The document discusses computer arithmetic and floating point number representation. It covers:
1) The Arithmetic Logic Unit (ALU) performs calculations and can handle integers and floating point numbers using separate floating point units.
2) Integer numbers are represented using binary and two's complement allows for positive and negative numbers. Floating point numbers use a sign bit, significand, and exponent in normalized form to represent numbers with fractions.
3) Operations like addition, subtraction, multiplication, and division are performed on integers in the ALU and floating point numbers following standard algorithms while managing overflow and normalization.
The document discusses integer representation and arithmetic logic unit (ALU) operations in computers. It describes how integers are represented using binary digits and two's complement notation. The ALU performs calculations on these binary integers. It can handle standard integer arithmetic as well as floating point numbers, potentially with a separate floating point unit.
The document discusses computer arithmetic and the arithmetic logic unit (ALU). It covers integer representation using binary, two's complement representation, and floating point representation. For integers, it describes addition, subtraction, multiplication, and division. For floating point numbers, it discusses the sign-magnitude format, normalization, ranges, densities, and the IEEE 754 standard. Arithmetic operations for floating point include adding exponents and multiplying/dividing significands while handling signs, normalizing results, and rounding.
This document summarizes key topics in computer arithmetic from William Stallings' Computer Organization and Architecture textbook, including:
1) The Arithmetic Logic Unit (ALU) performs calculations and handles integer and floating point representations. It may include a separate Floating Point Unit.
2) Integer values are represented in binary using two's complement, which allows for easy addition, subtraction and negation operations.
3) Floating point numbers follow the IEEE 754 standard and are represented as a sign bit, mantissa and exponent to allow for a wide range of values between +/- infinity.
Inductive programming incorporates all approaches which are concerned with learning programs or algorithms from incomplete (formal) specifications. Possible inputs in an IP system are a set of training inputs and corresponding outputs or an output evaluation function, describing the desired behavior of the intended program, traces or action sequences which describe the process of calculating specific outputs, constraints for the program to be induced concerning its time efficiency or its complexity, various kinds of background knowledge such as standard data types, predefined functions to be used, program schemes or templates describing the data flow of the intended program, heuristics for guiding the search for a solution or other biases.
Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of Turing-complete representation language.
In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or program synthesis, usually opposed to 'deductive' program synthesis, where the specification is usually complete.
In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general machine learning, the more specific area of structure mining or the area of symbolic artificial intelligence. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples.
The diversity of inductive programming usually comes from the applications and the languages that are used: apart from logic programming and functional programming, other programming paradigms and representation languages have been used or suggested in inductive programming, such as functional logic programming, constraint
programming, probabilistic programming
Research on the inductive synthesis of recursive functional programs started in the early 1970s and was brought onto firm theoretical foundations with the seminal THESIS system of Summers[6] and work of Biermann.[7] These approaches were split into two phases: first, input-output examples are transformed into non-recursive programs (traces) using a small set of basic operators; second, regularities in the traces are searched for and used to fold them into a recursive program. The main results until the mid 1980s are surveyed by Smith.[8] Due to
The document discusses different data types including binary numbers, unsigned and signed integers represented in binary, floating point numbers, and logical operations on bits. It explains binary addition and subtraction, overflow, and different representations for signed integers including sign-magnitude, one's complement, and two's complement. It also covers converting between decimal and binary numbers.
This document discusses arithmetic and logic unit (ALU) operations in a computer system. It covers:
1) The ALU is the part of the computer that performs arithmetic and logical operations on data using basic digital logic gates. All other computer elements bring data to the ALU and return results.
2) Integer numbers can be represented in binary using sign-magnitude or twos-complement formats. Twos-complement is more commonly used as it allows for simpler addition and negation operations.
3) The document explains how integer addition, subtraction, multiplication and division are performed in binary using the various representation schemes through examples and flowcharts. Special cases and algorithms like Booth's method for multiplying negative numbers are also
Organisasi dan Arsitektur Komputer MO-08EriekOrlando
Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer
The Arithmetic Logic Unit (ALU) performs calculations in a computer using binary representations of integers and floating point numbers. Integers are represented using binary and either sign-magnitude or two's complement, with two's complement being more commonly used. Floating point numbers use a sign bit, significand (mantissa), and exponent to represent values with a fixed or floating binary point. The IEEE 754 standard defines common floating point formats. Arithmetic operations like addition, subtraction, multiplication, and division are performed on these representations in the ALU.
