Automated reasoning

A probabilistic version of geometry is introduced. The fifth postulate of Euclid (Playfair's axiom) is adopted in the following probabilistic form: consider a line and a point not on the line-there is exactly one line through the point with probability P, where 0 ≤ P ≤ 1. Playfair's axiom is logically independent of the rest of the Hilbert system of axioms of the Euclidian geometry. Thus, the probabilistic version of the Playfair axiom may be combined with other Hilbert axioms. P = 1 corresponds to the standard Euclidean geometry; P = 0 corresponds to the elliptic-and hyperbolic-like geometries. 0 < P < 1 corresponds to the introduced probabilistic geometry. Parallel constructions in this case are Bernoulli trials. Theorems of the probabilistic geometry are discussed. Given a triangle and a line drawn from a vertex parallel to the opposite side, the event that this line is actually parallel occurs with probability P. Otherwise, the line may intersect the side or diverge. Parallelism is not transitive in the probabilistic geometry. Probabilistic geometry occurs on the surface with a stochastically variable Gaussian curvature. Alternative geometries adopting various versions of the probabilistic Playfair axiom are introduced. Probabilistic non-Archimedean geometry is addressed. Applications of the probabilistic geometry are discussed.

This white paper introduces and defines a novel, open-source human-machine interaction protocol— the Causal Completeness Constraint Framework (C³F). Traditional AI interaction models treat "hallucinations" as a random, intractable bug. However, the C³F framework, based on the first principles of physics, redefines hallucinations as a necessary byproduct of AI encountering high-entropy inputs with "causal incompleteness." C³F aims to fundamentally eliminate the conditions that lead to hallucinations by structurally constraining the "causal chains" in user inputs at the API level. The framework will provide a foundational API design specification, with the goal of not only enhancing the reliability of AI responses but also transforming AI from a probabilistic "text generator" into a rigorous "logical inference engine." This will provide a safer interaction foundation for all fields requiring high precision, such as scientific research, legal analysis, and engineering design.

The paper presents an iterative version of join-tree clustering that applies the message passing of join-tree clustering algorithm to join-graphs rather than to join-trees, itera tively. It is inspired by the success of Pearl's belief propagation algorithm (BP) as an it erative approximation scheme on one hand, and by a recently introduced mini-clustering (MC(i)) success as an anytime approximation method, on the other. The proposed Iterative Join-graph Propagation (IJGP) belongs to the class of generalized belief propagation meth ods, recently proposed using analogy with algorithms in statistical physics. Empirical evaluation of this approach on a number of problem classes demonstrates that even the most time-efficient variant is almost always superior to IBP and MC(i), and is sometimes more accurate by as much as several orders of magnitude.

The $\mathbf{LMT^{\rightarrow}}$ sequent calculus was introduced in Santos (2016). This paper presents a Termination proof and a new (more direct) Completeness proof for it. $\mathbf{LMT^{\rightarrow}}$ is aimed to be used for proof search in Propositional Minimal Implicational Logic ($\mathbf{M^{\rightarrow}}$), in a bottom-up approach. Termination of the calculus is guaranteed by a rule application strategy that stresses all the possible combinations. For an initial formula $\alpha$, proofs in $\mathbf{LMT^{\rightarrow}}$ have an upper bound of $|\alpha| \times 2^{|\alpha| + 1 + 2 \times log_2|\alpha|}$, which together with the system strategy ensure decidability. $\mathbf{LMT^{\rightarrow}}$ has the property to allow extractability of counter-models from failed proof searches (bicompleteness), i.e., the attempt proof tree of an expanded branch produces a Kripke model that falsifies the initial formula.

In this paper, we conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT) models as a representative of such models. Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned without explanations, and OPT-RE, finetuned with explanations. We then evaluate all models on 57 out-of-domain tasks drawn from the SUPER-NATURALINSTRUCTIONS benchmark, covering 26 distinct reasoning skills, utilizing three prompting techniques. Through a comprehensive grid of 27 configurations and 6,156 test evaluations, we investigate the dimensions of finetuning, prompting, and scale to understand the role of explanations on different reasoning skills. Our findings reveal that having explanations in the fewshot exemplar has no significant impact on the model's performance when the model is finetuned, while positively affecting the non-finetuned counterpart. Moreover, we observe a slight yet consistent increase in classification accuracy as we incorporate explanations during prompting and finetuning, respectively. Finally, we offer insights on which skills benefit the most from incorporating explanations during finetuning and prompting, such as Numerical (+20.4%) and Analogical (+13.9%) reasoning, as well as skills that exhibit negligible or negative effects.

We propose a methodology for investigating how well NLP systems handle meaning preserving syntactic variations. We start by presenting a method for the semi automated creation of a benchmark where entailment is mediated solely by meaning preserving syntactic variations. We then use this benchmark to compare a semantic role labeller and two grammar based RTE systems. We argue that the proposed methodology (i) supports a modular evaluation of the ability of NLP systems to handle the syntax/semantic interface and (ii) permits focused error mining and error analysis.

