Reviewerly/exHarmony huggingface dataset:
Novel Results on Global Robust Stability Analysis for Dynamical Delayed Neural Networks Under Parameter Uncertainties

In this paper, we focus on the global stability analysis with respect to dynamical delayed neural networks (NNs) that contain parameter uncertainties.Many investigations on the sufficient conditions utilizing different upper bounds for the norm of interconnection matrices pertaining to the global asymptotic robust stability of delayed NNs have been conducted.In this study, a new upper bound of the norm of connection weight matrices is derived for the delayed NNs under parameter uncertainties.The key focus is on how the new upper bound is able to yield minimum result with respects to some of the existing upper bounds.We demonstrate that the new upper bound can lead to some new sufficient conditions with respect to the global asymptotic robust stability of equilibrium point of the delayed NNs.The slope bounded activation functions and Lyapunov-Krasovskii functionals (LKFs) are employed for formulating the sufficient conditions of the equilibrium point of NNs.Moreover, the derived sufficient conditions are independent on the time delay parameter.Numerical examples are provided and the outcomes obtained are compared with those of the existing results subject to different network parameters.
Optional Stopping with Bayes Factors: A Categorization and Extension of Folklore Results, with an Application to Invariant Situations

It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary probability theory, of three different mathematical meanings that various authors give to this claim: (1) stopping rule independence, (2) posterior calibration and (3) (semi-) frequentist robustness to optional stopping. We then prove theorems to the effect that these claims do indeed hold in a general measure-theoretic setting. For claims of type (2) and (3), such results are new. By allowing for non-integrable measures based on improper priors, we obtain particularly strong results for the practically important case of models with nuisance parameters satisfying a group invariance (such as location or scale). We also discuss the practical relevance of (1)–(3), and conclude that whether Bayes factor methods actually perform well under optional stopping crucially depends on details of models, priors and the goal of the analysis.
Inductive Logic Programming At 30: A New Introduction

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.
Syntax Splitting = Relevance + Independence: New Postulates for Nonmonotonic Reasoning From Conditional Belief Bases

Syntax splitting, first introduced by Parikh in 1999, is a natural and desirable property of KR systems. Syntax splitting combines two aspects: it requires that the outcome of a certain epistemic operation should only depend on relevant parts of the underlying knowledge base, where relevance is given a syntactic interpretation (relevance). It also requires that strengthening antecedents by irrelevant information should have no influence on the obtained conclusions (independence). In the context of belief revision the study of syntax splitting already proved useful and led to numerous new insights. In this paper we analyse syntax splitting in a different setting, namely nonmonotonic reasoning based on conditional knowledge bases. More precisely, we analyse inductive inference operators which, like system P, system Z, or the more recent c-inference, generate an inference relation from a conditional knowledge base. We axiomatize the two aforementioned aspects of syntax splitting, relevance and independence, as properties of such inductive inference operators. Our main results show that system P and system Z, whilst satisfying relevance, fail to satisfy independence. C-inference, in contrast, turns out to satisfy both relevance and independence and thus fully complies with syntax splitting.
Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenario-based design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.
Quantum parameter estimation in a dissipative environment

We investigate the performance of quantum parameter estimation based on a qubit probe in a dissipative bosonic environment beyond the traditional paradigm of weak-coupling and rotating-wave approximations. By making use of an exactly numerical hierarchical equations of motion method, we analyze the influences of the non-Markovian memory effect induced by the environment and the form of probe-environment interaction on the estimation precision. It is found that (i) the non-Markovianity can effectively boost the estimation performance and (ii) the estimation precision can be improved by introducing a perpendicular probe-environment interaction. Our results indicate the scheme of parameter estimation in a noisy environment can be optimized via engineering the decoherence mechanism.
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks

Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. We propose a graph neural network architecture for predicting object importance in a single inference pass, thus incurring little overhead while greatly reducing the number of objects that must be considered by the planner. Our approach treats the planner and transition model as black boxes, and can be used with any off-the-shelf planner. Empirically, across classical planning, probabilistic planning, and robotic task and motion planning, we find that our method results in planning that is significantly faster than several baselines, including other partial grounding strategies and lifted planners. We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances. Video: https://youtu.be/FWsVJc2fvCE Code: https://git.io/JIsqX
Malicious and Benign Webpages Dataset

