Logic teaches important fundamentals like relation and deductive reasoning that have applications beyond mathematics. It distinguishes between valid reasoning and fallacies. Free demo classes are available to learn the fundamentals of logic.
Scott Wen-Tau Yih will give a talk titled "Learning with Integer Linear Programming Inference for Constrained Output". The talk will first demonstrate how constraints can be incorporated into conditional random fields using a novel inference approach based on integer linear programming. This allows CRF models to efficiently support general constraint structures. Experimental results will be provided for semantic role labeling. The second part will compare simple learning plus inference to inference based training, finding the latter is superior when local classifiers are difficult but requires more examples to show differences.
This paper introduces approaches to combining logic, probability, and learning. It discusses past attempts to solve probabilistic logic learning and overviews different formalisms for defining probabilities on logical views. It also describes approaches that combine probabilistic reasoning and logical representation, such as Bayesian logic programs and probabilistic relational models. Learning probabilistic logics involves adapting probabilistic models based on data, including tasks of parameter estimation and structure learning. The paper provides an integrated survey of various concepts in this area.
This document provides an introduction to soft computing. Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. The key components of soft computing include neural networks, fuzzy logic, and genetic algorithms. The goals of soft computing are to develop intelligent machines to solve real-world problems that may not have ideal or mathematically modeled solutions, while achieving practicality, robustness, and low cost. Soft computing uses techniques like machine learning, evolutionary computation, and artificial neural networks to approach problems that traditional computing cannot always solve.
How the philosophy of mathematical practice can be logic by other means (bris...Brendan Larvor
ย
The document discusses the author's view that informal proofs in mathematics depend on both logical form and content. The author argues that logic should be understood as the study of inferential actions, which can incorporate content and representations. This broader view of logic facilitates connecting logical questions about rigor to the study of mathematical cultures and practices, since logical constraints are enacted as cultural norms. The author claims this approach is needed to address shortcomings in using formal logic to model mathematical proof and to utilize studies of specific mathematical practices.
Are you a student from computer science and looking for best <a>gate coaching</a> institute then the best option is Gate Networks? Join <a>gate networks</a> for getting success in gate coaching for computer science.
web url : http://gatenetworks.co.in/gate-syllabus-for-computer-science/
This document discusses different methods of representing text semantics, including propositional semantics which converts text to logical formulas and vector representations which embeds text in a high-dimensional space. It also covers different general knowledge representations such as logical, production rule, semantic network, and description logic representations. Finally, it describes propositional and predicate logic in more detail, explaining their syntax, semantics, and how predicate logic builds upon propositional logic by allowing properties and relations between objects.
Scott Wen-Tau Yih will give a talk titled "Learning with Integer Linear Programming Inference for Constrained Output". The talk will first demonstrate how constraints can be incorporated into conditional random fields using a novel inference approach based on integer linear programming. This allows CRF models to efficiently support general constraint structures. Experimental results will be provided for semantic role labeling. The second part will compare simple learning plus inference to inference based training, finding the latter is superior when local classifiers are difficult but requires more examples to show differences.
This paper introduces approaches to combining logic, probability, and learning. It discusses past attempts to solve probabilistic logic learning and overviews different formalisms for defining probabilities on logical views. It also describes approaches that combine probabilistic reasoning and logical representation, such as Bayesian logic programs and probabilistic relational models. Learning probabilistic logics involves adapting probabilistic models based on data, including tasks of parameter estimation and structure learning. The paper provides an integrated survey of various concepts in this area.
This document provides an introduction to soft computing. Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. The key components of soft computing include neural networks, fuzzy logic, and genetic algorithms. The goals of soft computing are to develop intelligent machines to solve real-world problems that may not have ideal or mathematically modeled solutions, while achieving practicality, robustness, and low cost. Soft computing uses techniques like machine learning, evolutionary computation, and artificial neural networks to approach problems that traditional computing cannot always solve.
How the philosophy of mathematical practice can be logic by other means (bris...Brendan Larvor
ย
The document discusses the author's view that informal proofs in mathematics depend on both logical form and content. The author argues that logic should be understood as the study of inferential actions, which can incorporate content and representations. This broader view of logic facilitates connecting logical questions about rigor to the study of mathematical cultures and practices, since logical constraints are enacted as cultural norms. The author claims this approach is needed to address shortcomings in using formal logic to model mathematical proof and to utilize studies of specific mathematical practices.
Are you a student from computer science and looking for best <a>gate coaching</a> institute then the best option is Gate Networks? Join <a>gate networks</a> for getting success in gate coaching for computer science.
web url : http://gatenetworks.co.in/gate-syllabus-for-computer-science/
This document discusses different methods of representing text semantics, including propositional semantics which converts text to logical formulas and vector representations which embeds text in a high-dimensional space. It also covers different general knowledge representations such as logical, production rule, semantic network, and description logic representations. Finally, it describes propositional and predicate logic in more detail, explaining their syntax, semantics, and how predicate logic builds upon propositional logic by allowing properties and relations between objects.
