Reviewerly/exHarmony huggingface dataset:
#PrettyFlyForAWiFi

Drones are becoming increasingly popular for hobbyists and recreational use. But with this surge in popularity comes increased risk to privacy as the technology makes it easy to spy on people in otherwise-private environments, such as an individual’s home. An attacker can fly a drone over fences and walls to observe the inside of a house, without having physical access. Existing drone detection systems require specialist hardware and expensive deployment efforts, making them inaccessible to the general public. In this work, we present a drone detection system that requires minimal prior configuration and uses inexpensive commercial off-the-shelf hardware to detect drones that are carrying out privacy invasion attacks. We use a model of the attack structure to derive statistical metrics for movement and proximity that are then applied to received communications between a drone and its controller. We test our system in real-world experiments with two popular consumer drone models mounting privacy invasion attacks using a range of flight patterns. We are able both to detect the presence of a drone and to identify which phase of the privacy attack was in progress while being resistant to false positives from other mobile transmitters. For line-of-sight approaches using our kurtosis-based method, we are able to detect all drones at a distance of 6 m, with the majority of approaches detected at 25 m or farther from the target window without suffering false positives for stationary or mobile non-drone transmitters.
Graph-Based Deep Generative Modelling for Document Layout Generation

One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
IMM Robust Cardinality Balance Multi‐Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement

This paper presents a novel Interacting multi-model (IMM) Robust Cardinality balance multi-target multi-Bernoulli (R-CBMeMBer) filter to solve the maneuvering target tracking problem in the case of interval measurement, unknown detection probability and unknown clutter density. In essence, IMM R-CBMeMBer filter is an extended application of R-CBMeMBer filter. In the IMM R-CBMeMBer filter, the target state is first extended to distinguish clutter from the real target. The detection probability and model probability of the target can be adaptively updated. Then, generalized likelihood function and IMM algorithm are introduced to interactively predict and update the state of the target in the IMM R-CBMeMBer filtering process. In addition, a particle application of the IMM R-CBMeMBer filter is given, and a numerical experiment is designed under nonlinear conditions. Meanwhile, Doppler information of the target is employed to estimate the velocity of each maneuvering target. Numerical experiments also verify that the IMM R-CBMeMBer filter can effectively estimate the target position, target velocity, target detection probability and clutter number in the condition of unknown detection probability, unknown clutter rate and interval measurement.
Coordinate-Ordering-Free Upper Bounds for Linear Insertion-Deletion Codes

In this paper we prove several coordinate-ordering-free upper bounds on the insdel distances of linear codes. Our bounds are stronger than some previous known bounds. We apply these upper bounds to AGFC codes from some cyclic codes and one algebraic-geometric code with any rearrangement of coordinate positions. A strong upper bound on the insdel distances of Reed-Muller codes with the special coordinate ordering is also given.
Vampire with a Brain Is a Good ITP Hammer

Vampire has been for a long time the strongest first-order automatic theorem prover, widely used for hammer-style proof automation in ITPs such as Mizar, Isabelle, HOL, and Coq. In this work, we considerably improve the performance of Vampire in hammering over the full Mizar library by enhancing its saturation procedure with efficient neural guidance. In particular, we employ a recently proposed recursive neural network classifying the generated clauses based only on their derivation history. Compared to previous neural methods based on considering the logical content of the clauses, our architecture makes evaluating a single clause much less time consuming. The resulting system shows good learning capability and improves on the state-of-the-art performance on the Mizar library, while proving many theorems that the related ENIGMA system could not prove in a similar hammering evaluation.
A Personalized Recommender System

Due to social media, e-commerce, and the broader digitization of businesses, a data surge has occurred during the previous decade. The information is used to make informed decisions, forecast market trends, and identify patterns in consumer preferences. Following the widespread adoption of internet services, recommendation systems have become commonplace. The idea is to use filtering algorithms to recommend products to users who might be interested in them. Users are given recommendations for a media item such as movies by discovering user profiles of people who share similar interests. The preferences of users are first determined by allowing them to rate movies of their choosing. After some time, the recommender system will be able to better understand the user and recommend films that are more likely to get higher ratings. It also considers the impact of personal and situational factors on the user experience. In comparison to previous models, the experimental findings on the TMDB dataset provide a dependable model that is precise and generates more customized movie recommendations.
Hybrid Active Contour Mammographic Mass Segmentation and Classification

This research implements a novel segmentation of mammographic mass.Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selectionbased active contour techniques.In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter.To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts, specifically by taking the difference in their mean values.Accordingly, the difference in the mean value of the third method is 1.0853, which indicates the closeness of the segmentation.Moreover, the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system, classification is done and results in a sensitivity of 94.73%, accuracy of 93.93%, and Mathew's correlation coefficient (MCC) of 0.876.
QROSS: QUBO Relaxation Parameter optimisation via Learning Solver Surrogates

