Reviewerly/exHarmony huggingface dataset:
Modelling supra-classical logic in a Boltzmann neural network: III adaptation

Abstract The field of belief revision in logic is still in evolution and holds a variety of disparate approaches; a consequence of theoretical conjecture. As a probabilistic model of supra-classical, non-monotonic (SCNM) logic, the Boltzmann machine, offers an experimental gateway into the field. How does the Boltzmann network adapt to new information? Catastrophic forgetting is the default response to retraining in any neural network. We have moderated this irrational non-monotonicity by alterations in the Boltzmann learning algorithm. The spectrum of experimental belief change is limited by the availability of ‘new’ information, a pragmatic realization co-related to the property of Rational Monotonicity in the domain of SCNM logic. Recognizing this upper boundary of defeasible belief simplifies the task of experimentally exploring machine adaptation. A minority of belief revisions involve new, but unsurprising information, that is at least partially consistent with the previous learned beliefs. In these circumstances, the Boltzmann network incrementally adjusts the priority of model state exemplars in accordance with preference; the traditional approach in SCNM logic. However, in the majority of situations the new information will be surprisingly inconsistent with the previous beliefs. In these circumstances, the pre-order on model states stratified by preference, will not have sufficient granularity to represent the conflicting requirements of ranking based on compositional atomic typicality. This novel experimental finding has not previously been considered in the logical conjecture on Belief Revision.
Marshall–Olkin power-law distributions in length-frequency of entities

Entities involve important concepts with concrete meanings and play important roles in numerous linguistic tasks. Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts. In this paper, we are curious to know whether there are some common characteristics that connect those different forms of entities. Specifically, we investigate the underlying distributions of entities from different types and different languages, trying to figure out some common characteristics behind those diverse entities. After analyzing twelve datasets about different types of entities and eighteen datasets about entities in different languages, we find that while these entities are dramatically diverse from each other in many aspects, their length-frequencies can be well characterized by a family of Marshall–Olkin power-law (MOPL) distributions. We conduct experiments on those thirty datasets about entities in different types and different languages, and experimental results demonstrate that MOPL models characterize the length-frequencies of entities much better than two state-of-the-art power-law models and an alternative log-normal model. Experimental results also demonstrate that MOPL models are scalable to the length-frequency of entities in large-scale real-world datasets.
BOFUS and CLARITY: streamlining blockchain architecture and assessment for advanced standardization and interoperability in distributed ledger technologies

The Blockchain Organized Framework for Unified Systems (BOFUS) and the Comprehensive Ledger Assessment for Robust Interoperability and Trustworthiness (CLARITY) initiatives address the challenges of understanding, standardizing, and enabling interoperability between diverse blockchain systems. BOFUS is a comprehensive 5-layer model that systematically organizes core blockchain components, while the CLARITY assessment provides a standardized method for evaluating and comparing blockchains using the CONFIGURE acronym. Together, these initiatives aim to facilitate a deeper understanding of blockchain technology, promote effective communication and collaboration between stakeholders, and ultimately advance the development and adoption of distributed ledger technologies. This paper presents an in-depth discussion of the BOFUS architecture and the CLARITY assessment, exploring their utility in various blockchain scenarios and their potential implications for the future of blockchain technology.
Multi-Party Secured Collaboration Architecture from Cloud to Edge

ABSTRACTDistributed Federated Collaboration Secured Services is a proposed novel secure distributed Unified Communication and Collaboration (UCC) reference architecture planned for multi-environment ecosystems (including hybrid cloud, edge computing for IoT and real-time usages, and portable scenarios). DFCS-RA encourages new businesses and models, connecting people, “things,” and processes in secured and sensitive workloads, supporting efficient and cost-effective enhancements to traditional UCC (mostly centralized/monolithic services) tailored for different vertical markets having varying requirements. It provides a novel secure multi-environment communication and collaboration technology and services between large numbers of players. Key security concepts developed are secure separation, federation, and collaboration. The cryptographic architecture extends the Signal messenger security and encryption architecture. DFCS-RA comprises a toolbox for security modeling, a set of security design principles, and a set of security functions and mechanisms to implement controls to achieve stated security objectives. It was tested in diverse use cases; and found suitable, secure, and reliable.KEYWORDS: Signalsecured unified communication and collaborationdistributed UCChybrid collaboration Disclosure statementNo potential conflict of interest was reported by the author(s).
Hierarchical painter: Chinese landscape painting restoration with fine-grained styles