This book constitutes the refereed proceedings of the 7th International Semantic Web Conference, ISWC 2008, held in Karlsruhe, Germany, during October 26-30, 2008. The volume contains 43 revised full research papers selected from a total of 261 submissions, of which an additional 3 papers were referred to the semantic Web in-use track; 11 papers out of 26 submissions to the semantic Web in-use track, and 7 papers and 12 posters accepted out of 39 submissions to the doctorial consortium. The topics covered in the research track ...

Available remedies to challenge arbitral awards in Pakistan are modification, remission, annulment and revocation of recognition and enforcement. Arbitration is a method through which disputing parties resolve their disputes outside the Court by avoiding technicalities of procedural law. If there is irregularity regarding process and procedure of arbitral tribunal, parties to an arbitration agreement may apply against it in the Court. Arbitrator files an arbitral award in the Court after completion of arbitral proceedings. If arbitral award is domestic and comes under ambit of Civil Court, arbitrator files arbitral award there but in case of foreign arbitral award, parties to arbitration agreement are required to file arbitral award with arbitration agreement and in case if arbitral award and arbitration agreement are not in official language of Pakistan, translation in official language is required to be submitted before High Court for implementation of arbitral award in Pakistan. ...

We present a unified, formal account of AI agent behavior based on Local Uniform Generativity (LUG), a finitary meta-foundation in which evaluation proceeds by local, uniform, bounded-radius updates with primitive-recursive seeding and finitely witnessed acceptance. Within this setting, we introduce a concrete semantic debt calculus: a proof/program/agent run is valuated by a pair FPN(D) = (e, n), where e is the effective (debt-free) component and n is a vector of obligations (debts) arising from underspecification, incompatibility, or non-effective moves. Debts can be discharged by admissible cancellation tokens in an Effective Cancellation Calculus (ECC); a conservation principle ensures debts persist unless canceled by permitted tokens. We show that widely observed agent "failures"-hallucination, contradiction, hedging, role confusion, and the truth-vs-obedience clash-arise as persistent semantic debts. Conversely, a small set of local obligations (role, task, context, constraints) functions as debt-canceling structure that regularizes behavior. A structural safety condition, diagonal safety (DiagSafe), forbids certain global cancellations (e.g., uniform complement/XOR) and thereby explains principled impossibility results and safe refusals. The theory yields engineering patterns: debt ledgers, validators-as-tokens, SCC-based orchestration, and mode selection, with measurable reliability gains. We argue that intelligence can be viewed as controlled generation and well-founded discharge of semantic debt.

Software-Defined Networking (SDN) centralizes control and improves programmability, but this also increases vulnerability to large-scale threats such as DDoS. Conventional intrusion detection systems often fail in this context due to static rules and high false alarms. Recent studies apply deep learning for anomaly detection, with Convolutional Neural Networks (CNNs) offering faster training and lower latency than recurrent or hybrid models. This survey reviews CNN-based IDS approaches and compares them with alternatives like DNNs [1], GRU-RNNs [2], and SAE-based models [3]. Our analysis highlights CNNs' faster convergence and adaptability for real-time detection, while also identifying the challenges of scalability, data diversity, and deployment overhead. The paper concludes by outlining open research directions toward intelligent, lightweight, and automated IDS solutions for strengthening SDN environments. I.

Formal reasoning about concurrent programs is usually done with the assumption that the underlying memory model is sequentially consistent, i.e. the execution outcome is equivalent to an interleaving of instructions according to the program order. However, memory models in reality are weaker in order to accommodate compiler and hardware optimizations. To simplify the reasoning, many memory models provide a guarantee that data-race-free programs behave in a sequentially consistent manner, the so-called DRF-guarantee. The DRF-guarantee removes the burden of reasoning about relaxations when the program is well-synchronized. It is common belief that the current tools for program verification, such as Concurrent Separation Logic (CSL), yield DRF-programs. In principle, they can rely on the DRF-guarantee to ignore memory model issues. However, there is no rigorous evidence for that, given the fact that the work on memory models is not founded with a level of formalism adequate for program verification. It is a semantic gap between the two fields. This thesis provides seminal work towards bridging them together. In our presentation, we formalize memory consistency models by giving a parameterized operational semantics to a concurrent programming language. Behaviors of a program under a relaxed memory model are defined as behaviors of a set of related programs under the sequentially consistent model. This semantics is parameterized in the sense that different memory models can be obtained by using different relations between programs. We present one particular relation, called command subsumption, that we believe accounts for many memory models and sequential optimizations. We then show that the derived relaxed memory model provides the DRF-guarantee, using, as intermediate, an auxiliary

The performance of ontology matching algorithms is evaluated using F-measure, precision and recall which in turn rely on the availability of the ground truth. Typically, the ground truth generation process is manual, subjective and time consuming. Therefore, there is a need to come up with a (semi) automated approach which generates an unbiased reference set; an approximation of ground truth. We propose a framework based solution to generate a reference set and report encouraging results for the OAEI 2019 conference dataset.