The document introduces the Word-Sensibility Model as a way to represent commonsense knowledge for AI. It consists of several components, including quadranyms, micro-topics, and an ecological perspective. Quadranyms are four-part constructs that represent virtual units of orientation and constraint. Micro-topics help organize lexical information and abstract human contextual expectations. The model takes an ecological view of representing dynamic relationships between an agent's internal responses and external occurrences across different contextual levels.
This document summarizes the career and work of Valeria de Paiva, a mathematician who specializes in category theory, logic, and theoretical computer science. She has a PhD in pure mathematics from Cambridge University and has held professor and industrial researcher positions in the UK. Her research uses mathematical tools like category theory and proof theory to build computational models, including for theorem provers, authentication systems, programming languages, and natural language understanding. She now works on using logic to represent the meaning of natural language sentences.
This document discusses neuro fuzzy systems and soft computing. It provides the following key points:
1. Neuro-fuzzy systems combine fuzzy logic and neural networks, allowing the system to learn from data and maintain interpretable fuzzy rules. It can be viewed as a 3-layer neural network with fuzzy rules in the hidden layer.
2. Soft computing uses techniques like neural networks, fuzzy logic, and genetic algorithms to handle real-world problems involving uncertainty, ambiguity, and imprecision. It aims to build intelligent systems that can learn from experience.
3. Soft computing constituents include neural networks, fuzzy sets, approximate reasoning, and derivative-free optimization methods like genetic algorithms and simulated annealing. These work together to enable learning
Connectionist and Dynamical Systems approach to Cognitioncruzin008
ย
The document discusses connectionist and probabilistic models of cognition. It presents arguments against purely probabilistic models, arguing they do not account for cognitive development and may use incorrect representational units. It also summarizes examples of connectionist models in language, development, and semantics that aim to model emergent phenomena from low-level neural connections. The authors advocate an integrated approach informed by neural mechanisms over abstract probabilistic models alone.
Representation of syntax, semantics and Predicate logicschauhankapil
ย
This document discusses different methods for representing syntax, semantics, and predicate logic in natural language processing. It introduces propositional and formal semantics which represent text as logical formulas, as well as vector representations which embed text in multi-dimensional space. Various logical representations are examined including propositional logic, predicate logic, production rules, semantic networks, and description logics.
Fuzzy ARTMAP is a neural network architecture that uses fuzzy logic and adaptive resonance theory (ART) for supervised learning. It incorporates two fuzzy ART modules, ART-a and ART-b, linked together by an inter-ART module called the MAP field. This allows the network to form predictive associations between categories and track matches using a mechanism called match tracking. The match tracking recognizes category structures to avoid repeating predictive errors on subsequent inputs. Fuzzy ARTMAP is trained until it can correctly classify all training data by increasing the vigilance parameter of ART-a in response to predictive mismatches at ART-b.
Revisiting Information Hiding - Reflections on Classical and Nonclassical M...Klaus Ostermann
ย
This document discusses classical and non-classical approaches to modularity in software engineering. It relates the dichotomy of classical vs non-classical modularity to a similar dichotomy in logic. The key pillars of classical modularity, such as information hiding, abstraction, compositionality and monotonicity, are described and related to classical logic. Non-classical aspects of programming like inductive reasoning, default logic and negation as failure are discussed. The document argues that classical modularity breaks down for large complex software systems due to issues like tangled concerns, evolution and incomplete specifications. It suggests programs are more like overlapping models than scientific models.
Dale Schuurmans will discuss a new discriminative, convex training algorithm for hidden Markov models that avoids EM. Experimental results show it produces better conditional models than Baum-Welch training. He will also discuss how convex relaxation can derive effective algorithms for outlier detection, Bayesian network structure learning, and other hard machine learning problems. Dale Schuurmans is a Professor of Computing Science and Canada Research Chair in Machine Learning at the University of Alberta.
This document provides an introduction and overview of a course on data structures and algorithms. It discusses the importance and fundamental nature of the topics covered in the course. The course will focus on commonly used data structures like lists, trees, and graphs, as well as related algorithms for tasks like searching, sorting, and graph operations. It will also cover reasoning about the correctness and efficiency of algorithms. The document provides recommendations for textbooks to reference and related courses that provide useful background knowledge.
This course provides an introduction to the theory of computation, including formal models of computation and their relationships to formal languages. Key topics covered include automata, computability, and complexity. Students will learn about regular languages and context-free grammars, as well as limitations of computation like unsolvable problems. The goal is for students to strengthen their mathematical reasoning skills and understand the inherent capabilities and limitations of computers. Assessment includes exams, assignments, and projects. Prerequisites include discrete mathematics.
