Reviewerly/exHarmony huggingface dataset:
Brief Introduction to Propositional Logic and Predicate Calculus

Many recent neuro symbolic approaches rely on an underlying logical language. In this chapter, we provide a brief introduction to the basic concepts behind propositional logic and predicate calculus (first order logic). We cover syntax, semantics, satisfiability relationships, consistency and entailment, quantifiers and grounding, as well as how logic relates to lattice theory and fixpoint operators.
Neuro Symbolic Learning with Differentiable Inductive Logic Programming

In this chapter, we describe how a logic program can be learned from data in a neuro symbolic framework. Our focus is on the gradient-based method known as differentiable inductive logic programming (ILP), which combines concepts from ILP with a neural architecture to support gradient-based learning. Additionally, we also cover several other paradigms to learn logical structures in a neuro symbolic framework.
Neuro Symbolic Reasoning with Ontological Networks

In this chapter we describe neuro symbolic approaches developed for ontological domains, focusing mainly on Recurrent Reasoning Networks (RRNs), a formalism that was recently developed with the goal of automated learning of deep neural networks capable of deciding ontological entailment. After describing the intuition and main details of RRNs, we conclude by briefly mentioning other related approaches, such as Probabilistic Soft Logic (PSL, a formalism based on statistical relational learning) and its recent extension called NeuPSL.
LNN: Logical Neural Networks

Logical Neural Networks (LNN) is a framework that assumes knowledge of a logic program a-priori and uses gradient descent to fit the logic program to training data via parameterized logical operators, resulting in fuzzy logic semantics. The framework has several desirable properties, namely the ability to support open world reasoning, omnidirectional inference, and explainability. While consistency cannot be guaranteed, LNN’s use an additional term in the loss function to minimize inconsistencies in their approach. In this chapter, we review the foundations of LNN’s and discuss the architectural decisions that make LNN’s comparable and different from other neuro symbolic approaches.
LTN: Logic Tensor Networks

In this chapter, we provide an overview of Logic Tensor Networks (LTNs, for short), a formalism that makes use of tensor embeddings—n-dimensional vector representations—of elements tied to a logical syntax, which has seen traction in NSR literature in the past few years. After briefly recalling Real Logic, the underlying language of LTNs, we discuss the representation of different kinds of knowledge in formalism, the three main tasks that can be addressed with them (learning, reasoning, and query answering), and finally, describe several use cases that have shown the usefulness of LTNs in many tasks that are central to the construction of intelligent systems.
Neuro Symbolic AI for Sequential Decision Making

Deep learning based approaches have been used to address several problems of a sequential nature, whether using supervised learning to learn a model of a time-series system or using reinforcement learning to train an agent. However, this work has raised questions of generalizability, explainability, and verification. In this chapter, we review work on deep symbolic policies and verification of neural models by symbolic means, which represent some of the ongoing approaches to these problems.
Collecting Multi-type and Correlation-Constrained Streaming Sensor Data with Local Differential Privacy

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It has been widely deployed in real-world systems (e.g. Chrome, iOS, macOS). In LDP-based mechanisms, an aggregator collects private values perturbed by each user and then analyses these values to estimate their statistics, such as frequency and mean. Most existing works focus on simple scalar value types, such as boolean and categorical values. However, with the emergence of smart sensors and Internet of Things, high-dimensional data are gaining increasing popularity. In many cases where more than one type of sensor data are collected simultaneously, correlations exist between various attributes of such data, e.g. temperature and luminance. To ensure LDP for high-dimensional data, existing solutions either partition the privacy budget ϵ among these correlated attributes or adopt sampling, both of which dilute the density of useful information and thus result in poor data utility. In this paper, we propose a relaxed LDP model, namely, univariate dominance local differential privacy (UDLDP), for high-dimensional data. We quantify the correlations between attributes and present a correlation-bounded perturbation (CBP) mechanism that optimizes the partitioning of privacy budget on each correlated attribute. Furthermore, we extend CBP to support sampling, which is a common bandwidth reduction technique in sensor networks and Internet of Things. We derive the best allocation strategy of sampling probabilities among attributes in terms of data utility, which leads to the correlation-bounded perturbation mechanism with sampling (CBPS). Finally, we discuss how to collect and leverage the correlation from real-time data stream with a by-round algorithm to enhance the utility. The performance of the proposed mechanisms is evaluated and compared with state-of-the-art LDP mechanisms on real-world and synthetic datasets.
NeurASP