Web Security is a challenging task amidst ever rising threats on the Internet. With billions of websites active on Internet, and hackers evolving newer techniques to trap web users, machine learning offers promising techniques to detect malicious websites. The dataset described in this manuscript is meant for such machine learning based analysis of malicious and benign webpages. The data has been collected from Internet using a specialized focused web crawler named MalCrawler [1]. The dataset comprises of various extracted attributes, and also raw webpage content including JavaScript code. It supports both supervised and unsupervised learning. For supervised learning, class labels for malicious and benign webpages have been added to the dataset using the Google Safe Browsing API. The most relevant attributes within the scope have already been extracted and included in this dataset. However, the raw web content, including JavaScript code included in this dataset supports further attribute extraction, if so desired. Also, this raw content and code can be used as unstructured data input for text-based analytics. This dataset consists of data from approximately 1.5 million webpages, which makes it suitable for deep learning algorithms. This article also provides code snippets used for data extraction and its analysis.
The problem-based learning e-module of planes using Kvisoft Flipbook Maker for elementary school students

The conduct of the present study is encouraged by the needs to afford the teaching materials in the form of an electronic learning module or e-module that can be combined with the problem-based learning approach in order to expand the students’ insights in and draw the students’ interest toward Mathematics. These needs should be afforded because the present teaching materials used in the learning process are still conventional and, consequently, the students become easily bored. Departing from this elaboration, the objective of the present study is to develop the teaching materials, especially in the subject “Simple Planes” in the Third Grade, and to measure the validity, the practicality, and the effectiveness of the media that have been developed. In conducting the study, the ADDIE model, standing for Analysis, Design, Development, Implementation, and Evaluation, had been adopted and the data were gathered by means of validation sheet and response questionnaire. After the data had been gathered, the data were analysed by using the qualitative and the quantitative data analysis technique. The results of the study show that the problem-based learning (PBL)-based planes e-module using the KVISFOT Flipbook Maker that has been developed is already valid with the mean score of 85.82%. At the same time, the results of the response category show that the problem-based learning (PBL)-based planes e-module using the KVISFOT Flipbook Maker that has been developed is already good with the mean score 3.78. Furthermore, the mean score from the effectiveness test using the evaluation test for the Third-Grade students is 90.47. Therefore, it can be concluded that the e-module is able to improve the students’ learning motivation so that the learning process can proceed fluently.
Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods.
Revisiting heterogeneous defect prediction methods: How far are we?

Context: Cross-project defect prediction applies to the scenarios that the target projects are new projects. Most of the previous studies tried to utilize the training data from other projects (i.e., the source projects). However, metrics used by practitioners to measure the extracted program modules from different projects may not be the same, and performing heterogeneous defect prediction (HDP) is challenging. Objective: Researchers have proposed many novel HDP methods with promising performance until now. Recently, unsupervised defect prediction (UDP) methods have received more attention and show competitive performance. However, to our best knowledge, whether HDP methods can perform significantly better than UDP methods has not yet been thoroughly investigated. Method: In this article, we perform a comparative study to have a holistic look at this issue. Specifically, we compare five HDP methods with four UDP methods on 34 projects in five groups under the same experimental setup from three different perspectives: non-effort-aware performance indicators (NPIs), effort-aware performance indicators (EPIs) and diversity analysis on identifying defective modules. Result: We have the following findings: (1) HDP methods do not perform significantly better than some of UDP methods in terms of two NPIs and four EPIs. (2) According to two satisfactory criteria recommended by previous studies, the satisfactory ratio of existing HDP methods is pessimistic. (3) The diversity of prediction for defective modules across HDP vs. UDP methods is more than that within HDP methods or UDP methods. Conclusion: The above findings implicate there is still a long way for the HDP issue to go. Given this, we present some observations about the road ahead for HDP.
Artificial Intelligence Security Threat, Crime, and Forensics: Taxonomy and Open Issues

Advances in Artificial Intelligence (AI) have influenced almost every field including computer science, robotics, social engineering, psychology, criminology and so on.Although AI has solved various challenges, potential security threats of AI algorithms and training data have been stressed by AI researchers.As AI system inherits security threats of traditional computer system, the concern about novel cyberattack enhanced by AI is also growing.In addition, AI is deeply connected to physical space (e.g.autonomous vehicle, intelligent virtual assistant), so AI-related crime can harm people physically, beyond the cyberspace.In this context, we represent a literature review of security threats and AI-related crime.Based on the literature review, this article defines the term AI crime and classifies AI crime into 2 categories: AI as tool crime and AI as target crime, inspired by a taxonomy of cybercrime: Computer as tool crime and Computer as tool crime.Through the proposed taxonomy, foreseeable AI crimes are systematically studied and related forensic techniques are also addressed.We also analyze the characteristics of the AI crimes and present challenges that are difficult to be solved with the traditional forensic techniques.Finally, open issues are presented, with emphasis on the need to establish novel strategies for AI forensics.
A privacy-preserving protocol for continuous and dynamic data collection in IoT enabled mobile app recommendation system (MARS)