The document discusses integer representation in computers. It explains that integers are stored using binary and discusses two common methods: sign-magnitude and two's complement. It describes how positive and negative numbers are represented using each method and explains the benefits of two's complement representation, which includes only one representation of zero and simple arithmetic. The document also discusses the range of numbers that can be represented using 8-bit and 16-bit two's complement and how negation is performed in two's complement.
The document summarizes computer arithmetic and floating point representation. It discusses:
1) The arithmetic logic unit handles integer and floating point calculations. Integer values are represented in binary using two's complement. Floating point values use a sign-magnitude format with a fixed or moving binary point.
2) Addition and subtraction of integers is done through normal binary addition and subtraction. Multiplication requires generating partial products and addition. Division uses a long division approach.
3) Floating point numbers follow the IEEE 754 standard which represents values as ±mantissax2exponent in 32 or 64 bit formats. Arithmetic requires aligning operands and performing operations on significands and exponents.
The document summarizes computer arithmetic and the arithmetic logic unit (ALU). It discusses:
1) The ALU handles integer and floating point calculations. It may have a separate floating point unit.
2) There are different methods for representing integers like sign-magnitude and two's complement. Two's complement is commonly used.
3) Floating point numbers use a sign, significand, and exponent to represent real numbers in a normalized format like ±.significand × 2exponent.
The document summarizes computer arithmetic and the arithmetic logic unit (ALU). It discusses:
1) The ALU handles integer and floating point calculations. It may have a separate floating point unit.
2) There are different methods for representing integers like sign-magnitude and two's complement. Two's complement is commonly used.
3) Floating point numbers use a sign, significand, and exponent to represent real numbers in a normalized format like ±.significand × 2exponent.
Computer organization and architecture lab manual Shankar Gangaju
This document contains information about a computer organization and architecture lab. It includes details about the lab report format, integer representation using fixed point, two's complement, addition and subtraction algorithms and hardware. It also discusses logical operations, and provides MATLAB code examples for 4-bit binary addition, subtraction, multiplication and restoring division algorithms.
This document provides an overview of number systems covered in a digital logic design course. It discusses decimal, binary, hexadecimal, and octal number systems. For binary numbers, it describes conversion between decimal and binary, signed numbers using 1's and 2's complement representation, and arithmetic operations like addition, subtraction, multiplication, and division on signed binary numbers.
1. The document describes the von Neumann architecture and its key components including the ALU, control unit, memory and I/O devices.
2. It explains the structure of the von Neumann machine and details the functions of components like the program counter, memory address register, and instruction register.
3. The document covers integer and floating point representation in binary, including sign-magnitude, two's complement, and IEEE 754 standard. It describes arithmetic operations like addition, subtraction, multiplication and division on binary numbers.
BOOTH ALGO, DIVISION(RESTORING _ NON RESTORING) etc etcAbhishek Rajpoot
The document discusses various aspects of central processing unit (CPU) architecture and arithmetic operations. It covers the main components of a CPU - the arithmetic logic unit (ALU), control unit, and registers. It then describes different data representation methods including fixed-point and floating-point numbers. Various arithmetic operations for both types of numbers such as addition, subtraction, multiplication, and division are explained. Different adder designs like ripple-carry adder and carry lookahead adder are also summarized.
The document discusses the Arithmetic Logic Unit (ALU) and how it handles integer and floating point arithmetic in a computer. The ALU performs calculations and is supported by other parts of the computer. It describes how integers are represented in binary using sign-magnitude and two's complement methods. Two's complement allows for easier arithmetic operations. Floating point numbers use a sign bit, exponent field, and significand to represent values with a fixed or floating decimal point. IEEE 754 standard defines common floating point formats. The ALU performs operations on operands from registers and stores results back in registers.
The document introduces computer architecture and system software. It discusses the differences between computer organization and computer architecture. It describes the basic components of a computer based on the Von Neumann architecture, which consists of four main sub-systems: memory, ALU, control unit, and I/O. The document also discusses bottlenecks of the Von Neumann architecture and differences between microprocessors and microcontrollers. It covers computer arithmetic concepts like integer representation, floating point representation using IEEE 754 standard, and number bases conversion. Additional topics include binary operations like addition, subtraction using complements, and multiplication algorithms like Booth's multiplication.