This paper presents Unified Dynamic Approximation Equation (UDAE) version 3.0, upgrading artificial intelligence systems from single-core spectrum models to dual-core networked architectures, establishing the theoretical foundation for achieving Artificial General Intelligence (AGI). The core innovation lies in introducing a coupled dynamical system of Local Fitting Core (LFC) and Global Reasoning Core (GRC), achieving dynamic balance between local precise fitting and global knowledge reasoning through a "spectrum + network" multi-dimensional connection mechanism. We establish a complete system of continuous-time partial differential equations, prove global well-posedness of the system, existence of attractors, and provide analytical expressions for phase transition critical points. To address semantic convergence and cross-domain contamination in long-term operation, we design four theoretical modules: Cross-Domain Semantic Adaptation Layer (CDSA), Self-Emergent Reasoning Path Generator (SERP), Layered Persistent Memory System (LPMS), and Semantic Immune Defense (SID). Each module has rigorous mathematical foundations and convergence guarantees. Theoretical analysis shows that the dual-core architecture significantly enhances system long-term stability, cross-domain consistency, and creativity-authenticity balance while maintaining local task performance. Through Lyapunov stability theory, stochastic process analysis, and optimal control theory, we prove that the system can achieve self-assembly and continual learning, providing a feasible mathematical path for AGI realization. This research is not only a fundamental extension of existing deep learning theory but also provides a unified mathematical framework for understanding and constructing truly general intelligent systems.

Most automated reasoning tasks with practical applications can be automatically reformulated into a constraint solving task. A constraint programming platform can thus act as a unique, underlying engine to be reused for multiple automated reasoning tasks in intelligent agents and systems. We identify six key requirements for such platform: expressive task modeling language, rapid solving method customization and combination, adaptive solving method, user-friendly solution explanation, efficient execution, and seamless integration within larger systems and practical applications. We then propose a novel, model-driven, component and rule-based architecture for such a platform that better satisfies as a whole this set of requirements than those of currently available platforms.

in industrial and domestic considerations, tippers can pull a variety of products including gravel, grain, sand, fertilizer, heavy rocks, etc. The older dropping trolley or dumper has been conceived by observing the difficulty in unloading the materials. The survey in this regards in several automobile garages, revealed the facts that mostly some difficult methods were adopted in unloading the materials from the trolley. The three way mechanism is more efficient and economical, and it is easy to unload the materials in three ways or sides.

Tipper has lots of applications in today&#39;s world. I n industrial and domestic considerations, tippers c an haul a variety of products including gravel, potato es, grain, sand, compost, heavy rocks, etc. By cons idering wide scope of the topic, it is necessary to do stud y and research on the topic of tipper mechanism in order to make it more economical and efficient. In existing system, tipper can unload only in one side by using hydraulic jack or conveyor mechanism. By this research it is easy for the driver to unload the trailer and also it reduces time and fuel consumption. For making tipper mechanism with such above conditions both mechanisms namely hydraulic jack and conveyor mechanism can be used. But eventually it comes with question that how both systems can arrange in single set up? Answer to thi s question is nothing but this research work.

Abstract. We describe a matching algorithm for terms built over flexible arity function symbols and context, function, sequence, and individual variables. The algorithm is called a context sequence matching algorithm. Context variables allow matching to descend in term-trees ...

This paper proposes a technique to limit the number of possible terms to be considered in quantifier instantiation. One of the major hurdles that SMT solvers face when dealing with quantifiers is that there are simply too many terms to instantiate with. So even if the right set of terms is available to the solver, meaning they appear in the formula, the solver might not have enough resources to come upon the right combination. This motivates the technique presented in this paper, which instantiates only by a certain type of terms, called discriminating terms. The paper introduces a class of formulas, where the proposed technique has a considerable impact.

The administrator of current IT environments is facing a continuous increase in the complexity that s/he needs to handle. S/he can be compared to a power jet pilot sitting in front of a console with a plenty of gouges, leds, counters, each of them measuring or showing a particular part of the overall aircraft status. The pilot looks to all these indications, correlate each other these data sources, provide them a context (the air craft is landing, is taking off, and so forth) and, on the light of his knowledge and experience, he can ...

Sycophancy in neural networks-defined as the tendency of advanced models, particularly Large Language Models (LLMs), to excessively agree with or flatter users-has surfaced as a significant threat to the reliability, objectivity, and ethical deployment of artificial intelligence. This paper presents a comprehensive, formal investigation into neural network sycophancy, introducing and rigorously defining deferential biases as a core taxonomic class of AI bias. Drawing on the latest literature, empirical benchmarks, mechanistic analyses, and real-world case studies, we develop a novel multi-faceted framework for analyzing and mitigating deferential biases in neural networks. The paper carefully differentiates forms of sycophancy, reviews cutting-edge measurement strategies and mitigation techniques, and demonstrates both the theoretical and practical impacts of unchecked deferential bias on trust, factual accuracy, and safety in AI. Our findings emphasize the crucial necessity of integrating ethical, epistemic, and alignment-based approaches to develop more robust, trustworthy, and socially beneficial AI systems.

