Reviewerly/exHarmony huggingface dataset:
White-box Induction From SVM Models: Explainable AI with Logic Programming

Abstract We focus on the problem of inducing logic programs that explain models learned by the support vector machine (SVM) algorithm. The top-down sequential covering inductive logic programming (ILP) algorithms (e.g., FOIL) apply hill-climbing search using heuristics from information theory. A major issue with this class of algorithms is getting stuck in local optima. In our new approach, however, the data-dependent hill-climbing search is replaced with a model-dependent search where a globally optimal SVM model is trained first, then the algorithm looks into support vectors as the most influential data points in the model, and induces a clause that would cover the support vector and points that are most similar to that support vector. Instead of defining a fixed hypothesis search space, our algorithm makes use of SHAP, an example-specific interpreter in explainable AI, to determine a relevant set of features. This approach yields an algorithm that captures the SVM model’s underlying logic and outperforms other ILP algorithms in terms of the number of induced clauses and classification evaluation metrics.
Data Mining in Cloud Computing: Survey

Data and information or knowledge have been playing an essential role to understand the human day to day activities. Data mining is the process of discovering knowledge in data from various contexts and summarising useful patterns. Because of certain limitations with traditional ICT infrastructure, businesses are finding cloud computing is the powerful and cost-effective solution. Cloud computing allows the integration of data mining techniques and methods, which deals with the massive amounts of data. Cloud data mining extracts “structured” information from different Web-based data sources. Web data may be semi-structured data or unstructured data. Data mining applications in cloud computing such as classification techniques, clustering techniques, and association rule mining techniques discussed in this work. Security and privacy are one of the major challenging issues in cloud computing. Some data mining techniques such as classification algorithms discussed to classify the DoSs in cloud computing environment.
World competitive contest-based artificial neural network: A new class-specific method for classification of clinical and biological datasets

Many data mining methods have been proposed to generate computer-aided diagnostic systems, which may determine diseases in their early stages by categorizing the data into some proper classes. Considering the importance of the existence of a suitable classifier, the present study aims to introduce an efficient approach based on the World Competitive Contests (WCC) algorithm as well as a multi-layer perceptron artificial neural network (ANN). Unlike the previously introduced methods, which each has developed a universal model for all different kinds of data classes, our proposed approach generates a single specific model for each individual class of data. The experimental results show that the proposed method (ANNWCC), which can be applied to both the balanced and unbalanced datasets, yields more than 76% (without applying feature selection methods) and 90% (with applying feature selection methods) of the average five-fold cross-validation accuracy on the 13 clinical and biological datasets. The findings also indicate that under different conditions, our proposed method can produce better results in comparison to some state-of-art meta-heuristic algorithms and methods in terms of various statistical and classification measurements. To classify the clinical and biological data, a multi-layer ANN and the WCC algorithm were combined. It was shown that developing a specific model for each individual class of data may yield better results compared with creating a universal model for all of the existing data classes. Besides, some efficient algorithms proved to be essential to generate acceptable biological results, and the methods' performance was found to be enhanced by fuzzifying or normalizing the biological data.
Gaussian versus Non-Gaussian Filtering of Phase-Insensitive Nonclassicality

Measures of quantum properties are essential to understanding the fundamental differences between quantum and classical systems as well as quantifying resources for quantum technologies. Here two broad classes of bosonic phase-space functions, which are filtered versions of the Glauber-Sudarshan $P$ function, are compared with regard to their ability to uncover nonclassical effects of light through their negativities. Gaussian filtering of the $P$ function yields the family of $s$-parametrized quasiprobabilities, while more powerful regularized nonclassicality quasiprobabilities are obtained by non-Gaussian filtering. A method is proposed to directly sample such phase-space functions for the restricted case of phase-independent quantum states from balanced homodyne measurements. This overcomes difficulties of previous approaches that manually append uniformly distributed optical phases to the measured quadrature data. We experimentally demonstrate this technique for heralded single- and two-photon states using balanced homodyne detection with varying efficiency. The $s$-parametrized quasiprobabilities, which can be directly sampled, are non-negative for detection efficiencies below 0.5. By contrast, we show that significant negativities of non-Gaussian filtered quasiprobabilities uncover nonclassical effects even for low efficiencies.
Heterogeneous Univariate Outlier Ensembles in Multidimensional Data