In this chapter, we explore a neuro symbolic approach to incorporate explicit knowledge about a domain while learning a model. Specifically, we use a differentiable extension of the declarative problem solving language ASP (Answer Set Programming) that is inspired by Logic Programming. In this chapter, we review basic concepts in ASP and describe a differentiable extension known as “NeurASP.”
ASIDS: A Robust Data Synthesis Method for Generating Optimal Synthetic Samples

Most existing data synthesis methods are designed to tackle problems with dataset imbalance, data anonymization, and an insufficient sample size. There is a lack of effective synthesis methods in cases where the actual datasets have a limited number of data points but a large number of features and unknown noise. Thus, in this paper we propose a data synthesis method named Adaptive Subspace Interpolation for Data Synthesis (ASIDS). The idea is to divide the original data feature space into several subspaces with an equal number of data points, and then perform interpolation on the data points in the adjacent subspaces. This method can adaptively adjust the sample size of the synthetic dataset that contains unknown noise, and the generated sample data typically contain minimal errors. Moreover, it adjusts the feature composition of the data points, which can significantly reduce the proportion of the data points with large fitting errors. Furthermore, the hyperparameters of this method have an intuitive interpretation and usually require little calibration. Analysis results obtained using simulated original data and benchmark original datasets demonstrate that ASIDS is a robust and stable method for data synthesis.
Safety Integrity Levels for Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) technologies are rapidly being adopted to perform safety-related tasks in critical systems. These AI-based systems pose significant challenges, particularly regarding their assurance. Existing safety approaches defined in internationally recognized standards such as ISO 26262, DO-178C, UL 4600, EN 50126, and IEC 61508 do not provide detailed guidance on how to assure AI-based systems. For conventional (non-AI) systems, these standards adopt a ‘Level of Rigor’ (LoR) approach, where increasingly demanding engineering activities are required as risk associated with the system increases. This paper proposes an extension to existing LoR approaches, which considers the complexity of the task(s) being performed by an AI-based component. Complexity is assessed in terms of input entropy and output non-determinism, and then combined with the allocated Safety Integrity Level (SIL) to produce an AI-SIL. That AI-SIL may be used to identify appropriate measures and techniques for the development and verification of the system. The proposed extension is illustrated by examples from the automotive, aviation, and medical industries.
Trustworthy Anti-Collusion Federated Learning Scheme Optimized by Game Theory

Federated learning, a decentralized paradigm, offers the potential to train models across multiple devices while preserving data privacy. However, challenges such as malicious actors and model parameter leakage have raised concerns. To tackle these issues, we introduce a game-theoretic, trustworthy anti-collusion federated learning scheme, which combines game-theoretic techniques and rational trust models with functional encryption and smart contracts for enhanced security. Our empirical evaluations, using datasets like MNIST, CIFAR-10, and Fashion MNIST, underscore the influence of data distribution on performance, with IID setups outshining non-IID ones. The proposed scheme also showcased scalability across diverse client counts, adaptability to various tasks, and heightened security through game theory. A critical observation was the trade-off between privacy measures and optimal model performance. Overall, our findings highlight the scheme’s capability to bolster federated learning’s robustness and security.
Distributed, Decentralized Registries and Digital State. Are We Moving Toward a Blockchain Democracy?

Blockchain technology is a data storage architecture and ensures the immutability of records. It is used today primarily for cryptocurrency transactions but more and more often as well in the functioning of e-government. The development of the digital state generates a few challenges in collecting, processing data, while there is a high risk of data loss, damage, or theft. According to many researchers, blockchain technology could simplify the management of sensitive data, minimize costs, and generate new opportunities, while maintaining security. Importantly, the discussed technology can be used to shift the paradigm of democracy toward greater security and openness. If this is the case, then many democratic processes or mechanisms, such as information provision, communication, voting in elections, can be modified by this technology. However, it is important to keep in mind that these solutions are not without risks. The electoral process appears to be a sensitive area in this context. In the academic debate, there are also more and more voices on the possibility of using distributed and decentralized registries to support the development of e-democracy. This topic is not fully recognized—researchers create new concepts of i-voting systems based on blockchain, while others discourage this technology. However, it is not so today that blockchain technologies are only one of the potential ways to optimize the functioning of e-administration and support civic. The purpose of this paper is to attempt to show the opportunities and risks of blockchain technology, with a particular focus on the impact on democracy.
Patterns for Integrating NIST 800-53 Controls into Security Assurance Cases

It is sure that critical systems are appropriately secure and protected against malicious threats. In this paper, we present a novel pattern for Security Assurance Cases that integrates security controls from the NIST-800-53 cyber security standard into a comprehensive argument about system security. Our framework uses Eliminative Argumentation to increase confidence that these controls have been applied correctly by explicitly considering and addressing doubts in the argument.
Can Large Language Models Assist in Hazard Analysis?