The document discusses different number systems including binary, octal, decimal, and hexadecimal. It explains that number systems have a radix or base, which determines the set of symbols used and their positional values. The key representations for binary numbers discussed are sign-magnitude, one's complement, and two's complement, which provide different methods for representing positive and negative numbers. The document provides examples of addition, subtraction, multiplication, and division operations in binary.
The document provides an exam schedule for the week of July 19-24 listing the courses being tested each day. On Thursday, July 19 exams are scheduled for Computer Fundamentals and Physics lab. Friday has exams for English and Calculus. Monday's exam is for Humanities. Tuesday's exams are for PE and Sociology/Anthropology.
The document discusses arithmetic unit operations including addition, subtraction, multiplication, and division of signed and unsigned numbers. It covers integer representation using sign-magnitude, one's complement, and two's complement methods. Two's complement is identified as the best approach for integer representation as it simplifies arithmetic operations and overflow handling. The key hardware components for performing addition and subtraction of signed integers are also summarized.
In two's complement notation:
- The most significant bit represents the sign, with 0 indicating positive and 1 indicating negative.
- The remaining bits represent the magnitude, with the value determined by their place values.
- Negative numbers are calculated by taking the two's complement of the corresponding positive number.
- Two's complement notation allows for simple addition and subtraction operations on signed binary numbers.
This document summarizes different number systems used in computing including binary, octal, decimal, and hexadecimal. It explains how to convert between these number systems using theorems about their bases. Key topics covered include binary arithmetic, signed and unsigned integer representation, and how floating point numbers and characters are stored in binary format. Conversion charts are provided for binary to octal and hexadecimal. Representations of integers, characters, and floating point numbers in binary are also summarized.
Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.
Organisasi dan Arsitektur Komputer MO-08EriekOrlando
Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer Organisasi dan Arsitektur Komputer
The Arithmetic Logic Unit (ALU) performs calculations in a computer using binary representations of integers and floating point numbers. Integers are represented using binary and either sign-magnitude or two's complement, with two's complement being more commonly used. Floating point numbers use a sign bit, significand (mantissa), and exponent to represent values with a fixed or floating binary point. The IEEE 754 standard defines common floating point formats. Arithmetic operations like addition, subtraction, multiplication, and division are performed on these representations in the ALU.
The document discusses integer representation in computers. It explains that integers are stored using binary and discusses two common methods: sign-magnitude and two's complement. It describes how positive and negative numbers are represented using each method and explains the benefits of two's complement representation, which includes only one representation of zero and simple arithmetic. The document also discusses the range of numbers that can be represented using 8-bit and 16-bit two's complement and how negation is performed in two's complement.
The document summarizes computer arithmetic and floating point representation. It discusses:
1) The arithmetic logic unit handles integer and floating point calculations. Integer values are represented in binary using two's complement. Floating point values use a sign-magnitude format with a fixed or moving binary point.
2) Addition and subtraction of integers is done through normal binary addition and subtraction. Multiplication requires generating partial products and addition. Division uses a long division approach.
3) Floating point numbers follow the IEEE 754 standard which represents values as ±mantissax2exponent in 32 or 64 bit formats. Arithmetic requires aligning operands and performing operations on significands and exponents.
The document summarizes computer arithmetic and the arithmetic logic unit (ALU). It discusses:
1) The ALU handles integer and floating point calculations. It may have a separate floating point unit.
2) There are different methods for representing integers like sign-magnitude and two's complement. Two's complement is commonly used.
3) Floating point numbers use a sign, significand, and exponent to represent real numbers in a normalized format like ±.significand × 2exponent.
The document summarizes computer arithmetic and the arithmetic logic unit (ALU). It discusses:
1) The ALU handles integer and floating point calculations. It may have a separate floating point unit.
2) There are different methods for representing integers like sign-magnitude and two's complement. Two's complement is commonly used.
3) Floating point numbers use a sign, significand, and exponent to represent real numbers in a normalized format like ±.significand × 2exponent.
Computer organization and architecture lab manual Shankar Gangaju
This document contains information about a computer organization and architecture lab. It includes details about the lab report format, integer representation using fixed point, two's complement, addition and subtraction algorithms and hardware. It also discusses logical operations, and provides MATLAB code examples for 4-bit binary addition, subtraction, multiplication and restoring division algorithms.
