An abstract fixed-point theorem for Horn formula equations

This chapter describes four interdisciplinary fields originated and defined by Ashu M. G. Solo in 2011 called political engineering, public policy engineering, computational politics, and computational public policy. Political engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in politics. Computational politics is the application of computer science or mathematics to solving problems in politics. Political engineering and computational politics include, but are not limited to, principles and methods for political decision-making, analysis, modeling, optimization, forecasting, simulation, and expression. Public policy engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in public policy. Computational public policy is the application of computer science or mathematics to solving problems in public policy. Public policy engineering and computational public policy include, but are not limited to, principles and methods for public policy formulation, decision-making, analysis, modeling, optimization, forecasting, and simulation. The chapter describes the scope of research and development in these fields, provides examples of research and development in these fields, and provides possible university curricula for academic programs in these fields.

We consider constrained Horn clause solving from the more general point of view of solving formula equations. Constrained Horn clauses correspond to the subclass of Horn formula equations. We state and prove a fixed-point theorem for Horn formula equations which is based on expressing the fixed-point computation of a minimal model of a set of Horn clauses on the object level as a formula in first-order logic with a least fixed point operator. We describe several corollaries of this fixed-point theorem, in particular concerning the logical foundations of program verification, and sketch how to generalise it to incorporate abstract interpretations.

The purpose of the study is to assess the application of Computer science in the fractional distillation of petroleum. The study sought to: Determine the merit of using Computer science in fractional distillation of petroleum. Evaluate the cost effectiveness to the application of Computer science in fractional distillation of petroleum to the welfare of citizens. Determine if Computer science can help in controlling the natural effects of fractional distillation of petroleum on the environment. Computer science has gained wider application in so many areas of human endeavor ranging from the homes to more scientific and industrial applications, hence its application of Computer science in fractional distillation of petroleum cannot be overemphasized. Irrespective of the benefits of the application of Computer science in fractional distillation of petroleum as a whole, it has become pertinent to ask how effective and efficient its application of Computer science in fractional distillation of petroleum has been. This research work tries to investigate how effective the application of Computer science in fractional distillation of petroleum. The project exertion covered statement of the problem, purpose of the study, research questions/hypotheses were formulated to enable the researcher find out facts about the study, highlighted in division one. Subdivision two was based on the literature review of this study. The study covers the research methodology which guided the study, the next stage was based on the presentation of data and its’ analysis as well as the discussion of findings. The final stage of the research was concerned with the conclusion as well as recommendations and suggestions for further studies

One significant tool for addressing the concerns associated with the use of one of the applications in computer science is the law. Numerous laws and rules governing the use of computer science are designed to safeguard users, customers, and society at large. Law uses norms created by governmental and social institutions to control behavior. Programming is used to explore digital information in computer science. Computers got more potent and smaller as they evolved through generations, utilizing various technologies such as integrated circuits. Cyber law regulates online behavior and deals with matters pertaining to intellectual property, privacy, domain names, and other legal concerns. A few of the many concerns that the law must address in order to mitigate the electronic crimes are protecting privacy and security, upholding justice, limiting civil and criminal liability, ensuring safety, and controlling the application of AI in the workplace. Guidelines that people and organizations have to follow when utilizing computer science applications, making sure that these requirements are met in full. Additionally, as companies must commit to recording AI usage procedures and making clear the use of data and algorithms, the regulation fosters accountability and openness. By doing this, the possibility of prejudice and mistakes when utilizing AI is decreased. This survey provides a self-contained introduction to cybercrimes and types of cybercrimes. We also present ways to combat cybercrime and limit its spread.

First-order temporal logic is a concise and powerful notation, with many potential applications in both Computer Science and Artificial Intelligence. While the full logic is highly complex, recent work on monodic first-order temporal logics has identified important enumerable and even decidable fragments. In this paper, we develop a clausal resolution method for the monodic fragment of first-order temporal logic over expanding domains. We first define a normal form for monodic formulae and then introduce novel resolution calculi that can be applied to formulae in this normal form. We state correctness and completeness results for the method. We illustrate the method on a comprehensive example. The method is based on classical first-order resolution and can, thus, be efficiently implemented.

Theory interpretation is a logical technique for relating one axiomatic theory to another with important applications in mathematics and computer science as well as in logic itself. This paper presents a method for theory interpretation in a version of simple type theory, called lutins, which admits partial functions and subtypes. The method is patterned on the standard approach to theory interpretation in first-order logic. Although the method is based on a nonclassical version of simple type theory, it is intended as a guide for theory interpretation in classical simple type theories as well as in predicate logics with partial functions.

