can get the data of number of requests as
follows, methods metrics
Method of [3]
Method of [4]
N = 1
N = 2
N = 3
N = 4 a=0.6 A=0.4
Number of request
1000
500
334
250
518
383
Figure 9. The comparison of number of requests for methods [3] and [4]
The parameter 'N' in the method of [3] means the number of segments to be pushed to the client
each time.
The parameter 'a' in the method of [4] is the key coefficient of the

Computing Machinery And Intelligence By Alan Turing
Defining intelligence is extremely difficult and researchers now think that there are lots of different
aspects to it, including; social, logical, creative, emotional and practical intelligence. The correct
representation of the word is arguably a matter of opinion as there are many different definitions.
With this argument in mind, it makes the task of arguing a Computers level of intelligence (if any)
harder as, ultimately, each person's definition could be different. A part of being intelligent is the
ability for something/someone to be able to think for themselves which most computers aren't able
to do as they are simply programmed to perform certain functions which means that they are not in
control of their response. However modern technologies have the propensity to make decisions
based on an input from an end user. For example Apples 'Siri' service turns voice commands in
visual and audio responses. Alan Turing put this argument forward in his 1950 paper "Computing
Machinery and Intelligence". His opening pages of the paper begin with the words; "I propose to
consider the question, 'Can machines think?" (Alan Turing 1950). My main argument to this is that a
computer does not have the ability to think, primarily because it was created rather than 'born and
raised'. What is a computer? There are thousands of different types of computers that are built for an
extremely vast range of purposes, but overall, what do you class as a computer? Anything with a
CPU?

The Ict And Its Impact On Education
1. Introduction
The ICT subjects was introduced in secondary schools early 2000's with poor methodologies. It was
that time I was in secondary schools so I saw and even experienced how the mode of computer skills
delivery was. The word 'computer' itself was a bit knew to most students thou I was very good in
mathematics. Shortage of instructors and teaching tools was a big issue. Most young generation fails
to express themselves in terms of their future vision because they are blind to the rapid innovations
in computer technology. We are coming from different background with different family economic
levels. Not all of the young people are aware of this computerized system of the world. Not all are
privileged with computerized cellular ... Show more content on Helpwriting.net ...
I believe that the gap/problems lies at the 'middle' (secondary) schools whereby the introduction of
IT could reveal the vision and ability of most youth. Its more than ten (10) year of introduction of
secondary IT knowledge, I expect to see some positive changes. Learning ICT is a process of
growing awareness on various realities of life, thus lack of this skills problems won't be evaluated.
The ongoing invasion of outsider's technologies gives me warning alarm that the indoor capability is
still low.
Monitoring and Evaluation of every project is a key way to development; the ICT to secondary
schools need a special M&E section to ensure that there is positive progress annually. I know that
computer programming is mathematics based in one way or another, and the big number of
secondary students do no like mathematics. This should be tackled with sharp stick lest the mind–set
and capability of students lowers down. Indicator for ICT development in Tanzania will be openly to
all teachers so as to improve their efforts.
With a lot of ICT knowledge and skills, what changes can we say that this program is bringing to
our upcoming generation? Several field like engineering, medicine, finances and social science
needs a lot of ICT skills. Does this syllabus real brings change? What are the factor hindering big
results? If we have big results, how can we improve it? What are the indicators for ICT development
in Tanzania. Which

How Technology Has Changed Our Lives
Information technology has become a critical aspect and central part of every business today. An
effective IT system is a very important part that is comprised of several different functional elements
and each of these must work properly so that the system run as smoothly as possible. This paper will
discuss the five core technologies in an effective IT system and how they interact with one another.
The five core technologies include, Computer Programs and Programming, Computer Networks,
Databases, Information Assurance, and Web Technologies.
New technologies have caused a shift in our culture in many ways. The way we consume news,
listen to music, watch movies, and even how we purchase everyday items. As technology changes so
will our culture and communication and it is important that we continue to improve on them. Many
of the advancements in technology have changed our way of life for the better. Learning how to
construct a computer program is almost like learning a foreign language and the more you practice
the better at it you become. Learning how to program inspires creativity and the power to impact the
world. Computer programming involves developing lists of instructions often referred to as a code
or coding. Computer programs can develop video games, graphics and animations and even allow
students to attend class via online. Programing involves skilled attention to detail. Programmers can
also rewrite, debug, maintain and test (and retest and retest) software and

