Reviewerly/exHarmony huggingface dataset:
VideoABC: A Real-World Video Dataset for Abductive Visual Reasoning

In this paper, we investigate the problem of abductive visual reasoning (AVR), which requires vision systems to infer the most plausible explanation for visual observations. Unlike previous work which performs visual reasoning on static images or synthesized scenes, we exploit long-term reasoning from instructional videos that contain a wealth of detailed information about the physical world. We conceptualize two tasks for this emerging and challenging topic. The primary task is AVR, which is based on the initial configuration and desired goal from an instructional video, and the model is expected to figure out what is the most plausible sequence of steps to achieve the goal. In order to avoid trivial solutions based on appearance information rather than reasoning, the second task called AVR++ is constructed, which requires the model to answer why the unselected options are less plausible. We introduce a new dataset called VideoABC, which consists of 46,354 unique steps derived from 11,827 instructional videos, formulated as 13,526 abductive reasoning questions with an average reasoning duration of 51 seconds. Through an adversarial hard hypothesis mining algorithm, non-trivial and high-quality problems are generated efficiently and effectively. To achieve human-level reasoning, we propose a Hierarchical Dual Reasoning Network (HDRNet) to capture the long-term dependencies among steps and observations. We establish a benchmark for abductive visual reasoning, and our method set state-of-the-arts on AVR ( ∼ 74 %) and AVR++ ( ∼ 45 %), and humans can easily achieve over 90% accuracy on these two tasks. The large performance gap reveals the limitation of current video understanding models on temporal reasoning and leaves substantial room for future research on this challenging problem. Our dataset and code are available at https://github.com/wl-zhao/VideoABC.
Parameterized DAWGs: Efficient constructions and bidirectional pattern searches

Two strings $x$ and $y$ over $\Sigma \cup \Pi$ of equal length are said to \emph{parameterized match} (\emph{p-match}) if there is a renaming bijection $f:\Sigma \cup \Pi \rightarrow \Sigma \cup \Pi$ that is identity on $\Sigma$ and transforms $x$ to $y$ (or vice versa). The \emph{p-matching} problem is to look for substrings in a text that p-match a given pattern. In this paper, we propose \emph{parameterized suffix automata} (\emph{p-suffix automata}) and \emph{parameterized directed acyclic word graphs} (\emph{PDAWGs}) which are the p-matching versions of suffix automata and DAWGs. While suffix automata and DAWGs are equivalent for standard strings, we show that p-suffix automata can have $\Theta(n^2)$ nodes and edges but PDAWGs have only $O(n)$ nodes and edges, where $n$ is the length of an input string. We also give an $O(n |\Pi| \log (|\Pi| + |\Sigma|))$-time $O(n)$-space algorithm that builds the PDAWG in a left-to-right online manner. As a byproduct, it is shown that the \emph{parameterized suffix tree} for the reversed string can also be built in the same time and space, in a right-to-left online manner. This duality also leads us to two further efficient algorithms for p-matching: Given the parameterized suffix tree for the reversal of the input string $T$, one can build the PDAWG of $T$ in $O(n)$ time in an offline manner; One can perform \emph{bidirectional} p-matching in $O(m \log (|\Pi|+|\Sigma|) + \mathit{occ})$ time using $O(n)$ space, where $m$ denotes the pattern length and $\mathit{occ}$ is the number of pattern occurrences in the text $T$.
Classified generalized linear mixed model prediction incorporating pseudo‐prior information

Abstract We develop a method of classified mixed model prediction based on generalized linear mixed models that incorporate pseudo‐prior information to improve prediction accuracy. We establish consistency of the proposed method both in terms of prediction of the true mixed effect of interest and in terms of correctly identifying the potential class corresponding to the new observations if such a class matching one of the training data classes exists. Empirical results, including simulation studies and real‐data validation, fully support the theoretical findings.
Secure Medical Image Transmission Scheme Using Lorenz’s Attractor Applied in Computer Aided Diagnosis for the Detection of Eye Melanoma