With the proliferation of computing technologies and an ongoing trend of introducing digital and blended learning aspects into higher education, innovative approaches to teaching complex topics like artificial intelligence (AI) have emerged. Of particular interest is the use of game-based learning approaches. According to problem-based learning theory, providing students with an interactive problem and encouraging them to independently find solutions promotes deeper understanding and skill acquisition. Thus, game-based approaches offer an engaging way for students to explore challenging concepts. However, despite the growing use of game-based methods in fields like economics, their application in computer science - especially in teaching game AI - remains under-explored.Understanding AI is increasingly critical for game development, as modern games emulate human-like behaviour in areas such as decision-making, character routines, and adaptive strategies. While many mechanisms and approaches of agent-level decision-making and planning are well understood, their application in video games poses unique challenges, such as accommodating unpredictable player interactions and ensuring performance efficiency without degrading the player experience. While strategy games like StarCraft or multiplayer online battle arenas like DotA 2 have been of interest as proving grounds for advanced AI training methods, their use in education has been limited due to their high complexity and associated learning curve.This work proposes the development of a novel interactive application to fill this niche. Taking inspiration from city building and management games, the application simulates the campus of the University of Applied Sciences Mittweida, where students are given control of agents acting as archetypal roles of students. The agents' goal is the acquisition of knowledge, an abstract resource gained by participating in courses, requiring the agent to navigate the campus. Students interact with the system through an API that provides information on the state of the simulation and allows issuing commands to specific agents. For example, agents who continuously acquire knowledge over a prolonged period do so at decreasing efficiency. To remedy this, a student implements a routine checking the learning efficiency of all agents, commanding "tired" agents to take a break. Alternatively, the student could train a machine learning algorithm to do the same task, albeit more adaptive. Additionally, the application enables dynamic changes to the environment at runtime, such as adding or removing courses or buildings, simulating player-driven alterations to the game world. By designing decision-making algorithms for these agents, students gain hands-on experience with fundamental AI concepts, i.e. decision trees, bridging the gap between theoretical knowledge and practical application.To evaluate the effectiveness of the application, a comparative study with undergraduate students is planned. Over the course of two semesters, two groups of students will be taught the basics of game AI - one using traditional teaching methods (primarily lectures), the other using a game-based method incorporating the new application. The learning progress of both groups will be monitored using assignments, with students being given a sample project and tasked to develop a game AI solution, i.e. for a non-player character in a first-person shooter.

Thermal radiative emission from a hot surface to a cold surface plays an important role in many applications, including energy conversion, thermal management, lithography, data storage and thermal microscopy. Recent studies on bulk materials have confirmed long-standing theoretical predictions indicating that when the gap between the surfaces is reduced to tens of nanometres, well below the peak wavelength of the blackbody emission spectrum, the radiative heat flux increases by orders of magnitude. However, despite recent attempts, whether such enhancements can be obtained in nanoscale dielectric films thinner than the penetration depth of thermal radiation, as suggested by theory, remains experimentally unknown. Here, using an experimental platform that comprises a heat-flow calorimeter with a resolution of about 100 pW (ref. 7), we experimentally demonstrate a dramatic increase in near-field radiative heat transfer, comparable to that obtained between bulk materials, even for very thin dielectric films (50-100 nm) when the spatial separation between the hot and cold surfaces is comparable to the film thickness. We explain these results by analysing the spectral characteristics and mode shapes of surface phonon polaritons, which dominate near-field radiative heat transport in polar dielectric thin films.

Cycle bases in graphs characterization, algorithms, complexity, and applications

Knowledge graphs play an important role in many applications, such as link prediction and question answering. Translating embedding for knowledge graphs is done with the aim of encoding structured information on entities and their rich relations in a low-dimensional embedding space. TransE is one of the most important methods in translation-based models, and uses translation invariance to implement translating embedding for knowledge graphs. In this line of work, translating embedding models represent the relation as a translation from the head entity to the tail entity and have achieved impressive results. Currently, the TransE model is only developed on single-node machines. Unfortunately, the computing and storage capacities of a single machine can easily reach their limits as knowledge graphs become larger and more complex, which limits the application scope of TransE. In order to solve this problem, we propose a distributed TransE method, known as DTransE, which can utilize distributed computing resources to calculate knowledge graph embeddings. However, building a distributed TransE is complicated and involves challenges of knowledge graph partitioning and computation. To solve these challenges, we provide a high-quality edge partitioning algorithm for the power-law graph by considering the high-degree and low-degree vertices with adaptive weights, which can balance the workload. By using the unactivated Gather-Apply-Scatter model on TransE, the processes periodically exchange messages in a loop. The irregular data distribution among the processes is also optimized to further accelerate communication. As far as we know, this is the first work on a distributed TransE method. We use link prediction to evaluate the DTransE in a distributed environment. Experiments show that, compared to the original TransE method, our proposed DTransE is, on average, 24.5 times faster with a minimum loss of accuracy; compared to the state-of-the-art parallel TransE implementation, DTransE is two times faster on average.