Higher education remains in the midst of transformation because Artificial Intelligence continues to enhance institutional handling of admissions alongside student guidance and research operations. AI tools in admissions exams automatically examine academic documents together with application statements and reference points using efficient methodologies that support data-driven admissions while reducing human prejudice toward achieving diversity goals. Real-time academic and career support through AI-powered student advising tools provides students with personalized learning analysis that assists their path selection based on their objectives and achievements. The research field becomes more efficient through AI because automated literature review systems assist researchers with data interpretation as well as accurate plagiarism identification. New technological solutions increase operational output yet they generate essential moral considerations regarding data protection and revealing information together with transparency matters. The study argues that academic institutions must approach AI implementation by making it work second to human judgment in order to create a safe and unbiased platform that aids universities in achieving their educational objectives effectively. Keywords: University admissions processes involved AI technology combined with digital systems including predictive analytics which operate alongside virtual guidance for academic purposes and data security concerns along with ethical challenges and bias detection and academic automation systems for higher education institutions.

CLIP is an implementation of CLP(Intervals) which has been designed to be verifiably correct in the sense that the answers it returns are mathematically correct solutions to the underlying arithmetic constraints. This fundamental design criteria affects many aspects of the implementation from the input and output of decimal constants to the design of the interval arithmetic libraries and the constraint solving algorithms. In particular, to enhance verifiability, CLIP employs the simplest model of constraint solving in which constraints are decomposed into sets of primitive constraints which are then solved using a library of primitive constraint contractors. This approach results in a simple constraint solver whose correctness is relatively straightforward to verify, but the solver is only able to solve relatively simple constraints. In this paper, we present the syntax, semantics, and implementation of CLIP, and we show how to use metalevel techniques to enhance the power of the CLIP constraint solver while preserving the simple structure of the system. In particular, we demonstrate that several of the box-narrowing algorithms from the Newton and Numerica systems can be easily implemented in CLIP. The principal advantages of this approach are (1) the resulting solvers are relatively easy to prove correct, (2) new solvers can be rapidly prototyped since the code is more concise and declarative than for imperative languages, and (3) contractors can be implemented directly from mathematical formulae without having to first prove results about interval arithmetic operators. Finally, the source code for the system is publicly available, which is a clear prerequisite for public, independent verifiability.

Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication overlap, verification, and shape analysis. For sparse matrix codes, array data dependence analysis is made more difficult by the use of index arrays that make it possible to store only the nonzero entries of the matrix (e.g., in A[B[i]], B is an index array). Here, dependence analysis is often stymied by such indirect array accesses due to the values of the index array not being available at compile time. Consequently, many dependences cannot be proven unsatisfiable or determined until runtime. Nonetheless, index arrays in sparse matrix codes often have properties such as monotonicity of index array elements that can be exploited to reduce the amount of runtime analysis needed. In this paper, we contribute a formulation of array data dependence analysis that includes encoding index array properties as universally quantified constraints. This makes it possible to leverage existing SMT solvers to determine whether such dependences are unsatisfiable and significantly reduces the number of dependences that require runtime analysis in a set of eight sparse matrix kernels. Another contribution is an algorithm for simplifying the remaining satisfiable data dependences by discovering equalities and/or subset relationships. These simplifications are essential to make a runtime-inspection-based approach feasible.

Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication overlap, verification, and shape analysis. For sparse matrix codes, array data dependence analysis is made more difficult by the use of index arrays that make it possible to store only the nonzero entries of the matrix (e.g., in A[B[i]], B is an index array). Here, dependence analysis is often stymied by such indirect array accesses due to the values of the index array not being available at compile time. Consequently, many dependences cannot be proven unsatisfiable or determined until runtime. Nonetheless, index arrays in sparse matrix codes often have properties such as monotonicity of index array elements that can be exploited to reduce the amount of runtime analysis needed. In this paper, we contribute a formulation of array data dependence analysis that includes encoding index array properties as universally quantified constraints. This makes it possible to leverage existing SMT solvers to determine whether such dependences are unsatisfiable and significantly reduces the number of dependences that require runtime analysis in a set of eight sparse matrix kernels. Another contribution is an algorithm for simplifying the remaining satisfiable data dependences by discovering equalities and/or subset relationships. These simplifications are essential to make a runtime-inspection-based approach feasible.

In several aspects of the fuzzy sets theory and its applications, it is convenient to do some manipulations on the formulas that are proposed in order to obtain some enhancements in methods such as in design and implementation. Currently, the proper fuzzy sets characteristics do not let Boolean simplification methods be applied in an effective way, and consequently, the algorithm solutions proposed so far for these methods cannot be used. This paper presents preliminary ideas in the first algorithmic implementation design of a simplification method of Boolean and fuzzy formulas by using finite algebras.