Early detection of diseases is vital for patient recovery. This article explains the design and technical matters of a computer-supported diagnostic system for eye melanoma detection implementing a security approach using chaotic-based encryption to guarantee communication security. The system is intended to provide a diagnosis; it can be applied in a cooperative environment for hospitals or telemedicine and can be extended to detect other types of eye diseases. The introduced method has been tested to assess the secret key, sensitivity, histogram, correlation, Number of Pixel Change Rate (NPCR), Unified Averaged Changed Intensity (UACI), and information entropy analysis. The main contribution is to offer a proposal for a diagnostic aid system for uveal melanoma. Considering the average values for 145 processed images, the results show that near-maximum NPCR values of 0.996 are obtained along with near-safe UACI values of 0.296 and high entropy of 7.954 for the ciphered images. The presented design demonstrates an encryption technique based on chaotic attractors for image transfer through the network. In this article, important theoretical considerations for implementing this system are provided, the requirements and architecture of the system are explained, and the stages in which the diagnosis is carries out are described. Finally, the encryption process is explained and the results and conclusions are presented.
Utility–Privacy Trade-Off in Distributed Machine Learning Systems

In distributed machine learning (DML), though clients' data are not directly transmitted to the server for model training, attackers can obtain the sensitive information of clients by analyzing the local gradient parameters uploaded by clients. For this case, we use the differential privacy (DP) mechanism to protect the clients' local parameters. In this paper, from an information-theoretic point of view, we study the utility-privacy trade-off in DML with the help of the DP mechanism. Specifically, three cases including independent clients' local parameters with independent DP noise, dependent clients' local parameters with independent/dependent DP noise are considered. Mutual information and conditional mutual information are used to characterize utility and privacy, respectively. First, we show the relationship between utility and privacy for the three cases. Then, we show the optimal noise variance that achieves the maximal utility under a certain level of privacy. Finally, the results of this paper are further illustrated by numerical results.
AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Shadow is one of the fundamental indicators of remote sensing image which could cause loss or interference of the target data. As a result, the detection and removal of shadow has already been the hotspot of current study because of the complicated background information. In the following passage, a model combining the Atmospheric Transport Model (hereinafter abbreviated as ATM) with the Poisson Equation, AP ShadowNet, is proposed for the shadow detection and removal of remote sensing images by unsupervised learning. This network based on a preprocessing network based on ATM, A Net, and a network based on the Poisson Equation, P Net. Firstly, corresponding mapping between shadow and unshaded area is generated by the ATM. The brightened image will then enter the Confrontation identification in the P Net. Lastly, the reconstructed image is optimized on color consistency and edge transition by Poisson Equation. At present, most shadow removal models based on neural networks are significantly data-driven. Fortunately, by the model in this passage, the unsupervised shadow detection and removal could be released from the data source restrictions from the remote sensing images themselves. By verifying the shadow removal on our model, the result shows a satisfying effect from a both qualitative and quantitative angle. From a qualitative point of view, our results have a prominent effect on tone consistency and removal of detailed shadows. From the quantitative point of view, we adopt the non-reference evaluation indicators: gradient structure similarity (NRSS) and Natural Image Quality Evaluator (NIQE). Combining various evaluation factors such as reasoning speed and memory occupation, it shows that it is outstanding among other current algorithms.
Enhancing the Privacy of Network Services through Trusted Computing

The addressing and discovering service is a vital infrastructure of the Internet. New applications and scenarios in next-generation networks rely on the secure and stable operation of domain name services, which puts forward new security challenges for the original domain name mechanism. While previous security enhancements of network services struggled to strike a balance between security, performance, and compatibility, hindering further use of core network services, the TEE (Trusted Computing Environment) technology can provide trusted and confidential services in untrusted network environments by verifiable hardware signatures. In this paper, we present a novel trustworthy service architecture with the preservation of security and privacy for addressing messages. The scheme provides a secure enclave to generate authenticatable responses between clients and targets, thus ensuring the privacy of services. We further build a new TEE compilation model to ensure that the built resolver application can provide trusted and secure services within TEE while keeping the availability without the TEE hardware. Experimental results show that our approach can enhance the privacy and security of addressing services such as DNS (Domain Name System) without sacrificing the quality of service and breaking the infrastructures of existing services.
Application of the 2D Local Entropy Information in Sparse TFD Reconstruction

This paper investigates a method for information extraction from the time-frequency distributions (TFDs) based on the local Rényi entropy (LRE) calculated in 2-dimensional (2D) time-frequency (TF) regions. The obtained entropy map information has been projected on the time and frequency axes, estimating the local number of components. The local number of components obtained in this way has been compared to the existing 1D estimation method and applied in the shrinkage operator of a sparse TFD reconstruction algorithm. The obtained results confirm that the estimation based on the 2D entropy map achieves higher accuracy on the considered synthetic and real-life signals corrupted by noise when compared to the accuracy of the 1D method, improving both the shrinking operator classification and the TFD reconstruction performance.
Visible Light Sensing for Recognising Human Postural Transitions