This document provides an overview of number systems covered in a digital logic design course. It discusses decimal, binary, hexadecimal, and octal number systems. For binary numbers, it describes conversion between decimal and binary, signed numbers using 1's and 2's complement representation, and arithmetic operations like addition, subtraction, multiplication, and division on signed binary numbers.
1. The document describes the von Neumann architecture and its key components including the ALU, control unit, memory and I/O devices.
2. It explains the structure of the von Neumann machine and details the functions of components like the program counter, memory address register, and instruction register.
3. The document covers integer and floating point representation in binary, including sign-magnitude, two's complement, and IEEE 754 standard. It describes arithmetic operations like addition, subtraction, multiplication and division on binary numbers.
BOOTH ALGO, DIVISION(RESTORING _ NON RESTORING) etc etcAbhishek Rajpoot
The document discusses various aspects of central processing unit (CPU) architecture and arithmetic operations. It covers the main components of a CPU - the arithmetic logic unit (ALU), control unit, and registers. It then describes different data representation methods including fixed-point and floating-point numbers. Various arithmetic operations for both types of numbers such as addition, subtraction, multiplication, and division are explained. Different adder designs like ripple-carry adder and carry lookahead adder are also summarized.
The document discusses the Arithmetic Logic Unit (ALU) and how it handles integer and floating point arithmetic in a computer. The ALU performs calculations and is supported by other parts of the computer. It describes how integers are represented in binary using sign-magnitude and two's complement methods. Two's complement allows for easier arithmetic operations. Floating point numbers use a sign bit, exponent field, and significand to represent values with a fixed or floating decimal point. IEEE 754 standard defines common floating point formats. The ALU performs operations on operands from registers and stores results back in registers.
The document introduces computer architecture and system software. It discusses the differences between computer organization and computer architecture. It describes the basic components of a computer based on the Von Neumann architecture, which consists of four main sub-systems: memory, ALU, control unit, and I/O. The document also discusses bottlenecks of the Von Neumann architecture and differences between microprocessors and microcontrollers. It covers computer arithmetic concepts like integer representation, floating point representation using IEEE 754 standard, and number bases conversion. Additional topics include binary operations like addition, subtraction using complements, and multiplication algorithms like Booth's multiplication.
The document discusses different number systems including binary, octal, decimal, and hexadecimal. It explains that number systems have a radix or base, which determines the set of symbols used and their positional values. The key representations for binary numbers discussed are sign-magnitude, one's complement, and two's complement, which provide different methods for representing positive and negative numbers. The document provides examples of addition, subtraction, multiplication, and division operations in binary.
The document provides an exam schedule for the week of July 19-24 listing the courses being tested each day. On Thursday, July 19 exams are scheduled for Computer Fundamentals and Physics lab. Friday has exams for English and Calculus. Monday's exam is for Humanities. Tuesday's exams are for PE and Sociology/Anthropology.
The document discusses arithmetic unit operations including addition, subtraction, multiplication, and division of signed and unsigned numbers. It covers integer representation using sign-magnitude, one's complement, and two's complement methods. Two's complement is identified as the best approach for integer representation as it simplifies arithmetic operations and overflow handling. The key hardware components for performing addition and subtraction of signed integers are also summarized.
In two's complement notation:
- The most significant bit represents the sign, with 0 indicating positive and 1 indicating negative.
- The remaining bits represent the magnitude, with the value determined by their place values.
- Negative numbers are calculated by taking the two's complement of the corresponding positive number.
- Two's complement notation allows for simple addition and subtraction operations on signed binary numbers.
This document summarizes different number systems used in computing including binary, octal, decimal, and hexadecimal. It explains how to convert between these number systems using theorems about their bases. Key topics covered include binary arithmetic, signed and unsigned integer representation, and how floating point numbers and characters are stored in binary format. Conversion charts are provided for binary to octal and hexadecimal. Representations of integers, characters, and floating point numbers in binary are also summarized.
Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.
DeFAIMint | 🤖Mint to DeFAI. Vibe Trading as NFTKyohei Ito
DeFAI Mint: Vive Trading as NFT.
Welcome to the future of crypto investing — radically simplified.
"DeFAI Mint" is a new frontier in the intersection of DeFi and AI.