A shallow semantical embedding of Input/Output logic in classical higher-order logic is presented, and shown to be faithful (sound an complete). This embedding has been implemented in the higher-order proof assistant Isabelle/HOL. We provide an empirical regulative framework for assessing General Data Protection Regulation.

Number of vehicles on our roads is increasing day by day, also the technology has developed but the safety factor is always needed to be considered. Now a day’s vehicles come fitted with lots of safety features. One of the essential safety feature that need to be installed is automatic upper-dipper control of headlight, this feature can mainly use during night time driving. This feature can be installed in three axis trolley which are mainly used for industrial purpose

Mariana Olezza (IIEP/UBA), Javier Legris (IIEP/UBA/CONICET) -
Peirce on logical machines and semiotics.
Abstract: In the dawn of symbolic logic, Charles S. Peirce (1839–1914) discussed the logical machines devised at that time in a short paper published in 1887 in The American Journal of Psychology. In it, Peirce applied his own ideas on the theory of signs and reasoning. In the following, we show that, in doing this, Peirce advanced later semiotic reflections on Artificial Intelligence. The idea of theorematic reasoning, indispensable in Peirce’s account of mathematical proof, will play an important role in the discussion Peirce discussed the logical machines respectively designed by W. Stanley Jevons (1836–1882) and Allan Marquand (1853–1924). Jevons developed a “logical abacus” and more noticeably, the logical piano, in order to reduce complex boolean expressions. Marquand conceived the first electromechanical logical machine along with Peirce. In this discussion, Peirce argued that semiosis is not reducible to mechanical causal factors. Our paper will take into account his semiotic concept of mind. Machines possess quasi-semiosis, a simulation of semiosis, and while human beings are “semiotic systems”, logical machines are not. According to James H. Fetzer, it is the ability to make mistakes that characterizes a semiotic system. However, this criteria to distinguish a semiotic system should be revised, because artificial neural networks (ANN) in general can misclassify data. So: or it is not necessary for a system to be semiotic for it to generate an error, or ANNs could also be considered semiotic systems. At this point, Peirce introduced in the discussion the idea of theorematic reasoning, which goes beyond mere mechanical procedures. Theorematic deduction “resorts to more complex processes of thinking” (Peirce EP II, p.442). These processes involve the introduction of information that was not included in the premises of the deduction. With theorematic reasoning, Peirce attempts to explain the generation of new knowledge in mathematics, trying to explain what is usually called “mathematical creativity”. Now, is it possible to jump the leap from deductive reasoning to theorematic reasoning through learning mechanisms? Will creativity be achieved through ANNs? Faced with this, we find ourselves with the well-known “Lovelace objection”, raised by the first programmer Ada Lovelace (1815–1852), who expressed in 1843 that the Analytical Engine designed by Charles Babbage had no pretensions to originate anything. It can only do what we order it to perform. Alan Turing (1912–1954) replied to the objection stating that while Lady Lovevace in her day probably had no reason to think that machines were not capable of generating something original, that was not necessarily true of machine learning, and he began a project in 1950 to advance the field of AI based on learning. That project is currently being continued in what is known as ANNs. Today we have systems such as GPT-3
which, in the case of "creativity", offers us to discuss systems like DALL-e. In our contribution, we will examine this new achievements in the framework of Peirce’s thinking.

The paper presents a fuzzy expert system that possesses the ability to self-learn, self-correct and self-expand. These features embellish the expert system with the capability to cope effectively with the ever-changing industrial environment. The system primarily provides the optimization of the predictor parameters of a process, with respect to the desired objectives, and prediction of the process's performance measures based upon settings of the predictor parameters. The self-development mode of the system consists of modules possessing following characteristics: automatic storage/retrieval of data; automatic development of fuzzy sets; automatic generation of fuzzy rules for optimization and prediction rule-bases; conflict resolution among contradictory rules; and automatic updating of expert system interface. The presented expert system finds high degree of applicability in optimization of manufacturing processes in general, and machining in particular.

Tree automata completion is a technique for the verification of infinite state systems. It has already been used for the verification of cryptographic protocols and the prototyping of Java static analysers. However, as for many other verification techniques, the correctness of the associated tool becomes more and more difficult to guarantee. It is due to the size of the implementation that constantly grows and due to low level optimizations which are necessary to scale up the efficiency of the tool to verify real-size systems. In this paper, we define and develop a checker for tree automata produced by completion. The checker is defined using Coq and its implementation is automatically extracted from its formal specification. Using extraction gives a checker that can be run independently of the Coq environment. A specific algorithm for tree automata inclusion checking have been defined so as to avoid the exponential blow up. The obtained checker is certified in Coq, independant of the implementation of completion, usable with any approximation performed during completion, small and fast. Some benchmarks are given to illustrate the efficiency of the tool.