Human activity recognition and monitoring classify signals that are generated from various sensors based on the physical activities a person is performing during his/her daily life. This is useful if a human performs some postural transition activities such as sit-to-stand and stand-to-sit, which are hardly detected accurately by a single sensor. The aim of this study is to explore the possibilities of detecting daily postural transition activities through a novel wearable approach comprising of inertial measurement sensors (IMU) and visible light sensing (VLS) utilizing a single RGB photodiode in an unmodified lighting infrastructure. By employing a low-complex decision tree algorithm, the activity recognition can be achieved in a resourceful way. For enabling our approach to work precisely in changing environments, a K-means clustering algorithm is employed to adapt the parameters of both sit-to-stand and stand-to-sit transition detection. Our approach is validated with different scenarios; representing basic and daily life postural transition activities. The results showed that the approach was able to achieve the tasks accurately, which could not be the case if either IMU sensors or VLS is used alone.
Building Interpretable and Parsimonious Fuzzy Models using a Multi-Objective Approach

Nowadays, the growing amounts of collected data enable the training of machine learning models that can be used to extract insights from the data and make better-informed decisions. Among the possible models that can be learned from data are fuzzy rule-based models, which are transparent and enable – when properly designed – interpretable artificial intelligence. One of the requirements of interpretability is a simple model structure, which can be achieved by performing feature selection and by limiting the number of rules in the model. However, the chosen feature set and the number of rules may interact and strongly affect the model’s accuracy. In this study, we employ techniques from the field of evolutionary computation to perform feature and rule number selection simultaneously. To ensure the developed models do not only perform well but are also interpretable and have good generalization capabilities, we adopt a multi-objective approach in which we train the models focusing on three objectives: performance, complexity, and model stability. In this way, we strive to develop simple, well-performing parsimonious fuzzy models. We show the effectiveness of our approach on three benchmark data sets.
Data Processing under Fuzzy Uncertainty: Towards More Efficient Algorithms

In many practical situations, we need to process data under fuzzy uncertainty: we have fuzzy information about the algorithm’s input, and we want to find the resulting information about the algorithm’s output. It is known that this problem can be reduced to computing the range of the algorithm over several (A) alpha-cuts of the input. However, a straightforward application of this idea requires A times longer computation time than each range estimation – and for complex data processing algorithms, each range computation is already time-consuming. In this paper, we show how to compute all the desired ranges much faster.
Uniform Convergence of Probability Mixtures that Represent Combined Fuzzy Systems

We can combine expert knowledge by combining the probability mixtures that represent the if-then rules of the experts. Fuzzy rules define a generalized probability mixture whose moments describe a fuzzy system and its uncertainty. The mixture’s Bayesian structure gives a complete posterior probability description of the if-then fuzzy-set rules as they fire. A new theorem extends the uniform convergence of a fuzzy system’s mixture to the uniform convergence of the sequence of expert mixtures that represent any number of combined fuzzy systems as they each converge to a target function. A mixture of just two normal bell curves exactly represents the target function in the scalar case and serves as the probabilistic target of the converging mixture sequence. A sampled deep neural network can serve as the target function. Then the mixture defines a proxy system that gives a probabilistic form of explainable AI. The uniform convergence result extends to any continuous transformation of the converging fuzzy systems and further extends to the uniform mixture convergence of any continuous function of the combined systems and their continuous transformations.
Counterfactual rule generation for fuzzy rule-based classification systems

EXplainable Artificial Intelligence (XAI) is of in-creasing importance as researchers and practitioners seek better transparency and verifiability of AI systems. Mamdani fuzzy systems can provide explanations based on their linguistic rules, and thus a potential pathway to XAI. A factual rule based explanation generally refers to the given set of rules executed, or fired, for a given input. However, research has shown that human explanations are often counterfactual (CF), i.e. rather than explaining why a given output was reached, they show why other potential outputs were not. Although several machine learning-based CF explanation generation methods have been proposed in recent years, quasi none of them focus on fuzzy systems. Also, where they do, they focus on correlation, which limits the interpretive value of any CF explanations obtained as humans expect a causal relationship in rules, i.e. we are cause-effect thinkers. In this paper, we propose a new rule generation framework for Mamdani fuzzy classification systems, which we refer to as CF-MABLAR, building on the MARkov BLAnket Rules (MABLAR) framework. CF-MABLAR approximates the causal links between inputs and output(s) of fuzzy systems and generates CF rules by leveraging them. Uniquely, the CF rules obtained not only provide a basic CF explanation, but can also articulate how the given inputs would need to be changed to generate a different output, crucial for lay-user insight, verification and sensitivity-evaluation of XAI systems, for example in decision support around credit risk, cyber security and medical assistance.
A Web Application for Running Quantum-enhanced Support Vector Machine