At its core lies a simple idea: what if _minting one NFT_ could replace everything else? No tokens to pick.
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Just one action — mint — and your belief becomes an AI-powered investing agent.
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In a market where over 140,000 tokens launch daily, and only experts can keep up with the volatility.
DeFAI Mint offers a new paradigm: "Vibe Trading".
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Each DeFAI NFT carries a belief — political, philosophical, or protocol-based.
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- It owns its own wallet
- It signs and sends transactions
- It trades across chains, aligned with your chosen thesis
This is "belief-driven automation". Built to be safe. Built to be effortless.
- Your trade budget is fixed at mint
- Every NFT wallet is isolated — no exposure beyond your mint
- Login with Twitter — no crypto wallet needed
- No \$SOL required — minting is seamless
- Fully autonomous, fully on-chain execution
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Under the hood, DeFAI Mint runs on "Solana’s native execution layer", not just as an app — but as a system-level innovation:
- "Metaplex Execute" empowers NFTs to act as wallets
- "Solana Agent Kit v2" turns them into full-spectrum actors
- Data and strategies are stored on distributed storage (Walrus)
Other chains can try to replicate this.
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The main purpose of the current study was to formulate an empirical expression for predicting the axial compression capacity and axial strain of concrete-filled plastic tubular specimens (CFPT) using the artificial neural network (ANN). A total of seventy-two experimental test data of CFPT and unconfined concrete were used for training, testing, and validating the ANN models. The ANN axial strength and strain predictions were compared with the experimental data and predictions from several existing strength models for fiber-reinforced polymer (FRP)-confined concrete. Five statistical indices were used to determine the performance of all models considered in the present study. The statistical evaluation showed that the ANN model was more effective and precise than the other models in predicting the compressive strength, with 2.8% AA error, and strain at peak stress, with 6.58% AA error, of concrete-filled plastic tube tested under axial compression load. Similar lower values were obtained for the NRMSE index.
David Boutry - Specializes In AWS, Microservices And PythonDavid Boutry
With over eight years of experience, David Boutry specializes in AWS, microservices, and Python. As a Senior Software Engineer in New York, he spearheaded initiatives that reduced data processing times by 40%. His prior work in Seattle focused on optimizing e-commerce platforms, leading to a 25% sales increase. David is committed to mentoring junior developers and supporting nonprofit organizations through coding workshops and software development.
The TRB AJE35 RIIM Coordination and Collaboration Subcommittee has organized a series of webinars focused on building coordination, collaboration, and cooperation across multiple groups. All webinars have been recorded and copies of the recording, transcripts, and slides are below. These resources are open-access following creative commons licensing agreements. The files may be found, organized by webinar date, below. The committee co-chairs would welcome any suggestions for future webinars. The support of the AASHTO RAC Coordination and Collaboration Task Force, the Council of University Transportation Centers, and AUTRI’s Alabama Transportation Assistance Program is gratefully acknowledged.
This webinar overviews proven methods for collaborating with USDOT University Transportation Centers (UTCs), emphasizing state departments of transportation and other stakeholders. It will cover partnerships at all UTC stages, from the Notice of Funding Opportunity (NOFO) release through proposal development, research and implementation. Successful USDOT UTC research, education, workforce development, and technology transfer best practices will be highlighted. Dr. Larry Rilett, Director of the Auburn University Transportation Research Institute will moderate.
For more information, visit: https://aub.ie/trbwebinars
Jacob Murphy Australia - Excels In Optimizing Software ApplicationsJacob Murphy Australia
In the world of technology, Jacob Murphy Australia stands out as a Junior Software Engineer with a passion for innovation. Holding a Bachelor of Science in Computer Science from Columbia University, Jacob's forte lies in software engineering and object-oriented programming. As a Freelance Software Engineer, he excels in optimizing software applications to deliver exceptional user experiences and operational efficiency. Jacob thrives in collaborative environments, actively engaging in design and code reviews to ensure top-notch solutions. With a diverse skill set encompassing Java, C++, Python, and Agile methodologies, Jacob is poised to be a valuable asset to any software development team.
Deepfake Phishing: A New Frontier in Cyber ThreatsRaviKumar256934
n today’s hyper-connected digital world, cybercriminals continue to develop increasingly sophisticated methods of deception. Among these, deepfake phishing represents a chilling evolution—a combination of artificial intelligence and social engineering used to exploit trust and compromise security.