What Is The Intuitive Approach To The Orthogonal Matching...
Chapter 4
4. Orthogonal Matching Pursuit
4.1 Basic Pursuit
4.2 Orthogonal Matching Pursuit
4.3 Compressive Sensing in Image Processing
4.4 Modified OMP
Chapter 4
4. Orthogonal Matching Pursuit
Before discussing OMP we will go through the very basic algorithm used in CS i.e. Basic pursuit.
4.1 Basic Pursuit
The intuitive approach to the compressive sensing problem of recovering a sparse vector x ∈ RN
from its measurement vector Φx=y ∈ Rm, where m < N, consists in the l0 –minimization problem
min┬(xϵ R^n )⁡
〖‖x‖_(0 ) subject to Φx=y 〗 (4.1)
This is a non convex problem that it is NP–hard in general. However, keeping in mind that ‖z‖_q^(q
) approaches ‖z‖_0as q > 0 tends to zero, we can approximate (4.1) by the problem ... Show more
For 0 < q < 1, (4.2) is again a nonconvex problem, which is also NP–hard in general. But for the
critical value q = 1, it becomes the following convex problem (interpreted as the convex relaxation
of

Nt1330 Unit 7 Data Analysis
Homework #7
Problem 1.
(a). Here we have k number of routes from one source to one destination, which is a graph with the
source and shrink.
Suppose we have n files to transfer from k routes by minimizing the total time. We can apply simple
greedy routing algorithm. This algorithm assigns the file to the route which has low load compared
to other.
Select route from graph which has capacity still greater or equal to the li.
(b). The problem belongs to P Class (Polynomial Time).
(c). Let number of routes be k
Let n files f1, f2, ..., fn be transferred from source to the sink.
Let length of files be l1, l2, ..., ln and transfer speed be S
Let L be the total length of files
L = 0
For i = 1 to n L = L + li
EndFor
Optimal Time T = L/kS
Return T ... Show more content on Helpwriting.net ...
Problem 2.
(a). Here in the given problem we have scheduling problem, which we call multiple interval
scheduling. Here the change is job require a process for more than one set of intervals.
(b). This problem belongs to NP–complete, because we have k set of times and we can check that it
overlaps or not.
(c). Suppose we have Algorithm A to solve this problem we have k collection of interval. And we
will take the instance of the 3 dimension that is, set of 3 intervals.
So, we have triples (t1i, t2j, t3k) from the set T1, T2 and T3 of size n each. We divide the time in 3n
disjoint interval. For each tuple, we can find job that require same intervals si, sn+j, s2n+k.
Now as per algorithm if there is valid triple matching then this is the set of jobs where intervals are
completely disjoint, hence we can accept this set of jobs to run on the processor as there is no
overlapping. The size of the multiple interval scheduling is polynomial n.

Algorithm:
Let n denote the number of edges in G, and label them e1,...,en
Divide time into n disjoint slices, s1,...,sn, with slice si intuitively corresponding to edge ei
For each vertex

Features Reflection Analysis
Feature extraction is defined as transform the existing features into a lower dimensional space.
subsection{Local Binary Pattern} Local Binary Patterns (LBP) is on of well–know descriptor used
for pattern recognition cite{G1}. LBP describes textures features of the image. It divided into two
different descriptors: (1) local descriptors and (2) global descriptor. The global descriptor used for
separating the non objects blocks, while the local provides detailed objects information which can
be used in recognition application. It is mathematically calculated through dividing the image into
blocks and computing the texture histogram for each block.par Let pixel position $(i_{c},j_{c})$,
LBP is defined as an ordered set of binary pixel ... Show more content on Helpwriting.net ...
Random variables or vectors can be expressed by its entropy. MI is calculated by taking the
difference between entropy of a variable and the conditional entropy. It can be expressed as the
Kullback–Leibler divergence between the product of marginal distributions $mu_{x}, mu_{y}$
and the product of marginal distribution $mu_{x,y}$. It's mathematically defined at Equ.
(ref{MI}). begin{equation}label{MI} MI(x,y)=int mu_{x,y}(x,y)log{frac{mu_{x,y}(x,y)}
{mu_{x}(x)mu_{y}(y)}} end{equation} where $x$ and $y$ are two independent variables, where
$x$ is a feature vector/variable and $y$ is class label. When the mutual information reaches to zero
that means the higher dependency between $x$ and $y$. The higher dependency means the higher
mutual information. subsection{Statistical Dependency} The main objective of statistical
dependency (SD) is measuring the dependence of feature values on the associated class labels
cite{G3}. It also measure weather two feature vectors are simply occurs by chance. SD is
mathematically defined at Equ. (ref{SD}). begin{equation}label{SD} SD(x,y)=int mu_{x,y}
(x,y){frac{mu_{x,y}(x,y)}{mu_{x}(x)mu_{y}(y)}} end{equation} where $x$ is discredited
feature values and $y$ is the corresponding label. The higher SD, the higher dependency between
the feature value and its corresponding class label. When SD equals to 1 that means the
corresponding feature is fully independent of