Abstract Image inpainting is a critical area of research in computer vision with a broad range of applications, including image restoration and editing. However, current inpainting models often struggle to learn the specific painting styles and fine-grained brushstrokes of individual artists when restoring Chinese landscape paintings. To address this challenge, this paper proposes a novel inpainting model specifically designed for Chinese landscape paintings, featuring a hierarchical structure that can be applied to restore the famous Dwelling in the Fuchun Mountains with remarkable fidelity. The proposed method leverages an image processing algorithm to extract the structural information of Chinese landscape paintings. This approach enables the model to decompose the inpainting process into two separate steps, generating less informative backgrounds and more detailed foregrounds. By seamlessly merging the generated results with the remaining portions of the original work, the proposed method can faithfully restore Chinese landscape paintings while preserving their rich details and fine-grained styles. Overall, the results of this study demonstrate that the proposed method represents a significant step forward in the field of image inpainting, particularly for the restoration of Chinese landscape paintings. The hierarchical structure and image processing algorithm used in this model is able to faithfully restore delicate and intricate details of these paintings, making it a promising tool for art restoration professionals and researchers.
On Gradient Descent Training Under Data Augmentation with On-Line Noisy Copies

In machine learning, data augmentation (DA) is a technique for improving the generalization performance of models. In this paper, we mainly consider gradient descent of linear regression under DA using noisy copies of datasets, in which noise is injected into inputs. We analyze the situation where noisy copies are newly generated and injected into inputs at each epoch, i.e., the case of using on-line noisy copies. Therefore, this article can also be viewed as an analysis on a method using noise injection into a training process by DA. We considered the training process under three training situations which are the full-batch training under the sum of squared errors, and full-batch and mini-batch training under the mean squared error. We showed that, in all cases, training for DA with on-line copies is approximately equivalent to the ℓ2 regularization training for which variance of injected noise is important, whereas the number of copies is not. Moreover, we showed that DA with on-line copies apparently leads to an increase of learning rate in full-batch condition under the sum of squared errors and the mini-batch condition under the mean squared error. The apparent increase in learning rate and regularization effect can be attributed to the original input and additive noise in noisy copies, respectively. These results are confirmed in a numerical experiment in which we found that our result can be applied to usual off-line DA in an under-parameterization scenario and can not in an over-parametrization scenario. Moreover, we experimentally investigated the training process of neural networks under DA with off-line noisy copies and found that our analysis on linear regression can be qualitatively applied to neural networks.
設計根拠に注目した鉄鋼材料設計知識モデルの記述と活用

In order to design materials efficiently and to avoid quality defects, it is important to understand design rationales, which are usually implicit behind numerical data. This paper proposes an ontological knowledge modeling framework for design rationale of steel materials. The framework integrates tree-like structural models of design actions, manufacturing processes and machining processes with an ontological classification of nodes in these models. Based on the framework, general steel design knowledge models had been described using ontological dictionaries extended for steel materials. Using the knowledge models, a prototype system summarizes crucial points for realizing a given target property and for avoiding quality defects. For deployment of the framework in the steel company, a browser-based client software and a knowledge server system had been developed. The software with a real-scale general design knowledge model for a specific kind of steel had been used by researchers in the company and evaluated through questionnaires. The result shows it is useful for understanding design rationale and for inheritance of steel design knowledge.
Imbalanced Data Over-Sampling Method Based on ISODATA Clustering

Class imbalance is one of the challenges faced in the field of machine learning. It is difficult for traditional classifiers to predict the minority class data. If the imbalanced data is not processed, the effect of the classifier will be greatly reduced. Aiming at the problem that the traditional classifier tends to the majority class data and ignores the minority class data, imbalanced data over-sampling method based on iterative self-organizing data analysis technique algorithm(ISODATA) clustering is proposed. The minority class is divided into different sub-clusters by ISODATA, and each sub-cluster is over-sampled according to the sampling ratio, so that the sampled minority class data also conforms to the imbalance of the original minority class data. The new imbalanced data composed of new minority class data and majority class data is classified by SVM and Random Forest classifier. Experiments on 12 datasets from the KEEL datasets show that the method has better G-means and F-value, improving the classification accuracy.
Non-Touching Measurement Method on Yaw Angle Using a Stereo Camera

This letter proposes a measuring algorithm of yaw angle using a stereo camera. The proposed method estimates the change of the yaw angle by aligning the point groups with respect to the result of the three-dimensional reconstruction of consecutive frames obtained from the stereo camera. The algorithm prevents the degradation of 3D point cloud correspondence. Experimental results of head posture angle show the effectiveness of the proposed method.
Shadow Detection Based on Luminance-LiDAR Intensity Uncorrelation