Deepfake technology, once a novelty used in entertainment, has quickly found its way into the toolkit of cybercriminals. It allows for the creation of hyper-realistic synthetic media, including images, audio, and videos. When paired with phishing strategies, deepfakes can become powerful weapons of fraud, impersonation, and manipulation.
This document explores the phenomenon of deepfake phishing, detailing how it works, why it’s dangerous, and how individuals and organizations can defend themselves against this emerging threat.
OPTIMIZING DATA INTEROPERABILITY IN AGILE ORGANIZATIONS: INTEGRATING NONAKA’S...ijdmsjournal
Agile methodologies have transformed organizational management by prioritizing team autonomy and
iterative learning cycles. However, these approaches often lack structured mechanisms for knowledge
retention and interoperability, leading to fragmented decision-making, information silos, and strategic
misalignment. This study proposes an alternative approach to knowledge management in Agile
environments by integrating Ikujiro Nonaka and Hirotaka Takeuchi’s theory of knowledge creation—
specifically the concept of Ba, a shared space where knowledge is created and validated—with Jürgen
Habermas’s Theory of Communicative Action, which emphasizes deliberation as the foundation for trust
and legitimacy in organizational decision-making. To operationalize this integration, we propose the
Deliberative Permeability Metric (DPM), a diagnostic tool that evaluates knowledge flow and the
deliberative foundation of organizational decisions, and the Communicative Rationality Cycle (CRC), a
structured feedback model that extends the DPM, ensuring long-term adaptability and data governance.
This model was applied at Livelo, a Brazilian loyalty program company, demonstrating that structured
deliberation improves operational efficiency and reduces knowledge fragmentation. The findings indicate
that institutionalizing deliberative processes strengthens knowledge interoperability, fostering a more
resilient and adaptive approach to data governance in complex organizations.
2. Key Points
Two principle concerns for computer
arithmetic
• the way in which numbers are
represented
• the algorithms used for the basic
arithmetic operations
These two considerations apply both to integer
and floating point arithmetic
3. part of the computer that actually
performs arithmetic and logical
operations on data.
Handles integers
May handle floating point (real)
numbers
Arithmetic & Logic Unit (ALU)
5. Integer Representation
Only have 0 & 1 to represent everything
Positive numbers stored in binary
e.g. 41=00101001
No minus sign
No period
Sign-Magnitude
Two’s complement
6. Sign-Magnitude
Left most bit is sign bit
0 means positive
1 means negative
+18 = 00010010
-18 = 10010010
Problems
Need to consider both sign and magnitude in
arithmetic
Two representations of zero (+0 and -0)
8. Benefits of 2’s complement
One representation of zero
Arithmetic works easily (see later)
Negating is fairly easy
3 = 00000011
Boolean complement gives 11111100
Add 1 to LSB 11111101
10. Negation Special Case 1
0 = 00000000
Bitwise not 11111111
Add 1 to LSB +1
Result 1 00000000
Overflow is ignored, so:
- 0 = 0
11. Negation Special Case 2
-128 = 10000000
bitwise not 01111111
Add 1 to LSB +1
Result 10000000
So:
-(-128) = -128 X
Monitor MSB (sign bit)
It should change during negation
12. Range of Numbers
8 bit 2’s complement
+127 = 01111111 = 2 -1
-128 = 10000000 = -2
16 bit 2’s complement
+32767 = 011111111 11111111 = 2 - 1
-32768 = 100000000 00000000 = - 2
13. Conversion Between Lengths
Positive number pack with leading zeros
+18 = 00010010(sign magnitude, 8bits)
+18 = 00000000 00010010(sign magnitude,16bits)
Negative numbers pack with leading ones
-18 = 10010010(sign magnitude, 8bits)
-18 = 10000000 10010010(sign magnitude,16bits)
i.e. pack with MSB (sign bit)
14. Addition and Subtraction
Normal binary addition
Monitor sign bit for overflow
Take two’s complement of subtrahend and
add to minuend
i.e. a - b = a + (-b)
So we only need addition and complement
circuits
17. Multiplication Example
1011 Multiplicand (11 dec)
x 1101 Multiplier (13 dec)
1011 Partial products
0000
1011
1011
10001111 Product (143 dec)
Note: need double length result
if multiplier bit is 1 copy
otherwise zero