Micrometre- and nanometre-sized particles play important roles in many applications, including catalysis, optics, biosensing and data storage. Organic particles are usually prepared through polymerization of suitable monomers or precipitation methods. In the case of inorganic materials, particle fabrication tends to involve the reduction of a metal salt, or the controlled mixing of salt solutions supplying a metal cation and an elemental anion (for example, S2-, Se2-, O2-), respectively; in some instances, these methods even afford direct control over the shape of the particles produced. Another class of materials are metal-organic coordination polymers, which are based on metal ions coordinated by polydentate organic ligands and explored for potential use in catalysis, gas storage, nonlinear optics and molecular recognition and separations. In a subset of these materials, the use of organometallic complexes as ligands (so-called metalloligands) provides an additional level of tailorability, but these materials have so far not yet been fashioned into nano- or microparticles. Here we show that simple addition of an initiation solvent to a precursor solution of metal ions and metalloligands results in the spontaneous and fully reversible formation of a new class of metal-metalloligand particles. We observe initial formation of particles with diameters of a few hundred nanometres, which then coalesce and anneal into uniform and smooth microparticles. The ease with which these particles can be fabricated, and the ability to tailor their chemical and physical properties through the choice of metal and organic ligand used, should facilitate investigations of their scope for practical applications.

Node localization technique is a fundamental and crucial issue for the wireless sensor networks. Localization algorithms play important role for many applications of wireless sensor networks. In this paper, we discuss wireless senor networks architecture and localization algorithms classification. The DV-Hop algorithm has been concentrated on.

Top-k similarity join on high-dimensional data plays an important role in many applications. The traditional tree-like index based approaches can’t deal with large scale high-dimensional data efficiently because of “curse of dimensionality”. So in this paper, we firstly propose an approach to construct the similarity distribution histogram using stratified sampling method, then to estimate the similarity threshold according to the number of the required returned results, finally we propose a novel Top-k similarity join algorithm based on similarity distribution histogram. We conduct comprehensive experiments and the experimental results show that our proposed approaches has good efficiency and scalability.

Sweep coverage plays an important role in many applications like data gathering, sensing coverage, and devices control, etc. In this paper, we deal with sweep coverage problem with multiple mobile sensors to periodically cover n targets in the surveillance region. We propose three constant-factor approximations, namely CycleSplit, CoCycle, and AugPrim, to minimize the longest trajectory length of mobile sensors under different scenarios respectively. CycleSplit deals with non-cooperative sweep coverage problem (NCSC), in which each mobile sensor works independently along different trajectory cycle. It has an approximation ratio of (5-\frac{2}{n-m+1}), which improves the best known approximation ratio of 5. CoCycle solves the cooperative sweep coverage problem (CSC), in which multiple sensors work together to cover $n$ targets. Its approximation ratio is 4, which outperforms the state-of-art best approximation ratio of 6. AugPrim is also a 6-approximation. It computes the sensor route for multi-sink sweep coverage problem (MSSC), where each sensor has to transmit the gathered data to one of the given sinks in each detection period. It implements the modified Prim algorithm and augment paths in turn according to the sink positions. AugPrim is the first approximation for sweep coverage problem with sinks. Finally, we provide various numerical experiments and comparisons with several previous work to validate the efficiency of our design.

Anonymous channels or similar techniques that achieve sender's anonymity play important roles in many applications, e.g. electronic voting. However, they will be meaningless if cryptographic primitives containing sender's identity are carelessly used during the transmission. In computationally secure settings, this problem may be easily overcome by using public key encryption and group signatures. However, in an unconditionally secure setting, in which no computational difficulty is assumed, this is not an easy case as such. As the increasing computational power approaches the point where security policy can no longer assume the difficulty of solving factoring or discrete logarithm problems, it must shift its focus to assuring the solvency of unconditionally secure schemes that provide long-term security. The main contribution of this paper is to study the security primitives for the above problem. In this paper, we first define the unconditionally secure asymmetric encryption scheme, which is an encryption scheme with unconditional security and where it is impossible for a receiver to deduce the identity of a sender from the encrypted message. We also investigate tight lower bounds on required memory sizes from an information theoretic viewpoint and show an optimal construction based on polynomials. It is remarkable to see that these bounds are considerably different from those in Shannon's model of the conventional unconditionally secure symmetric encryption. Other than the polynomial-based scheme, we also show a construction based on combinatorial theory, a non-malleable scheme and a multi-receiver scheme. Then, we define and formalize the group authentication code (GA-code), which is an unconditionally secure authentication code with anonymity like group signatures. In this scheme, any authenticated user will be able to generate and send an authenticated message while the receiver can verify the legitimacy of the message---that it has been sent from a legitimate user but at the same time retains his anonymity. However, by cooperating with the group authority, such as in the case of disputes, the receiver is able to obtain information of the user's identity. For GA-code, we show two concrete constructions.