Large Language Models (LLMs), such as GPT-3, have demonstrated remarkable natural language processing and generation capabilities and have been applied to a variety tasks, such as source code generation. This paper explores the potential of integrating LLMs in the hazard analysis for safety-critical systems, a process which we refer to as co-hazard analysis (CoHA). In CoHA, a human analyst interacts with an LLM via a context-aware chat session and uses the responses to support elicitation of possible hazard causes. In a preliminary experiment, we explore CoHA with three increasingly complex versions of a simple system, using Open AI’s ChatGPT service. The quality of ChatGPT’s responses were systematically assessed to determine the feasibility of CoHA given the current state of LLM technology. The results suggest that LLMs may be useful for supporting human analysts performing hazard analysis.
Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence

Various neuro symbolic approaches such as Logical Neural Networks, Logical Tensor Networks, differentiable ILP, and others rely on the use of several forms of real-valued logic and fuzzy operators. In this chapter, we review generalized annotated logic that encompasses the various logics used in neuro symbolic frameworks as well as the fuzzy operators leveraged as connectives.
Nudges, emojis, and memes: Mapping interpassivity theory onto digital civic culture

Once lauded for liberating audiences from their passive state by granting voice, the digital public sphere today increasingly resembles a cacophony of disjointed voices datafied for the gain of giant tech firms. Instead of bemoaning the co-optation of users’ activity, we might find it timely to reconsider the possibility that users’ passivity might be at stake, the key assumption of interpassivity theory implying a delegation of one’s affective subject position under seeming interactive practices. This article reviews burgeoning research on interpassivity and brings it to bear on pervasive phenomena in digital interactive environments: nudges, emojis, and memes. Tracing the artificial canned laughter of the broadcast era to the famous internet adage, Poe’s law, I argue that strategic ambivalence, vitriolic joking, and irony weaponized by far-right online subcultures exploit the interactive nudge logic of social media affordances. The means of expressing authentic feelings afforded by digital media entail a fissure that trades in illusions which users disavow. This explains how surprising phenomena beyond belief, such as “meme magic,” gain symbolic power. The theoretical takeaway upends the key premise of cultural participation theory suggesting that rather than serving as a precondition, relentless interactivity might paradoxically undermine the democratic ethos of participatory culture.
Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning?

With the increasing use of Artificial Intelligence (AI), the dependability of AI-based software components becomes a key factor, especially in the context of safety-critical applications. However, as current AI-based models are data-driven, there is an inherent uncertainty associated with their outcomes. Some in-model uncertainty quantification (UQ) approaches integrate techniques during model construction to obtain information about the uncertainties during inference, e.g., deep ensembles, but do not provide probabilistic guarantees. Two model-agnostic UQ approaches that both provide probabilistic guarantees are conformal prediction (CP), and uncertainty wrappers (UWs). Yet, they differentiate in the type of quantifications they provide. CP provides sets or regions containing the intended outcome with a given probability, UWs provide uncertainty estimates for point predictions. To investigate how well they perform compared to each other and a baseline in-model UQ approach, we provide a side-by-side comparison based on their key characteristics. Additionally, we introduce an approach combining UWs with CP. The UQ approaches are benchmarked with respect to point uncertainty estimates, and to prediction sets. Regarding point uncertainty estimates, the UW shows the best reliability as CP was not designed for this task. For the task of providing prediction sets, the combined approach of UWs with CP outperforms the other approaches with respect to adaptivity and conditional coverage.
New Ideas in Neuro Symbolic Reasoning and Learning

Neuro symbolic reasoning and learning is a topic that combines ideas from deep neural networks with symbolic reasoning and learning to overcome several significant technical hurdles such as explainability, modularity, verification, and the enforcement of constraints. While neuro symbolic ideas date back to the early 2000’s, there have been significant advances in the last 5 years. In this chapter, we outline some of these advancements and discuss how they align with several taxonomies for neuro symbolic reasoning.
Comparison study of data adaptability based on k-nearest neighbor(KNN), multilayer perceptron(MLP), and decision tree(DT)

In the current context of increasingly popular and developed human-computer interaction, machine learning (ML) and data mining are becoming more and more important in various areas, such as image detecting, medicine, and commercial companies. Different researchers from various fields have considered ways to improve the correctness rate for data mining. This paper used data sets from Kaggle and UCI machine learning repository, by applying k-nearest neighbor (KNN), multilayer perceptron (MLP), and decision tree (DT) classifiers to these data sets, the confusion matrix shows the result of the correctness rate for different classifiers under these data sets. As a result, the confusion matrices have shown that data adaptability is based on the data sets characteristics which rely on different classifiers specialties.
DFA-SAT: Dynamic Feature Abstraction with Self-Attention-Based 3D Object Detection for Autonomous Driving