User trust is an important factor in the success of recommendation systems, including Internet of Things (IoT)-based recommendation systems. However, such trust can be eroded in many different ways (e.g., unauthorized data modifications). Several privacy-preservation schemes have been designed for specific data and/or require strict assumptions (e.g., a private/secure communication channel between client-server and third-party authentication). However, these may limit their application in practice. Hence, in this paper we propose the Reversible Data Transform (RDT) algorithm based privacy-preserving data collection protocol. Our protocol allows us to achieve privacy preservation against beyond the scope processing and does not require a private channel or rely on a third-party authentication. Due to group formation, the disclosure probability of the internal disclosure attack will not be greater than 1/k. Similarly, the reversible privacy-preserving data mining approach protects beyond the scope processing. Findings from the experimentation demonstrates the utility of the proposed protocol and its potential to be deployed in a mobile app recommendation system.
Multi-Encoder-Decoder Transformer for Code-Switching Speech Recognition

Code-switching (CS) occurs when a speaker alternates words of two or more languages within a single sentence or across sentences. Automatic speech recognition (ASR) of CS speech has to deal with two or more languages at the same time. In this study, we propose a Transformer-based architecture with two symmetric language-specific encoders to capture the individual language attributes, that improve the acoustic representation of each language. These representations are combined using a language-specific multi-head attention mechanism in the decoder module. Each encoder and its corresponding attention module in the decoder are pre-trained using a large monolingual corpus aiming to alleviate the impact of limited CS training data. We call such a network a multi-encoder-decoder (MED) architecture. Experiments on the SEAME corpus show that the proposed MED architecture achieves 10.2% and 10.8% relative error rate reduction on the CS evaluation sets with Mandarin and English as the matrix language respectively.
Experimental quantum polarimetry using heralded single photons

We perform experimental quantum polarimetry using a heralded single photon to analyze the optical activity of linearly polarized light traversing a chiral medium. Three kinds of estimators are considered to estimate the concentrations of sucrose solutions from measuring the rotation angle of the linear polarization of the output photons. Through repetition of independent and identical measurements performed for each individual scheme and different concentration sucrose solutions, we compare the estimation uncertainty among the three schemes. The results are also compared to classical benchmarks for which a coherent state of light is taken into account. The quantum enhancement in the estimation uncertainty is evaluated and the impact of experimental and technical imperfections is discussed. In this work, we lay out a route for future applications relying on quantum polarimetry.
ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT

In this paper, we propose an evaluation metric for image captioning systems using both image and text information.Unlike the previous methods that rely on textual representations in evaluating the caption, our approach uses visiolinguistic representations.The proposed method generates image-conditioned embeddings for each token using ViLBERT from both generated and reference texts.Then, these contextual embeddings from each of the two sentence-pair are compared to compute the similarity score.Experimental results on three benchmark datasets show that our method correlates significantly better with human judgments than all existing metrics.
Interactive Refinement of Cross-Lingual Word Embeddings

Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages.We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem.First, CLIME ranks words by their salience to the downstream task.Then, users mark similarity between keywords and their nearest neighbors in the embedding space.Finally, CLIME updates the embeddings using the annotations.We evaluate CLIME on identifying health-related text in four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur.Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings.CLIME often improves accuracy faster than an active learning baseline and can be easily combined with active learning to improve results.
Restarting the accelerated coordinate descent method with a rough strong convexity estimate

We propose new restarting strategies for the accelerated coordinate descent method. Our main contribution is to show that for a well chosen sequence of restarting times, the restarted method has a nearly geometric rate of convergence. A major feature of the method is that it can take profit of the local quadratic error bound of the objective function without knowing the actual value of the error bound. We also show that under the more restrictive assumption that the objective function is strongly convex, any fixed restart period leads to a geometric rate of convergence. Finally, we illustrate the properties of the algorithm on a regularized logistic regression problem and on a Lasso problem.
Using score distributions to compare statistical significance tests for information retrieval evaluation

Statistical significance tests can provide evidence that the observed difference in performance between 2 methods is not due to chance. In information retrieval (IR), some studies have examined the validity and suitability of such tests for comparing search systems. We argue here that current methods for assessing the reliability of statistical tests suffer from some methodological weaknesses, and we propose a novel way to study significance tests for retrieval evaluation. Using Score Distributions, we model the output of multiple search systems, produce simulated search results from such models, and compare them using various significance tests. A key strength of this approach is that we assess statistical tests under perfect knowledge about the truth or falseness of the null hypothesis. This new method for studying the power of significance tests in IR evaluation is formal and innovative. Following this type of analysis, we found that both the sign test and Wilcoxon signed test have more power than the permutation test and the t‐ test. The sign test and Wilcoxon signed test also have good behavior in terms of type I errors. The bootstrap test shows few type I errors, but it has less power than the other methods tested.