The document presents X-SOM, a flexible ontology mapping system. It uses a combination of matching algorithms and a neural network to find similarities between ontologies. It then uses a debugging process to ensure the mappings are semantically consistent and do not introduce inconsistencies. The system was tested on the OAEI 2007 benchmarks and was able to improve precision and recall over simple averaging techniques. Future work is focused on expanding the types of mappings supported and improving matching algorithms.
Soft-computing refers to computational techniques that study and analyze complex phenomena for which conventional methods have not provided low-cost or complete solutions. It includes fuzzy logic, evolutionary computation, neural networks, Bayesian networks, support vector machines, and hybrid systems. Soft-computing techniques are robust, tolerant of imprecise data, and resemble biological processes more than traditional logical techniques. They provide useful approximations to intractable problems rather than exact solutions.
Data Mining - Separating Fact From Fiction - NetIKXTony Hirst
ย
This document discusses various topics related to data mining including knowledge discovery in databases, pattern and structure identification, seasonal trends analysis, relationships, identity problems, partial string matching, Simpson's paradox, anomaly detection, predictive vs descriptive tasks, supervised and unsupervised machine learning algorithms, and the consequences and ethics of algorithms. It provides an overview of key concepts and techniques in data mining.
FCA-MERGE: Bottom-Up Merging of Ontologiesalemarrena
ย
The document describes a new bottom-up method called FCA-MERGE for merging ontologies. It extracts instances from documents for each ontology to generate formal contexts. It then merges the contexts and computes a concept lattice using techniques from Formal Concept Analysis. This lattice provides a structural description of the merging process. The final merged ontology is then generated from the lattice with human guidance. FCA-MERGE circumvents the problem of finding instances classified in both ontologies by extracting instances from relevant documents.
The document provides an overview of the topics covered in the TSPGECET-2018 exam for the Computer Science and Information Technology code. The topics are grouped into Engineering Mathematics, Computer Science and Information Technology, and include subjects like discrete mathematics, linear algebra, calculus, probability, digital logic, computer organization, programming and data structures, algorithms, theory of computation, compiler design, operating systems, databases, computer networks, and software engineering.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
ย
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
The document introduces the Word-Sensibility Model as a way to represent commonsense knowledge for AI. It consists of several components, including quadranyms, micro-topics, and an ecological perspective. Quadranyms are four-part constructs that represent virtual units of orientation and constraint. Micro-topics help organize lexical information and abstract human contextual expectations. The model takes an ecological view of representing dynamic relationships between an agent's internal responses and external occurrences across different contextual levels.
This document summarizes the career and work of Valeria de Paiva, a mathematician who specializes in category theory, logic, and theoretical computer science. She has a PhD in pure mathematics from Cambridge University and has held professor and industrial researcher positions in the UK. Her research uses mathematical tools like category theory and proof theory to build computational models, including for theorem provers, authentication systems, programming languages, and natural language understanding. She now works on using logic to represent the meaning of natural language sentences.
This document discusses neuro fuzzy systems and soft computing. It provides the following key points:
1. Neuro-fuzzy systems combine fuzzy logic and neural networks, allowing the system to learn from data and maintain interpretable fuzzy rules. It can be viewed as a 3-layer neural network with fuzzy rules in the hidden layer.
2. Soft computing uses techniques like neural networks, fuzzy logic, and genetic algorithms to handle real-world problems involving uncertainty, ambiguity, and imprecision. It aims to build intelligent systems that can learn from experience.
3. Soft computing constituents include neural networks, fuzzy sets, approximate reasoning, and derivative-free optimization methods like genetic algorithms and simulated annealing. These work together to enable learning
Connectionist and Dynamical Systems approach to Cognitioncruzin008
ย
The document discusses connectionist and probabilistic models of cognition. It presents arguments against purely probabilistic models, arguing they do not account for cognitive development and may use incorrect representational units. It also summarizes examples of connectionist models in language, development, and semantics that aim to model emergent phenomena from low-level neural connections. The authors advocate an integrated approach informed by neural mechanisms over abstract probabilistic models alone.
Representation of syntax, semantics and Predicate logicschauhankapil
ย
This document discusses different methods for representing syntax, semantics, and predicate logic in natural language processing. It introduces propositional and formal semantics which represent text as logical formulas, as well as vector representations which embed text in multi-dimensional space. Various logical representations are examined including propositional logic, predicate logic, production rules, semantic networks, and description logics.
Fuzzy ARTMAP is a neural network architecture that uses fuzzy logic and adaptive resonance theory (ART) for supervised learning. It incorporates two fuzzy ART modules, ART-a and ART-b, linked together by an inter-ART module called the MAP field. This allows the network to form predictive associations between categories and track matches using a mechanism called match tracking. The match tracking recognizes category structures to avoid repeating predictive errors on subsequent inputs. Fuzzy ARTMAP is trained until it can correctly classify all training data by increasing the vigilance parameter of ART-a in response to predictive mismatches at ART-b.