Di Indonesia, bitcoin merupakan bentuk mata uang digital (e-money) dan alat transaksi pembayaran yang sudah digunakan oleh sebagian masyarakat. Hal tersebut perlu mendapatkan perhatian, khususnya dari Bank Indonesia. Lain dari pada itu, pengawasan yang dulu sepenuhnya dilakukan oleh bank sentral yaitu Bank Indonesia, sekarang diambil alih oleh OJK (Otoritas Jasa Keuangan). Sehingga Bank Indonesia pun hanya memiliki wewenang untuk mengatur dan mengontrol peredaran mata uang saja. Sejak sebagian tugas dan wewenang Bank Indonesia diambil alih oleh OJK (Otoritas Jasa Keuangan), banyak hal yang belum tercover seperti adanya fenomena baru dalam bidang keuangan dalam hal permodalan, investasi, peredaran mata uang, dan lain-lain. Selain belum ada payung hukum terhadap bitcoin, yaitu semakin merebaknya transaksi yang menggunakan bitcoin yang dilakukan dalam transaksi e-commerce. Sehingga dari segi keamananannya juga perlu dipertanyakan, maka dari itu perlu ada regulasi dan pengawasan secara ...

Industri Keuangan Syari'ah (IKS) dalam menjalankan aktifitasnya harus mematuhi sejumlah aturan yang memuat berbagai prinsip syari'ah yang diaktualisasikan dalam bentuk fatwa yang dikeluarkan oleh Dewan Syari'ah Nasional (DSN). Diwajibkannya keberadaan Dewan Pengawas Syari'ah (DPS) pada setiap industri keuangan syari'ah baik bank syari'ah (IKBS) maupun Industri Keuangan Non-Bank Syari'ah (IKNBS) untuk mematuhi ketentuan syari'ah dan menjadikan pengawasan syari'ah sebagai salah satu aspek penting serta tidak terpisahkan dengan kepatuhan syari'ah. Hal tersebut bisa dilihat dari beberapa regulasi yang dikeluarkan oleh DPS-MUI yang menjadi acuan bagi industri IKBS dan IKNBS di bidang keuangan syari'ah. Fokus penelitian ini tertuju pada regulasi yang dikeluarkan oleh DSN-MUI serta implementasinya terhadap lembaga IKBS dan IKNBS, serta DPS sebagai pihak yang berwenang untuk mengawasi setiap industri keuangan yang berbasis syari'ah. Pendekatan penelitian menggunakan deskriptif-analitis dengan jenis penelitian pustaka, yaitu pencarian data melalui buku atau dokumen lainnya yang berhubungan dengan objek penelitian .Hasil penelitian membuktikan bahwa DPS sebagai pemegang otoritas pengawasan terhadap kepatuhan syari'ah memiliki tanggung jawab yang diatur melalui ketentuan hukum yang tegas. Oleh karena itu, kedudukan DPS sangat menentukan terciptanya kepatuhan syari'ah sebagai unsur utama dalam keberadaan dan kelangsungan usaha bagi industri keuangan syari'ah.

This research was conducted at IAIN Raden Intan Bandar Lampung which is aimed at revealing the influence of leadership on the lecturers' professionalism and teaching-learning quality. The study uses a quantitative approach through descriptive correlation technique and regression by using parametric statistical analysis. The research findings show that the three hypothesis can be accepted as supported by empirical as following: 1) the influence of leadership upon lecturers' professionalism is 12.5%, with the strength low correlation as (0.354); 2) the leadership influence toward the learning quality is 0.81% with a low correlation strength i.e. 0.285; and 3) the lecturers' professionalism influence on learning quality is equal to 63.9%, with a strong correlation (0.799).

In recent years backtrack search algorithms for propositional satisfiability (SAT) have been the subject of dramatic improvements. These improvements allowed SAT solvers to successfully solve instances with thousands or tens of thousands of variables. However, many new challenging problem instances are still too hard for current SAT solvers. As a result, further improvements to SAT technology are expected to have key consequences in solving hard realworld instances. This paper introduces a new idea: choosing the backtrack variable using a heuristic approach with the goal of diversifying the regions of the space that are explored during the search. The proposed heuristics are inspired by the heuristics proposed in recent years for the decision branching step of SAT solvers, namely, VSIDS and its improvements. Completeness conditions are established, which guarantee completeness for the new algorithm, as well as for any other incomplete backtracking algorithm. Experimental results on hundreds of instances derived from real-world problems show that the new technique is able to speed SAT solvers, while aborting fewer instances. These results clearly motivate the integration of heuristic backtracking in SAT solvers.