In recent years, there has been a growing demand for urban digitization using cameras and light detection and ranging (LiDAR). Shadows are a condition that affects measurement the most. Therefore, shadow detection technology is essential. In this study, we propose shadow detection utilizing the LiDAR intensity that depends on the surface properties of objects but not on irradiation from other light sources. Unlike conventional LiDAR-intensity-aided shadow detection methods, our method embeds the un-correlation between luminance and LiDAR intensity in each position into the optimization. The energy, which is defined by the un-correlation between luminance and LiDAR intensity in each position, is minimized by graph-cut segmentation to detect shadows. In evaluations on KITTI and Waymo datasets, our shadow-detection method outperformed the previous methods in terms of multiple evaluation indices.
Functional Shell and Reusable Components for Easy GUIs

Some object-oriented GUI toolkits tangle state management with rendering. Functional shells and observable toolkits like GUI Easy simplify and promote the creation of reusable views by analogy to functional programming. We have successfully used GUI Easy on small and large GUI projects. We report on our experience constructing and using GUI Easy and derive from that experience several architectural patterns and principles for building functional programs out of imperative systems.
The Beauty and Elegance of Functional Reactive Animation

It has been over 20 years since Elliott and Hudak published Functional Reactive Animation, which outlined the principles of interactive programming in functional languages. As a result, Functional Reactive Programming (FRP) has seen numerous implementations and has been applied to multiple areas, like robotics, physics simulations, game programming and user interfaces. The use of the term FRP has itself broadened, and nowadays covers both continuous-time purely functional abstractions and discrete-time reactive implementations. This paper presents a series of increasingly complex FRP animations in a current implementation. With a main focus on clarity and meaning, we explore three independent dimensions: space, time, and color. We demonstrate that, when embraced fully, Functional Programming can result in declarative constructs that are aesthetically beautiful and notationally elegant.
Homotopy Type Theory for Sewn Quilts

This paper introduces PieceWork, an imperative programming language for the construction of designs for sewn quilts, whose semantics are inspired by Homotopy Type Theory. The goals of PieceWork include improving the diversity of sewn designs that can be represented in computational methods, demonstrating a creative application of Homotopy Type Theory, and demonstrating that the craft of quilting is a worthy object of study in programming language theory. We develop an operational semantics, provide a prototype implementation and examples, and provide initial theoretical results. Type system design is in-progress.
Visual displays for communicating scientific uncertainty in influenza forecasts

We offer a general method for testing the usability of visual displays communicating scientific uncertainty, illustrated with publicly available results from CDC's influenza forecasts. The heavy toll of seasonal influenza has prompted major investments in improving these forecasts, making them a focus of machine learning research. However, little research has been devoted to how well users can understand and use these forecasts to inform decisions under uncertainty. Our approach extends psychological theory to experimental tasks posing hypothetical, but realistic decisions using alternative displays based on actual forecasts. Based on Tversky's theory of conceptual-spatial congruence, we predicted actual and perceived usability of four displays (bar chart, tree map, PDF, and 90% confidence interval). Participants ( N = 301, recruited on Amazon MTurk) were randomly assigned to use one of four displays for four decision tasks, created to reflect our extension of the theory. We evaluated participants' comprehension, confidence, and judgments of perceived helpfulness, when the display and the decision were congruent or non-congruent. Participants had better comprehension with the most familiar display (bar chart), for all four decisions. However, they did not perceive that display as more helpful or have greater confidence in their responses to it. Participants who reported greater familiarity with a display performed more poorly, despite expressing greater confidence and rating it as more helpful. We discuss the need to evaluate performance, as well as ratings, and the opportunities to extend theoretical frameworks to specific contexts.
Mria: An Eventually Consistent Mnesia

Mnesia, an Erlang distributed database, serves as an embedded storage and replication layer for OTP applications requiring low read latency and high availability. EMQX is a publish-subscribe message broker supporting the MQTT protocol that uses Mnesia to replicate its internal state across the cluster. We analyze the limitations of Mnesia's replication protocol scalability in large clusters under high load. To address these limitations, we developed Mria, an extension to the Mnesia database that provides eventual consistency within a cluster and achieves better horizontal scalability. We validated Mria using a variety of testing techniques, including model checking, chaos engineering, and formal verification. Replacing Mnesia with Mria allowed us to scale the EMQX cluster to 23 nodes, handle 100 million simultaneous client sessions and achieve a higher sustained load.
Shape-Constrained Array Programming with Size-Dependent Types