Autonomous vehicles (AVs) play a crucial role in enhancing urban mobility within the context of a smarter and more connected urban environment. Three-dimensional object detection in AVs is an essential task for comprehending the driving environment to contribute to their safe use in urban environments. Existing 3D LiDAR object detection systems lose many critical point features during the down-sampling process and neglect the crucial interactions between local features, providing insufficient semantic information and leading to subpar detection performance. We propose a dynamic feature abstraction with self-attention (DFA-SAT), which utilizes self-attention to learn semantic features with contextual information by incorporating neighboring data and focusing on vital geometric details. DFA-SAT comprises four modules: object-based down-sampling (OBDS), semantic and contextual feature extraction (SCFE), multi-level feature re-weighting (MLFR), and local and global features aggregation (LGFA). The OBDS module preserves the maximum number of semantic foreground points along with their spatial information. SCFE learns rich semantic and contextual information with respect to spatial dependencies, refining the point features. MLFR decodes all the point features using a channel-wise multi-layered transformer approach. LGFA combines local features with decoding weights for global features using matrix product keys and query embeddings to learn spatial information across each channel. Extensive experiments using the KITTI dataset demonstrate significant improvements over the mainstream methods SECOND and PointPillars, improving the mean average precision (AP) by 6.86% and 6.43%, respectively, on the KITTI test dataset. DFA-SAT yields better and more stable performance for medium and long distances with a limited impact on real-time performance and model parameters, ensuring a transformative shift akin to when automobiles replaced conventional transportation in cities.
PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST

With so many airlines competing with each other, airlines are competing to become the consumer/market's main choice, but to achieve this, there is no airline strategy that can predict the price of airline tickets according to market needs. To meet the needs of airlines, we need a way to determine the price of airline tickets according to market needs with the help of the influence of technology and information. This research method was carried out using Google Collaboratory as a media to create a data model with the Random Forest, Logistic Regression and Gradient Boosting Regressor algorithms. In this study, the model that produced the highest R2 value and the lowest RMSE was a random forest with an R2 value of 83.91% and an RMSE of $175.9. However, from the three models, Random Forest got a change in accuracy of 1.96% to 85.87. To assist in predicting the determination of flight fares, airline companies can more easily and be alert to determine flight fares that are in accordance with the market. Therefore, Random Forest can be declared better than Logistic Regression and Gradient Boosting models. The Random Forest model that has been created can be used to predict in real-time using Machine Learning.
Implementasi Algoritma K-Nearest Neigbor Untuk Klasifikasi Pengajuan Kredit

Unsecured loans are the community's choice for lending to banks that provide these services. PT. BPR Diori Ganda is a regional private banking company that serves savings and loans and loans without collateral for the community. Submission of unsecured loans must go through an assessor team to process the analysis of the attributes that affect the customer's classification so that credit can be approved, which is then submitted to the commissioner for credit approval. But what if those who apply for credit on the same day in large amounts, of course this will make the process of credit analysis and approval will take a long time. If it is seen from the many needs of the community to apply for loans without collateral, a classification application is needed, in order to facilitate the work of the assessor team in the process of analyzing the attributes that affect customer classification. To find out the classification of customers who apply for unsecured loans using data mining with the K-Nearest Neighbor algorithm. The result of this research is the classification of problematic or non-performing customers for credit applications without collateral
PERANCANGAN MEDIA BELAJAR INSTRUCTIONAL GAMES UNTUK PENINGKATAN MINAT BELAJAR SISWA SMA

Games that are interesting and fun to play make the interest of students to prefer playing games rather than studying. Submission of teaching materials in a conventional way will be boring to be followed by students in class. It is necessary to improve teaching methods that are able to attract students' interest in learning to be motivated in participating in teaching and learning activities. The design of interactive learning media with the instructional games model aims to increase the interest of students so that they can receive teaching materials in a good and fun way. The method used in this research is Research and Development (R&amp;D). To analyze students' learning motivation, a field survey questionnaire was used which was given to 33 students of Class XI SMA Negeri 1 Plered. There has been an increase of 8.22%, where the initial motivation of students is at the percentage of 62.88% increasing to a percentage of 71.10%, which can also be interpreted that the use of interactive instructional games learning media is able to increase student learning motivation.