A popular method for solving a constrained combinatorial optimisation problem is to first convert it into a quadratic unconstrained binary optimisation (QUBO) problem, and solve it using a standard QUBO solver. However, this relaxation introduces hyper-parameters that balance the objective and penalty terms for the constraints, and their chosen values significantly impact performance. Hence, hyper-parameters tuning is important. Existing generic hyper-parameter tuning methods require multiple expensive calls to a QUBO solver, making them inefficient for performance critical applications when repeated solutions of similar combinatorial optimisation problems are required. In this paper, we propose the QROSS method, in which we build surrogate models of QUBO solvers via learning from solver data on a collection of instances of a problem. In this way, we are able capture the common structure of the instances and their interactions with the solver, and produce promising hyper-parameters with fewer calls to the QUBO solver. We take the Traveling Salesman Problem (TSP) as a case study, where we demonstrate QROSS finds better solutions with fewer calls to QUBO solver, compared with conventional hyper-parameter tuning techniques. Moreover, with simple adaptation methods, QROSS is shown to generalise well to out-of-distribution datasets and different types of QUBO solvers.
Implementation of Machine Learning in Lung Cancer Prediction and Prognosis: A Review

The machine learning is essentially the computer assessment inevitably developed over practice which is a part within the sphere of artificial intelligence. Machine learning algorithms are based upon models that are data driven which is the training set provided to the algorithm resulting in making prediction. Statistical, probabilistic knowledge is used to site the patterns from past examples which contain data sets which are huge, defeating, or hard. This skill is explicitly acceptable for medical applications especially for cancer detection and diagnosis as they rely on complex measurements of proteomics and genomics. The application of machine learning in cancer prediction is around 20 years now. The accuracy of the algorithms has gradually increased from past till now. The intention of this paper is to evaluate various machine learning algorithms particularly for the lung cancer detection to look for a void in the future improvement of lung cancer detection. Each technique is analyzed, and the overall disadvantages are pointed out. Various types of machine learning algorithms like Naive Bayes, support vector machine (SVM), logistic regression, and artificial neural network (ANN) have been applied in the healthcare sector for analysis and prognosis of lung cancer.
Small angle reconstruction of plasma luminosity for spherical tokamak

Публикация посвящена применению методов вычислительной геометрии, интервального анализа и линейного программирования к задачам физики управляемого термоядерного синтеза. Рассмотрены геометрические аспекты проблемы, получены проекции светимостей различных объемов сферического токамака на плоскость матричного детектора, изучены изображения предполагаемых макроскопических структур и микроскопических включений. Для набора модельных распределений светимости объема токамака поставлена задача восстановления сигнала. Решение получено с использованием задач линейного программирования. The problems of reconstruction of plasma luminosity are important for physics and technology of power plants-tokamaks. The Globus-M research tokamak obtained a large amount of data using a matrix detector in pinhole camera geometry. From the mathematical point of view, finding the luminosity for different regions of the plasma volume according to the matrix detector is an inverse problem related to the field of integral geometry. An essential feature of the particular task is the use of a single fixed camera with a small viewing angle. In this regard, application of methods of harmonic analysis of data is not enough. The paper investigates the geometric aspects of the problem. In the general view, a threedimensional object is projected onto a two-dimensional plane through a diaphragm. Under the assumption of azimuthal symmetry, there is a central projection of the luminosity of the body of rotation onto a flat matrix detector. The initial information for the calculation is the plasma boundary obtained from magnetic sensors. There is no reliable information about the internal structure of the plasma, so its division into regions of the equal luminosity is not unambiguous. The paper presents an algorithm for finding the projections of the luminosity of plasma volumes on the plane of the matrix detector. A set of model direct problems for the construction of algorithms for their recognition according to the detector data was investigated. Images of supposed macroscopic structures and microscopic inclusions were obtained. The methodological basis of the work is the use of interval analysis methods for solving geometric and algebraic problems. This approach allows obtaining qualitative and quantitative results that takes into account the uncertainty of the input data with the minimum amount of computational costs. Algebraic solvability is investigated in the interval formulation using response functionality. Solutions for a set of test problems are obtained, which demonstrate the availability of successful reconstruction for real data. An important result of the study is an information about the presence of uncertainties in geometric data and related calculations by obtaining results about the luminosity of the plasma by solving linear programming problems.
Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

Purpose Smooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal. Design/methodology/approach Therefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot. Findings During experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables. Originality/value After literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.
Privacy-Preserving Collaborative Filtering Using Fully Homomorphic Encryption