Software 520 : Differential Evolution Essay
Intro:
Hi, my name is blank and the project I have been working on this year for computing 520 is
differential evolution, DE, on the cloud, under the supervision of blank.
Parallel programming, the utilisation of many small tasks to complete a larger one, has become far
more prevalent in recent times as problems call for systems with higher performance, faster turnover
times, easy access, and lower costs. While this has previously been cost–prohibitive, given that one
would have had to purchase a large number of physical machines to work on, the development of
cloud computing systems has largely answered this call, providing resources and computing power
as a service to users, rather than a product. The addition of hardware virtualisation has further
increased the availability of massively–parallel collections of computers as flexible networked
platforms for computing large–scale problems.
Differential Evolution, or DE, is a cost minimisation method that utilises various evolutionary
algorithm concepts, but can also handle non–differentiable, nonlinear, and multimodal objective
functions that standard evolutionary algorithms cannot. Experiments have shown that DE shows
good convergence properties and outperforms other EA's, converging faster and with more certainty
than many other popular global optimization methods.
DE provides a general optimization function that converges on an optimal set of parameter values
according to some objective function. This is a valuable

Example Of Greedy Algorithm
3 Greedy Algorithm
Greedy technique is an algorithm design policy, built on configurations such as different choices,
values to find objective .Greedy algorithms produce good solutions on mathematical problems. The
main aim is to find some configurations that are either maximized or minimized. Greedy Algorithms
provide a solution for optimization problems that has certain sequence of steps, with a set of choices
for each step. Another solution for Greedy algorithm is dynamic programming . It is also used to
determine the best choices. But greedy algorithm always makes the choice that is best at the moment
to provide the optimal solution for the problem. A greedy algorithm for an optimization always
provides the current sub solution. Basically greedy algorithm always gives an optimal solution to the
MST (Minimum Spanning tree)problem. Some Examples that are solved by greedy algorithm are
Dijkstra's shortest path algorithm and Prim/Kruskal's algorithms.
Greedy algorithm technique relies on following elements:
configurations: It consists of different choices, values to apply on data.
objective : some configurations to be either maximized or minimized to get the predefined objective.
Greedy algorithms are applicable to optimization problems ... Show more content on
Helpwriting.net ...
On the other hand it shows that data mining can become both a source of making discrimination and
discovering discrimination items present in the dataset. Discrimination of data is categorized in two
types as direct discrimination and indirect discrimination. Direct discrimination consists of data
mining rules that inherently mention underprivileged groups based on sensitive discriminatory
parameters present in that data set. Indirect discrimination consists of data mining rules that will not
explicitly mentions the discriminatory

Profile Based Personalized Web Search
We suggest a Profile–based personalized web search framework UPS (User customizable Privacy–
preserving Search), for each query ac–cording to user specified privacy requirements profile is
generated. For hierarchical user pro–file we trust two conflicting metrics, namely personalization
utility and privacy risk, with its NP–hardness proved we formulate the problem of Profile–based
personalized search as Risk Profile Generalization.
With the help of two greedy algorithms, namely GreedyIL and GreedyDP, we generate the expected
search result, greedy algorithms support runtime profiling. While the former tries to maximize the
discriminating power (DP), the latter attempts to minimize the in–formation loss (IL). By exploiting
a number of heuristics, GreedyIL outperforms GreedyDP significantly.
For the client to decide whether to personalize a query in UPS we provide an inexpensive
mechanism. Before each runtime profiling this decision can be made to improve the stability of the
search results while avoid the needless exposure of the profile.
3.1 System Architecture :
Indeed, the privacy concern is one of the main barriers is how to attain personalized search though
preserving users privacy and deploying serious personalized search applica–tions. Hence we
propose a client side profile–based personalization which deals with the preserving privacy and
envision possible fu–ture strategies to fully protect user privacy. For
Fig. 1. Personalized Search Engine