This paper summarises and gives an overview of the development of the ΩMEGA system during the 12 years of funding by the SFB 378. The research objective of the ΩMEGA project has been to lay the foundation complex, heterogenous, but well integrated assistance systems for mathematics, which support the wide range of typical research, publication and knowledge management activities of a working mathematician. Examples are computing (for instance algebraic and numeric problems), proving (lemmas or theorems) , solving (for instance equations), modelling (by axiomatic definitions), verifying (typically a proof), structuring (for instance the new theory and knowledge base), maintaining (the knowledge base), searching (in a very large mathematical knowledge base), inventing (your new theorems), paper writing, explaining and illustrating in natural language and diagrams. Clearly, some of them require a high amount of human ingenuity

Non-classical logics are used in a wide spectrum of disciplines, including artificial intelligence, computer science, mathematics, and philosophy. The de-facto standard infrastructure for automated theorem proving, the TPTP World, currently supports only classical logics. Similar standards for non-classical logic reasoning do not exist (yet). This hampers practical development of reasoning systems, and limits their interoperability and application. This paper describes the latest extension of the TPTP World, which provides languages and infrastructure for reasoning in non-classical logics. The extensions integrate seamlessly with the existing TPTP World.

This paper presents the Dr.Doodle system, an interactive theorem prover that uses diagrammatic representations. The assumption underlying this project is that, for some domains (principally geometry), diagrammatic reasoning is easier to understand than conventional algebraic approaches -at least for a significant number of people. The Dr.Doodle system was developed for the domain of metric-space analysis (a geometric domain, but traditionally taught using a dry algebraic formalism). Pilot experiments were conducted to evaluate its potential as the basis of an educational tool, with encouraging results.

For the whole class of linear term rewriting systems and for each integer k, we define k-bounded rewriting as a restriction of the usual notion of rewriting. We show that the problem of the existence of an infinite k-bounded derivation, called the k-bounded termination problem, is decidable. The k-bounded class (BO(k)) is, by definition, the set of linear systems for which every derivation can be replaced by a k-bounded derivation. In general, the k-bounded termination problem for BO(k) systems is not equivalent to the termination problem. This leads us to define more restricted classes for which those problems are equivalent: the classes BOLP(k) of k-bounded systems that have the length preservation property. By definition, a system is BOLP(k) if every derivation of length n can be replaced by a k-bounded derivation of length n. We define the class BOLP of bounded systems that have the length preservation property as the union of all the BOLP(k) classes. The class BOLP contains (strictly) several already known classes of systems: the inverse left-basic semi-Thue systems, the linear growing term rewriting systems, the inverse Linear-Finite-Path-Ordering systems, the strongly bottom-up systems.

Bounded rewriting for linear term rewriting systems has been defined in (I. Durand, G. Sénizergues, M. Sylvestre. Termination of linear bounded term rewriting systems. Proceedings of the 21st International Conference on Rewriting Techniques and Applications) as a restriction of the usual notion of rewriting. We extend here this notion to the whole class of left-linear term rewriting systems, and we show that bounded rewriting is effectively inverse-recognizability preserving. The bounded class (BO) is, by definition, the set of left-linear systems for which every derivation can be replaced by a bottom-up derivation. The class BO contains (strictly) several classes of systems which were already known to be inverse-recognizability preserving: the left-linear growing systems, and the inverse right-linear finite-path overlapping systems.

We propose a method which allows us to develop tableaux modulo theories using the principles of superdeduction, among which the theory is used to enrich the deduction system with new deduction rules. This method is presented in the framework of the Zenon automated theorem prover, and is applied to the set theory of the B method. This allows us to provide another prover to Atelier B, which can be used to verify B proof rules in particular. We also propose some benchmarks, in which this prover is able to automatically verify a part of the rules coming from the database maintained by Siemens IC-MOL.

The use of formal methods provides confidence in the correctness of developments. Yet one may argue about the actual level of confidence obtained when the method itself -or its implementation -is not formally checked. We address this question for the B, a widely used formal method that allows for the derivation of correct programs from specifications. Through a deep embedding of the B logic in Coq, we check the B theory but also implement B tools. Both aspects are illustrated by the description of a proved prover for the B logic.

We develop a formal proof of the ML type inference algorithm, within the Coq proof assistant. We are much concerned with methodology and reusability of such a mechanization. This proof is an essential step toward the certification of a complete ML compiler.

EU-IST Integrated Project (IP) IST-2003-506826 SEKT Deliverable D3.6.1(WP3.6) In this document, we present a framework for inconsistent ontology diagnosis and repair by defining a number of new non-standard reasoning services to explain inconsistencies through pinpointing. We developed two different types of algorithms for the framework, and we describe these algorithms in some detail. Both algorithms have been prototypically implemented as the DION (Debugger of Inconsistent ONtologies) and MUPStersystem. The first implements a bottom-up approach to calculate pinpoints by the support of an external DL reasoner, the second using a specialised tableau-based calculus.

Evolutionary algorithms or computations (EC) are commonly used for solving problems such as optimization and reverse engineering of complex systems. In this paper, we present bio-inspired approach in a process of evolving design for the workflow processes. Workflow management systems often offer modelling capabilities to support construction of business processes. However, constructing formal workflow model for the extensive business process is a non-trivial task. Workflow process could be described as bipartite graph and therefore interpreted by Petri Nets. The goal is to describe a process for evolving and verification of petri nets.