In outlier detection, recent major research has shifted from developing univariate methods to multivariate methods due to the rapid growth of multidimensional data. However, one typical issue of this paradigm shift is that many multidimensional data often mainly contains univariate outliers , in which many features are actually irrelevant. In such cases, multivariate methods are ineffective in identifying such outliers due to the potential biases and the curse of dimensionality brought by irrelevant features. Those univariate outliers might be well detected by applying univariate outlier detectors in individually relevant features. However, it is very challenging to choose a right univariate detector for each individual feature since different features may take very different probability distributions. To address this challenge, we introduce a novel Heterogeneous Univariate Outlier Ensembles (HUOE) framework and its instance ZDD to synthesize a set of heterogeneous univariate outlier detectors as base learners to build heterogeneous ensembles that are optimized for each individual feature. Extensive results on 19 real-world datasets and a collection of synthetic datasets show that ZDD obtains 5%–14% average AUC improvement over four state-of-the-art multivariate ensembles and performs substantially more robustly w.r.t. irrelevant features.
Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence

Many attempts are made to resolve the clustering problem over categorical dataset through the process of clustering ensemble. However, the conventional techniques failed to generate proper data portioning to cluster the data due to the presence of incomplete information. Hence, this paper uses a modified cluster ensemble similarity framework to cluster the relevant categorical datasets, and a weight-based similarity approach has been proposed to work with type II full-space and subspace ensembles that involve random-k and fixed-k techniques. The centroid selection is initiated using swarm-based bio-inspired optimization method or swarm-based similarity ensemble clustering (WSE). Finally, Division Reminder Hashing (DRH) technique is used to determine the weights of the cluster using similarity measurement. This technique helped in eliminating the null and duplicate datasets. The measurement for testing includes Adjusted Rand (AR), Normalized Mutual Information (NMI), and Classification Accuracy (CA). The performance of the proposed technique is tested over University of California Irvine (UCI) dataset, and the results proved that the proposed technique provides better clustering using Type-2 and Type-3 ensemble.
Natural language goal understanding for smart home environments

One of the main challenges of the Internet of Things (IoT) is to enable end-users without technical experience to use, control or monitor smart devices. However, enabling end-users to interact with these smart devices in an intuitive and natural way becomes increasingly important as they become more pervasive in our homes, workplaces and public environments. Voice-based interfaces are the emerging trend to provide a more natural human-device interaction in smart environments. Such interfaces require Natural Language Understanding (NLU) approaches to identify the meaning of end-users' voice inputs. Designing voice interfaces that are not limited to a small, fixed set of pre-defined commands is far from trivial. Existing voice-based solutions in the smart home domain either restrict the end-users to follow a strict language pattern, do not support indirect goals, require a large training dataset, or need a voice assistant located in the cloud. In this paper, we propose an approach for understanding end-users goals from voice inputs in smart homes. Our approach alleviates the need for end-users to learn or remember concrete operations of the devices and specific words/pattern structures rather it enables them to control their smart homes based on the desired goals (effects). We evaluate the approach through application to a collection of 253 goals from real end-users and report on quality metrics. The results demonstrate that our solution provides a good accuracy, high precision and acceptable recall for understanding end-users goals in the smart home domain.
Automatic Modulation Classification Using Multi-Scale Convolutional Neural Network

In this paper, a multi-scale convolutional neural network-based (MSN) method is proposed for robust automatic modulation classification (AMC). The classifier directly utilizes in-phase and quadrature (I/Q) samples to identify the modulation type of received signal without any data preprocessing, thereby reducing the computational complexity. Further, the network architecture employs one-dimensional convolution (Conv1D) to extract multi-scale feature maps due to its merits of low computational complexity. Then these multi-scale feature maps are merged together by repeated multi-scale fusions, in order to improve the classification accuracy performance and the robustness to varying SNR environment. Repeated multi-scale fusions can make better use of amplitude-phase information because it can learn the local changes brought by modulation as well as the timing characteristics of the samples. Simulation results show that proposed MSN achieves classification rate of 97.38% classification accuracy at high SNR regimes for 24 different modulation types on the public well-known over-the-air (OTA) dataset. Moreover, MSN still can recognize the modulation types of received signals with the accuracy rates of about 95% under varying SNR scenarios. Compared to the methods proposed in other papers, our classifier not only shows a better performance in terms of classification accuracy, but also is the most robust in varying SNR environment.
Remote-controlled quantum computing by quantum entanglement