Revisiting Information Hiding - Reflections on Classical and Nonclassical M...Klaus Ostermann
ย
This document discusses classical and non-classical approaches to modularity in software engineering. It relates the dichotomy of classical vs non-classical modularity to a similar dichotomy in logic. The key pillars of classical modularity, such as information hiding, abstraction, compositionality and monotonicity, are described and related to classical logic. Non-classical aspects of programming like inductive reasoning, default logic and negation as failure are discussed. The document argues that classical modularity breaks down for large complex software systems due to issues like tangled concerns, evolution and incomplete specifications. It suggests programs are more like overlapping models than scientific models.
Dale Schuurmans will discuss a new discriminative, convex training algorithm for hidden Markov models that avoids EM. Experimental results show it produces better conditional models than Baum-Welch training. He will also discuss how convex relaxation can derive effective algorithms for outlier detection, Bayesian network structure learning, and other hard machine learning problems. Dale Schuurmans is a Professor of Computing Science and Canada Research Chair in Machine Learning at the University of Alberta.
This document provides an introduction and overview of a course on data structures and algorithms. It discusses the importance and fundamental nature of the topics covered in the course. The course will focus on commonly used data structures like lists, trees, and graphs, as well as related algorithms for tasks like searching, sorting, and graph operations. It will also cover reasoning about the correctness and efficiency of algorithms. The document provides recommendations for textbooks to reference and related courses that provide useful background knowledge.
This course provides an introduction to the theory of computation, including formal models of computation and their relationships to formal languages. Key topics covered include automata, computability, and complexity. Students will learn about regular languages and context-free grammars, as well as limitations of computation like unsolvable problems. The goal is for students to strengthen their mathematical reasoning skills and understand the inherent capabilities and limitations of computers. Assessment includes exams, assignments, and projects. Prerequisites include discrete mathematics.
The document presents X-SOM, a flexible ontology mapping system. It uses a combination of matching algorithms and a neural network to find similarities between ontologies. It then uses a debugging process to ensure the mappings are semantically consistent and do not introduce inconsistencies. The system was tested on the OAEI 2007 benchmarks and was able to improve precision and recall over simple averaging techniques. Future work is focused on expanding the types of mappings supported and improving matching algorithms.
Soft-computing refers to computational techniques that study and analyze complex phenomena for which conventional methods have not provided low-cost or complete solutions. It includes fuzzy logic, evolutionary computation, neural networks, Bayesian networks, support vector machines, and hybrid systems. Soft-computing techniques are robust, tolerant of imprecise data, and resemble biological processes more than traditional logical techniques. They provide useful approximations to intractable problems rather than exact solutions.
Data Mining - Separating Fact From Fiction - NetIKXTony Hirst
ย
This document discusses various topics related to data mining including knowledge discovery in databases, pattern and structure identification, seasonal trends analysis, relationships, identity problems, partial string matching, Simpson's paradox, anomaly detection, predictive vs descriptive tasks, supervised and unsupervised machine learning algorithms, and the consequences and ethics of algorithms. It provides an overview of key concepts and techniques in data mining.
FCA-MERGE: Bottom-Up Merging of Ontologiesalemarrena
ย
The document describes a new bottom-up method called FCA-MERGE for merging ontologies. It extracts instances from documents for each ontology to generate formal contexts. It then merges the contexts and computes a concept lattice using techniques from Formal Concept Analysis. This lattice provides a structural description of the merging process. The final merged ontology is then generated from the lattice with human guidance. FCA-MERGE circumvents the problem of finding instances classified in both ontologies by extracting instances from relevant documents.
The document provides an overview of the topics covered in the TSPGECET-2018 exam for the Computer Science and Information Technology code. The topics are grouped into Engineering Mathematics, Computer Science and Information Technology, and include subjects like discrete mathematics, linear algebra, calculus, probability, digital logic, computer organization, programming and data structures, algorithms, theory of computation, compiler design, operating systems, databases, computer networks, and software engineering.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
ย
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
ย
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
ย
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐๐ ๐๐ฎ๐ซ๐ซ๐ข๐๐ฎ๐ฅ๐ฎ๐ฆ ๐ข๐ง ๐ญ๐ก๐ ๐๐ก๐ข๐ฅ๐ข๐ฉ๐ฉ๐ข๐ง๐๐ฌ:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
ย
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the bodyโs response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
ย
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
2. Importance of fundamentals of logic:-
Logic is an important subject because it
teaches relation. This has far reaching
effects beyond mathematics, where it is
often studied. It teaches deductive
reasoning, such as the difference between
reason and fallacy.