The PV solar energy development has already emerged as both framework and keystone to sustainable development in 21st century which is being offered as a boon for a clean pathway of renewable sustainable source amid mounting climate change concerns. With rapid advancement in solar panel efficiency and declining product cost and its widespread adoption in developing nations, its pivotal role in shaping the global economy with transition to low carbon economy changed drastically. However, with the present scenario of rapid climate change and rising global energy demand concerns are being raised regarding the efficiency of solar panels and susceptibility to major factors of environment including dust accumulation, humidity, temperature fluctuation and reduced light intensity. In view of the prevailing condition and present circumstance with alignment to global current trend the following research paper presents and exhibits a novel solution of real time intelligent automated system of PV panel health monitoring and automated cleaning utilizing multimodal data fusion algorithms with embedded IOT control architecture. The proposed solution exhibits panel dirt detection and cleaning system as smart solution towards panel efficiency optimal maintenance through automated monitoring. The following system sites and classifies panel cleanliness in 2 categories namely clean and dirty using lightweight (YOLOv5) model with a webcam feed as aid for capturing and feeding visual input. As complementary to this scenario sensor data with embedded Arduino architecture is being captured with increased reliability and both former and latter data is being fused via Large Language model (LLM) to improve prediction. By this current approach of fusing visual data with sensor input, the system accurately determines when a panel is dirty or not. Upon detection, it alerts the user and can automatically trigger a cleaning mechanism via a relay module. By minimizing manual inspection and mitigating energy loss concerns this method of approach enhances its contribution towards more sustainable solar power systems though timely and intelligent data driven maintenance strategy ultimately promoting sustainable integration at global energy landscape

As it appeared in the first and second editions of this Encyclopedia, this article dealt with machine-and assembly-language programming as if they were dead or dying, and the article aspired to do little more than deliver a eulogy before turning away for the last time from something on the verge of extinction. In the third edition, a reprieve was announced-the arrival of the personal computer, typically with no more than 640K of application-program memory, restored the importance of tight code, and with it the rationale for machine language (ML) and assembly language (AL). For this fourth edition, the picture has changed again: in 1999 personal computers usually have at least 64 MB of memory, along with fast hard disk (q.v.) drives of several gigabytes or more, and tight code is no longer a critical issue for most personal computers, whether desktop or laptop. Today's client for tight code is the palmtop computer (see PORTABLE COMPUTERS), andeven more-the embedded processor that lives in an environment that most consumers don't associate with computing at all. Because many computer users are unaware of the reasons for the stubborn survival of AL and ML, this article goes well beyond a presentation of the technology involved, and offers extended discussion of the strategies involved in its use. That discussion includes treatments of: + subroutines and macro-instructions + binding-time control + modularization + NSC architecture's effect on AL/ML For the sake of clarity, the coding examples offered throughout this article are for a long-obsolete machine family (the IBM 704-709-7090-7094 series), which lends itself to piecemeal elementary presentation better than its successors (you learn how to fly in a Piper Comanche, not a Boeing 747). This can be done without loss of generality because today's machines, though faster, bigger, and far more complex, embody no essentially new programming principles. Machine language has traditionally meant that particular representation of instructions and data that is directly usable by .the central processing unit (q. v . ) of the machine in question. It was a low-level languageindeed, the lowest possible-with the corollary that it reflected the machine's internal structure far more than the purposes to which most users would want to

As it appeared in the first and second editions of this Encyclopedia, this article dealt with machine-and assembly-language programming as if they were dead or dying, and the article aspired to do little more than deliver a eulogy before turning away for the last time from something on the verge of extinction. In the third edition, a reprieve was announced-the arrival of the personal computer, typically with no more than 640K of application-program memory, restored the importance of tight code, and with it the rationale for machine language (ML) and assembly language (AL). For this fourth edition, the picture has changed again: in 1999 personal computers usually have at least 64 MB of memory, along with fast hard disk (q.v.) drives of several gigabytes or more, and tight code is no longer a critical issue for most personal computers, whether desktop or laptop. Today's client for tight code is the palmtop computer (see PORTABLE COMPUTERS), andeven more-the embedded processor that lives in an environment that most consumers don't associate with computing at all. Because many computer users are unaware of the reasons for the stubborn survival of AL and ML, this article goes well beyond a presentation of the technology involved, and offers extended discussion of the strategies involved in its use. That discussion includes treatments of: + subroutines and macro-instructions + binding-time control + modularization + NSC architecture's effect on AL/ML For the sake of clarity, the coding examples offered throughout this article are for a long-obsolete machine family (the IBM 704-709-7090-7094 series), which lends itself to piecemeal elementary presentation better than its successors (you learn how to fly in a Piper Comanche, not a Boeing 747). This can be done without loss of generality because today's machines, though faster, bigger, and far more complex, embody no essentially new programming principles. Machine language has traditionally meant that particular representation of instructions and data that is directly usable by .the central processing unit (q. v . ) of the machine in question. It was a low-level languageindeed, the lowest possible-with the corollary that it reflected the machine's internal structure far more than the purposes to which most users would want to