Quantum entanglement enables measurement on one party to affect the other’s state. Based on this peculiar feature, we propose a model of remote-controlled quantum computing and design an optical scheme to realize this model for a single qubit. As an experimental demonstration of this scheme, we further implement three Pauli operators, Hardmard gate, phase gate, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>π<!-- π --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>8</mml:mn> </mml:math> gate. The minimal fidelity obtained by quantum process tomography reaches 82%. Besides, as a potential application, our model contributes to secure remote quantum information processing.
Gun Detection with Model and Type Recognition using Haar Cascade Classifier

The crime percentages brought about by weapons are very concerning in numerous places on the planet, particularly in nations where the ownership of weapons is legitimate or was lawful for a while. In this advanced time of observation and security the quantity of Closed-Circuit Television (CCTV) conveyed out in the open and private places, for example, Cinemas, Malls has expanded exponentially. As of now, there are a huge number of CCTV cameras in operation. In this way, the expanding density of investigation camera recording makes it a challenge for a human administrator to inspect, analyze whether a conceivably risky circumstance is going to occur. Object detection dependent on digital image processing on weapons is very important for establishing security systems. This paper presents a real-time framework for gun detection with model and type recognition. The method uses real-time video as an input and the method used is Haar Cascade Classifier, Open-CV library. This method is mainly designed for security and safety management purposes. This method was built to identify and classify guns in a video and a high accuracy rate is obtained which reaches 95% in Submachine Guns. Software required for the framework is effectively accessible because of which it turns out to be a more affordable framework.
iWink: Exploring Eyelid Gestures on Mobile Devices

Although gaze has been widely studied for mobile interactions, eyelid-based gestures are relatively understudied and limited to few basic gestures (e.g., blink). In this work, we propose a gesture grammar to construct both basic and compound eyelid gestures. We present an algorithm to detect nine eyelid gestures in real-time on mobile devices and evaluate its performance with 12 participants. Results show that our algorithm is able to recognize nine eyelid gestures with 83% and 78% average accuracy using user-dependent and user-independent models respectively. Further, we design a gesture mapping scheme to allow for navigating between and within mobile apps only using eyelid gestures. Moreover, we show how eyelid gestures can be used to enable cross-application and sensitive interactions. Finally, we highlight future research directions.
Deep Recurrent Architecture based Scene Description Generator for Visually Impaired

Vision is the most essential sense for human beings. But today, more than 2.2 billion people worldwide suffer from some form of vision impairment. This paper presents an end-to-end human-centric model for aiding the visually impaired by employing the deep recurrent architecture of the start-of-the-art image captioning models. A VGG-16 net convolutional neural network (CNN) is used to extract feature vectors from real-time video (image frames) and an long short-term memory (LSTM) network is employed to generate captions from these feature vectors. The model is tested on the Flickr 8K Dataset, one of the most popularly used image captioning dataset which contains over 8000 images. On real-time videos, the model generates rich descriptive captions which are converted to audio for a visually impaired person to listen. Comprehensively the model generates promising results which has great potential to enhance the lives of the visually impaired people by assisting them to get a better understanding of their surroundings.
Exploring the Limits of Combined Image/'omics Analysis for Non-cancer Histological Phenotypes

The last several years have witnessed an explosion of methods and applications for combining image data with 'omics data, and for prediction of clinical phenotypes. Much of this research has focused on cancer histology, for which genetic perturbations are large, and the signal to noise ratio is high. Related research on chronic, complex diseases is limited by tissue sample availability, lower genomic signal strength, and the less extreme and tissue-specific nature of intermediate histological phenotypes. Data from the GTEx Consortium provides a unique opportunity to investigate the connection among phenotypic histological variation, imaging data, and 'omics profiling, from multiple tissue-specific phenotypes at the sub-clinical level. Investigating histological designations in multiple tissues, we survey the evidence for genomic association and prediction of histology, and use the results to test the limits of prediction accuracy using machine learning methods applied to the imaging data, genomics data, and their combination. We find that expression data has similar or superior accuracy for pathology prediction as our use of imaging data. A variety of machine learning methods have similar performance, while network embedding methods offer at best limited improvements. These observations hold across a range of tissues and predictor types. The results are supportive of the use of genomic measurements in the same target tissue in which pathological phenotyping has been performed, which to our knowledge is a novel finding. Even while prediction accuracy remains a challenge, the results show clear evidence of pathway and tissue-specific biology.
Computer Cursor Control Using Eye and Face Gestures