The Support Vector Machine (SVM) is a well-known supervised machine learning approach aimed at facing classification problems. Thanks to the exploitation of kernel functions, SVM succeeds to address also complex classification tasks involving non-linearly separable data. However, there are limitations to its success when the feature space becomes large, and the kernel functions become computationally expensive to estimate. Quantum-enhanced Support Vector Machine (QSVM) utilizes the properties of quantum computers to obtain an exponential speed up with respect to the conventional SVM by using the quantum state space as the feature space. Unfortunately, to benefit from these advantages, currently, it is necessary to have a background knowledge about quantum computing concepts and specific language skills. The goal of this paper is to introduce a web-based tool to make simple to all researchers, coming from several and heterogeneous scientific backgrounds, the application of QSVM to different real-world classification problems. To achieve this goal, the presented web tool involves data preprocessing techniques and a user-friendly interface. The suitability of the presented web tool is shown in an experimental session where QSVM is applied to solve well-known classification tasks.
A New Deep Complex-Valued Single-Iteration Fuzzy System for Predictive Modelling

Numerical prediction is an important application field of machine learning. However, the current mainstream relating to deep network solutions can be computationally taxing, whereas fuzzy systems may also prove to be inefficient for high dimensional systems. Combining the notion of complex-valued neural network and the Wang-Mendel (WM) fuzzy algorithm, we propose a new complex-valued Wang-Mendel (CVWM) method, which reduces the rule-base of fuzzy systems to the scale of its square root. Further, by introducing the concept of a hierarchical fuzzy system, a deep complex-valued single-iteration fuzzy system (DCVSF) that can be trained with only one iteration and can effectively process high-dimensional data is also elicited. In addition, for sparse data, t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction is introduced to increase data density. The experimental results show that both CVWM and DCVSF exhibit competitive nonlinear modeling capabilities.
An Open-Source Software Library for Explainable Support Vector Machine Classification

Since computer predictions can be used for supporting high-impact decision-making processes, finding mechanisms that favor the interpretability of such predictions has grown into a challenging research topic. One of those mechanisms proposes the contextualization of support vector machine (SVM) predictions within a classification process by means of the most influential support vectors (MISVs). An open-source software library by which a researcher or practitioner can obtain such contextualized SVM predictions is proposed and presented in this paper. The proposed library enables the creation of coherent explanations for SVM predictions about the class(es) of an object to be built from contextualized evaluations of the membership (and nonmembership) of that object in different classes. Examples illustrating how to use the library for designing and building explanation interfaces are presented.
An improvement of collaborative fuzzy clustering based on active semi-supervised learning

Semi-supervised clustering is a hybrid method of supervised and unsupervised clustering that has the advantages of both. The paper presents an improvement of the collaborative fuzzy clustering algorithm by using active semi-supervised learning. This method is implemented if there is very little labeled data and not enough to apply supervised learning algorithms. After each iteration, active semi-supervised learning is accomplished by adding samples to the labeled dataset. The initial data set has very few labeled samples. The proposed algorithm selects and adds more high-confidence data samples to the labeled data set. After each iteration, the labeled dataset is added with new data samples, which helps the clustering process be stable and achieve higher accuracy. Experiments on standard data sets from UCI Machine Learning Repository (UCI) and satellite image data show that the proposed method gives clustering results with high accuracy and stability compared to the pre-improvement algorithm.
Fuzzy Rule-based Outlier Detector

The problem of detecting outliers in data is a widely discussed issue. The sources of outliers vary and can come from system errors, or human mistakes. Due to the constantly increasing number of data for analysis, an effective tool for detecting outliers should be proposed. Therefore, in this study we present a method based on the use of the fuzzy three-sigma rule to detect outliers. The novelty of the described method is the use of the properties of fuzzy sets to replace the properties of the analyzed data with common statistical semantics. Due to the untypical approach consisting in an independent analysis of each dimension of the data set, a universal method was obtained, which operates regardless of the specificity of the analyzed data. Moreover, the appropriate aggregation of the membership degrees to the descriptors describing the type and strength of deviation from the norm makes it possible to look at the analyzed data from various angles. The high performance of the proposed novel approach was confirmed in numerical experiments.