Survey On Sentiment Analysis And Opinion Mining
A Survey on Sentiment Analysis and Opinion Mining Abstract– This survey reviews the recent
progress in the field of sentiment analysis with the focus on available datasets and sentiment
analysis techniques. Since many exhaustive surveys on sentiment analysis of text input are
available, this survey briefly summarizes text analysis techniques and focuses on the analysis of
audio, video and multimodal input. This survey also describes different available datasets. In most
of the work datasets are prepared as per specific research requirements. This survey also discusses
methods used to prepare such datasets. This survey will be helpful for beginners to obtain an
overview of available datasets, methods to prepare datasets sentiment analysis techniques, and
challenges in this area. Key words– Sentiment Analysis, Opinion Mining, Multimodal Sentiment
Analysis, datasets 1 INTRODUCTION Opinions always play an important role in decision making.
Businesses seek consumer opinions about their products and services to improvise them, consumers
seek opinions of other consumers to get the best deal. Governors and policy makers of the country
also need to consider opinions of the masses before making some decisions. Emergence of social
networking sites, blogs, forums, e–commerce websites have provided internet users with a platform
where they can express their opinions. Thus a huge source of opinions, views, and sentiments has
been created and is being updated every day. The

Cis 115 Week 3 Exercise Solution
Week 3 Activity–Calculate Overtime Pay
–––––––––––––––––––––––––––––––––––––––––––––––––
TCO 3–Given a simple problem, design and desk–check a solution algorithm requiring a modular
design that is expressed in terms of pseudocode or program notes, input–process–output (IPO)
analysis, and flow chart.
–––––––––––––––––––––––––––––––––––––––––––––––––
TCO 4–Given a simple problem that requires one or more decisions, create a working solution that
uses decisions with logical and relational expressions.
–––––––––––––––––––––––––––––––––––––––––––––––––
TCO 8–Given a more complex problem, develop a complete solution that includes a comprehensive
statement of the problem, complete program design, and program documentation. ... Show more
Game Seating Charges | Document | Points possible | Points received | Variable list | 4 | | IPO chart |
4 | | Flowchart | 4 | | Pseudocode | 4 | | Working program | 4 | | Total points | 20 | |
1) Variable List
List all variables you will use (use valid variable names). Indicate whether the data type is string,
integer, or double, and so on.
Constant MARRIED_RATE .15 Decimal
Constant SINGLE_RATE .22 Decimal
Constant DIVORCED_RATE .23 Decimal
Constant WIDOWED_RATE .13 Decimal hourlyRate Decimal hoursWorked Decimal regularPay
Decimal overtimePay Decimal maritalStatus Character grossPay Decimal taxRate Decimal
taxAmount Decimal netPay Decimal
2) IPO Chart
List the inputs, any processes/calculations, and outputs. Use the same valid variable names you used
in Step 1.
Inputs | Process (calculations) | Outputs | hourlyRatehoursWorkedmaritalStatus | if maritalStatus =
'M' taxRate = MARRIED_RATEif maritalStatus = 'S' taxRate = SINGLE_RATEif maritalStatus =
'D' taxRate = DIVORCED_RATEif maritalStatus = 'W' taxRate = WIDOWED_RATEIf
hoursWorked <= 40 grossPay = hoursWorked * hourlyRateElse regularPay = (40 * hourlyRate)
overtimePay = ((hoursWorked–40) * (hourlyRate * 1.5)) grossPay = regularPay +

overtimePaytaxAmount = grossPay * taxRatenetPay = grossPay – taxAmount | If hoursWorked
<= 40 grossPay netPayElse

A Master 's Degree Of Computer Science
Statement of Purpose Whether providing light during blackouts, adding a romantic flair to an
evening dinner, or just adding a pleasant fragrance and sense of comfort to a college student's
apartment, candles are an important, yet often overlooked part of our lives. This became clear to me
when my ailing grandmother requested that we bring candles from her house to her in the hospital
so that she could have reminders of home. Google one of the great achievement lives of billions of
people thanks to computer science ,The Internet, one of the many great achievements of computer
science, have changed the way of acquiring information and communicate and perhaps even
thinking process of billions of people. It also advances other disciplines. After spending countless
hours browsing through Google, . I marvel at the untold possibilities computer science could
provide. I have distinction of the exact that I want to pursue a Master's degree in Computer Science.
I want to pursue a Master's degree in computer science. Computer science has brought numerous
changes to the world. Statement of purpose , the exact I have decided to apply for the following
reasons. I have distinction of the exact that I want to pursue a further degree in Computer Science.
In early 2014, I joined a research In my third year, I participated in a project that aimed to develop a
. I use my skills and help programmed the most of the program. Statement of Purpose The state of
mind is obviously the exact have to

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