In this paper, a computer application is developed using Python and OpenCV. This application will make it possible for the user to move the cursor point on the screen of the computer and also additional functionalities like clicking on the computer screen. The proposed system is a virtual interactive module for the users and can be used as an alternative module of touch screen technology. In this system, after the input is taken from the user, the real-time video input is processed and face recognition is performed. A point on the user's face would control the cursor on the screen and the right and left wink would implement the right and left click respectively. Squeezed eyes would facilitate the enabling of the scroll function in case of reading PDFs and other documents. It would thus facilitate the use and movement of the cursor through the face gestures. The application can be implemented on desktops and laptops. With the help of this application, the user can perform actions like move the cursor in all directions, scroll, drag and click. Thus the system would be extremely helpful for physically challenged users.
The Hermitian Dual Containing Non-Primitive BCH Codes

Let q be a prime power and m ≥ 4 an even integer. Suppose that n = q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2m-1</sup> /a such that m is the multiplicative order of q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> modulo n, where a ≥ 2 is a positive integer. This letter mainly determines the maximum designed distance of Hermitian dual-containing Bose-Chaudhuri-Hocquenghem (BCH) codes of length n over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q2</sub> . Our results show that the designed distances of non-primitive BCH codes in this letter are larger. Moreover, we obtain the dimensions of some non-primitive BCH codes.
EnDeA: Ensemble based Decoupled Adversarial Learning for Identifying Infrastructure Damage during Disasters

Identifying tweets related to infrastructure damage during a crisis event is an important problem. However, the unavailability of labeled data during the early stages of a crisis event poses major challenge in training suitable models. Several domain adaptation strategies have been proposed for text classification that can be used to train models using available source data of previous crisis events and apply on a target data related to a current event. However, these approaches are insufficient to handle the distribution drift in the source and target data along with the class imbalance in the target data. In this paper we introduce an Ensemble learning approach with a Decoupled Adversarial (EnDeA) model to classify infrastructure damage tweets in a target tweet dataset. EnDeA is an ensemble of three different models two of which separately learn the event invariant and specific features of a target data from a set of source and target data. The third model which is an adversarial model helps to improve the prediction accuracy of both models. Unlike the existing approaches that also identify the domain invariant and specific properties of target data for sentiment classification, our method works for short texts and can better handle the distribution drift and class imbalance problem. We rigorously investigate the performance of the proposed approach using multiple public datasets and compare it with several state-of-the-art baselines. We discover that EnDeA outperforms these baselines with around 20% improvement in the 1 scores.
Data Driven and Psycholinguistics Motivated Approaches to Hate Speech Detection

Computational models of hate speech detection and related tasks (e.g., detecting misogyny, racism, xenophobia, homophobia etc.) have emerged as major Natural Language Processing (NLP) research topics in recent years. In the present work, we investigate a range of alternative implementations of three of these tasks - namely, hate speech, aggressive behaviour and target group recognition- by presenting a number of experiments involving different learning methods, including regularised logistic regression, convolutional neural networks (CNN) and deep bidirectional transformers (BERT), and using word embeddings, word n-grams, character n-grams and psycholinguistics-motivated (LIWC) features a like. Results suggest that a purely data-driven BERT model, and to some extent also a hybrid psycholinguisticly informed CNN model, generally outperform the alternatives under consideration for all tasks in both English and Spanish languages.
PBCS: Efficient Exploration and Exploitation Using a Synergy Between Reinforcement Learning and Motion Planning

The exploration-exploitation trade-off is at the heart of reinforcement learning (RL). However, most continuous control benchmarks used in recent RL research only require local exploration. This led to the development of algorithms that have basic exploration capabilities, and behave poorly in benchmarks that require more versatile exploration. For instance, as demonstrated in our empirical study, state-of-the-art RL algorithms such as DDPG and TD3 are unable to steer a point mass in even small 2D mazes. In this paper, we propose a new algorithm called “Plan, Backplay, Chain Skills” (PBCS) that combines motion planning and reinforcement learning to solve hard exploration environments. In a first phase, a motion planning algorithm is used to find a single good trajectory, then an RL algorithm is trained using a curriculum derived from the trajectory, by combining a variant of the Backplay algorithm and skill chaining. We show that this method outperforms state-of-the-art RL algorithms in 2D maze environments of various sizes, and is able to improve on the trajectory obtained by the motion planning phase.