Protecting the privacy of users is one of the most important issues in recommender systems, where new items, e.g., books, movies, and friends in online social networking service/sites, are recommended to target users. To identify recommended items, encryption-based privacy-preserving collaborative filtering is widely used to generate recommendations. However, existing solutions are either slow or not scalable. To tackle this issue, in this paper, we first propose a privacy-preserving user-based CF protocol using the BGV fully homomorphic encryption scheme, which is named BGV-CF. By reducing interactions and the amount of communication traffic among users and recommendation servers, the proposed BGV-CF protocol significantly facilitates the recommendation process. Then, we propose an optimized BGV-CF (OBGV-CF) protocol where some computations are offloaded to users during the recommendation process. The security of the proposed schemes is qualitatively analyzed and quantitative analyses of the computation and communication costs are performed. In addition, provable security analysis using random oracles is provided. The BGV-CF and OBGV-CF protocols are implemented using C++, and testbeds using the MovieLens dataset are conducted. Experimental results demonstrate that the proposed BGV-CF and OBGV-CF successfully achieve their design goals.
HUI-Audio-Corpus-German: A High Quality TTS Dataset

The increasing availability of audio data on the internet leads to a multitude of datasets for development and training of text to speech applications, based on deep neural networks. Highly differing quality of voice, low sampling rates, lack of text normalization and disadvantageous alignment of audio samples to corresponding transcript sentences still limit the performance of deep neural networks trained on this task. Additionally, data resources in languages like German are still very limited. We introduce the “HUI-Audio-Corpus-German”, a large, open-source dataset for TTS engines, created with a processing pipeline, which produces high quality audio to transcription alignments and decreases manual effort needed for creation.
Image-Text Integration Using a Multimodal Fusion Network Module for Movie Genre Classification

Multimodal models have received increasing attention from researchers for using the complementarity of data to obtain a better inference on the dataset. These multimodal models have been applied to several deep learning tasks, such as emotion recognition, video classification and audio-visual speech enhancement. In this paper, we propose a multimodal method that has two branches, one for text classification and another for image classification. In the image classification branch, we use the Class Activation Mapping (CAM) method as an attention module for the identification of relevant regions of the images. To validate our method, we used the MM-IMDB dataset, which consists of 25959 movies with their respective plot outlines, poster and genres. Our results showed that our method averaged 0:6749 in F1-Weight, 0:6734 in F1-Samples, 0:6750 in F1-Micro and 0:6159 in F1-Macro, achieving better results than the state of the art in the F1-Weight and F1-Macro metrics, and being the second best result in the F1-Samples and F1-Micro metrics.
Blockchain-based electric vehicle charging reputation management mechanism

At present, there are still some problems about the credible evaluation and management of electric vehicle charging station. In response to such problems, in order to enable more effective evaluation and management of electric vehicle charging stations, and to enable users to obtain a good user experience, this paper proposes a credibility mechanism for electric vehicle charging stations based on a double-layer blockchain. The current credibility mechanisms are all built on the basis of centralization. Once the central server is compromised, it is extremely prone to irreparable situations such as system paralysis. In order to improve the efficiency of consensus in the existing blockchain, this article proposes a method of combining roulette with a consensus mechanism to prevent monopoly, cheating, and improve system efficiency. Through extensive analysis and experimental results, it is proved that the trusted mechanism can well reflect the quality of the charging station nodes, and the addition of roulette can stimulate the positive behavior of each block node.
Design and analysis of quantum powered support vector machines for malignant breast cancer diagnosis

Abstract The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F 1-score, to exemplify the supremacy of quantum SVM over its conventional variants.
Fault Detection of Hydroelectric Generators by Robust Random Cut Forest with Feature Selection Using Hilbert-Schmidt Independence Criterion

This paper proposes a fault detection method for hydroelectric generators by robust random cut forest (RRCF) with feature selection using Hilbert-Schmidt Independence Criterion (HSIC). It is difficult to obtain fault data because faults unlikely occur in hydroelectric generators. Therefore, only normal data can be utilized for developing a fault detection method. Since data of hydroelectric generators have non-linear correlation, the fault detection method must consider the correlation. In this paper, the fault detection method of hydroelectric generators is assumed to be installed as cloud services in a data center. Therefore, there are two requirements for the applied method in order to develop a practical fault detection system. The first one is that the method has to generate an appropriate fault detection model for each hydroelectric generator in a short time in order to reduce CPU cost in the data center. The second one is that the limited number of features must be selected in order to save storage cost in the data center and communication cost through the internet. Therefore, if all data of hydroelectric generators are gathered, communication and CPU usage fees must be expensive. In order to reduce the costs, it is required to utilize only data with effective features for fault detection. The proposed method can construct models using only normal data, detect faults using data with non-linear correlation, and select effective features for fault detection of hydroelectric generators. The proposed method is compared with an isolation forest (IF) based method with all features, an IF based method with features of the highest accuracy, a RRCF based method with all features, and a RRCF based method with features of the highest accuracy. Area Under the Curve (AUC) values and calculation time are evaluated from the point of view of practical application of fault detection in data center. The proposed method is verified to be the most effective from the point of view. The results are verified using the Mann-Whitney U test.