We present a dependent type system for enforcing array-size consistency in an ML-style functional array language. Our goal is to enforce shape-consistency at compile time and allow nontrivial transformations on array shapes, without the complexity such features tend to introduce in dependently typed languages. Sizes can be arbitrary expressions and size equality is purely syntactical, which fits naturally within a scheme that interprets size-polymorphic functions as having implicit arguments. When non-syntactical equalities are needed, we provide dynamic checking. In contrast to other dependently typed languages, we automate the book-keeping involved in tracking existential sizes, such as when filtering arrays. We formalise a large subset of the presented type system and prove it sound. We also discuss how to adapt the type system for a real implementation, including type inference, within the Futhark programming language.
Dependently-Typed Programming with Logical Equality Reflection

In dependently-typed functional programming languages that allow general recursion, programs used as proofs must be evaluated to retain type soundness. As a result, programmers must make a trade-off between performance and safety. To address this problem, we propose System DE, an explicitly-typed, moded core calculus that supports termination tracking and equality reflection. Programmers can write inductive proofs about potentially diverging programs in a logical sublanguage and reflect those proofs to the type checker, while knowing that such proofs will be erased by the compiler before execution. A key feature of System DE is its use of modes for both termination and relevance tracking, which not only simplifies the design but also leaves it open for future extension. System DE is suitable for use in the Glasgow Haskell Compiler, but could serve as the basis for any general purpose dependently-typed language.
More Fixpoints! (Functional Pearl)

Haskell’s laziness allows the programmer to solve some problems naturally and declaratively via recursive equations. Unfortunately, if the input is “too recursive”, these very elegant idioms can fall into the dreaded black hole, and the programmer has to resort to more pedestrian approaches. It does not have to be that way: We built variants of common pure data structures (Booleans, sets) where recursive definitions are productive. Internally, the infamous unsafePerformIO is at work, but the user only sees a beautiful and pure API, and their pretty recursive idioms – magically – work again.
Formal Specification and Testing for Reinforcement Learning

The development process for reinforcement learning applications is still exploratory rather than systematic. This exploratory nature reduces reuse of specifications between applications and increases the chances of introducing programming errors. This paper takes a step towards systematizing the development of reinforcement learning applications. We introduce a formal specification of reinforcement learning problems and algorithms, with a particular focus on temporal difference methods and their definitions in backup diagrams. We further develop a test harness for a large class of reinforcement learning applications based on temporal difference learning, including SARSA and Q-learning. The entire development is rooted in functional programming methods; starting with pure specifications and denotational semantics, ending with property-based testing and using compositional interpreters for a domain-specific term language as a test oracle for concrete implementations. We demonstrate the usefulness of this testing method on a number of examples, and evaluate with mutation testing. We show that our test suite is effective in killing mutants (90% mutants killed for 75% of subject agents). More importantly, almost half of all mutants are killed by generic write-once-use-everywhere tests that apply to any reinforcement learning problem modeled using our library, without any additional effort from the programmer.
A Graded Modal Dependent Type Theory with a Universe and Erasure, Formalized

We present a graded modal type theory, a dependent type theory with grades that can be used to enforce various properties of the code. The theory has Π-types, weak and strong Σ-types, natural numbers, an empty type, and a universe, and we also extend the theory with a unit type and graded Σ-types. The theory is parameterized by a modality, a kind of partially ordered semiring, whose elements (grades) are used to track the usage of variables in terms and types. Different modalities are possible. We focus mainly on quantitative properties, in particular erasure: with the erasure modality one can mark function arguments as erasable. The theory is fully formalized in Agda. The formalization, which uses a syntactic Kripke logical relation at its core and is based on earlier work, establishes major meta-theoretic properties such as subject reduction, consistency, normalization, and decidability of definitional equality. We also prove a substitution theorem for grade assignment, and preservation of grades under reduction. Furthermore we study an extraction function that translates terms to an untyped λ-calculus and removes erasable content, in particular function arguments with the “erasable” grade. For a certain class of modalities we prove that extraction is sound, in the sense that programs of natural number type have the same value before and after extraction. Soundness of extraction holds also for open programs, as long as all variables in the context are erasable, the context is consistent, and erased matches are not allowed for weak Σ-types.
LURK: Lambda, the Ultimate Recursive Knowledge (Experience Report)

We introduce Lurk, a new LISP-based programming language for zk-SNARKs. Traditional approaches to programming over zero-knowledge proofs require compiling the desired computation into a flat circuit, imposing serious constraints on the size and complexity of computations that can be achieved in practice. Lurk programs are instead provided as data to the universal Lurk interpreter circuit, allowing the resulting language to be Turing-complete without compromising the size of the resulting proof artifacts. Our work describes the design and theory behind Lurk, along with detailing how its implementation of content addressing can be used to sidestep many of the usual concerns of programming zero-knowledge proofs.