Reviewerly/exHarmony huggingface dataset:
Stability and Convergence of Stochastic Particle Flow Filters

In this paper, we examine dynamic properties of particle flows for a recently derived parameterized family of stochastic particle flow filters for nonlinear filtering and Bayesian inference. In particular, we establish that particles maintain desired posterior distribution without the Gaussian assumption on measurement likelihood. Adopting the concept of Lyapunov stability, we further show that particles stay close but do not converge to the maximum likelihood estimate of the posterior distribution. The results demonstrate that stability of particle flows is maintained for this family of stochastic particle flow filters.
Online Reconfigurable Deep Learning-Aided Multi-User Detection for IoT

Deep learning has been exploited to tackle the multi-user detection problem of non-orthogonal multiple access (NOMA), for its high detection accuracy and low computational delay. Existing deep learning algorithms adopt an offline training and online deploying method. When the online system configuration, such as the number of the users, differs from that in offline training, existing deep learning algorithms fail to work due to the mismatch of the output dimensions. In this paper, we propose an online reconfigurable deep learning framework for multi-user detection which can adapt to diversified number of the users. Inspired by the factor graph representation of NOMA, the framework is designed as the composition of several interlinked deep neural network branches where each branch is dedicated for the detection of a single user. The connections among the branches are configurable to achieve online dynamic extension or clipping so as to match the varying number of NOMA users. Experiments validate the online reconfigurability and the performance gain of the proposed deep learning framework.
X-ray optics through the text mining of five decades of conference proceedings

Text mining is the process of transforming unstructured text into structured data for easy analysis. It uses natural language processing tools to interpret the human language and process text documents automatically. The extracted insights provide a valuable evaluation tool, which can provide systematic feedbacks and can drive machine-learning algorithms. We have applied these techniques to the SPIE proceeding papers, dedicated to the x-ray optics with special regards to the Optics for EUV, X-Ray, and Gamma-Ray Astronomy conference text data corpus, accessible through the ADS website. In particular, we worked on the collection of text formed by titles, abstracts, authors and affiliations to extract patterns, correlations and knowledge.
Text sentiment polarity classification based on TextCNN-SVM combination model

In the face of a large amount of information, managing, filtering, and screening information has become effectively one of the focuses in information science, especially in the accurate processing of text information. In this paper, a TextCNN-SVM combination model for text sentiment polarity classification was proposed and implemented in the TensorFlow framework. Combined with the ability of TextCNN to extract text eigenvalues well and the classification function of Support Vector Machine (SVM) to find the maximum segmentation hyperplane, we improve the shortcoming of the SoftMax layer, which is weaker than the machine learning algorithm in terms of classification ability and generalization ability. Compared with the traditional TextCNN model, the accuracy of the combined model has been improved by about 12.37%.
Cybersecurity: Perceived Threats and Policy Responses in the Gulf Cooperation Council

The Gulf Cooperation Council (GCC) is a political and economic union of the Arab states, comprising some of the fastest growing economies in the world due to the prevalence of oil and gas industry and more recently due to significant investments in the digital economy. However, in recent years, rates of cybercrime and cyber-threats have risen significantly, posing challenges to the security and economic performance of the region. This article deals with the necessity to engage in transnational cooperation to counter the complex challenges posed by cybercrime across the GCC. Interviews were held with members of the GCC political elite who hold expertise in cybersecurity affairs. An analysis is presented of cybersecurity threats in the region, problems of strategic cooperation and lack of dialogue to tackle common challenges in the areas of cybercrime and cybersecurity. Finally, specific aspects of GCC cybersecurity policy are discussed: preventive and mitigation actions; potential intraregional and international cooperation avenues; and the formulation of effective strategy to enhance cybersecurity capabilities and resilience. Although grounded in the GCC as a regional bloc, the findings also carry implications for other contexts, especially other Middle East and North Africa countries and, thus, represent a promising avenue for future research.
iSklearn: Automated Machine Learning with irace

Automated algorithm engineering has become an important asset for academia and industry. irace, for instance, is an algorithm configurator (AC) that has successfully designed effective algorithms for optimization problems. The major advantage of irace is combining learning and parallelization, but no fully-functional automated machine learning (AutoML) system powered by irace has yet been proposed. This is rather striking, as some of the most relevant existing AutoML tools are powered by ACs, of which irace is one of the most effective examples.In this work, we propose iSklearn, an irace-powered AutoML system. Our proposal improves existing work applying an AC to engineer a machine learning (ML) pipeline. First, our configuration space represents a minimalist pipeline template, demonstrating that simpler pipelines can be competitive with elaborate approaches (e.g. ensembles). Second, our configuration setup improves the application of AC-based AutoML to time series (TS) problems, and is more flexible to fit other applications. We evaluate iSklearn on three major ML domains, namely computer vision (CV), natural language processing (NLP), and TS. Results prove competitive to AUTOSKLEARN, a state-of-the-art AutoML system also built on scikit-learn. Furthermore, the compositions of the pipelines devised vary with the problem domain and dataset considered, providing further evidence for the need of AutoML tools. We conclude our investigation ablating through the proposed configuration space and setup to understand their impact on the performance of iSklearn.
Optimized generative adversarial network with fractional calculus based feature fusion using Twitter stream for spam detection

The social networks continue to augment their popularity due to the increased usage of the Internet. The people become connected using social media like Facebook and Twitter. This has increased impulsive communication, namely, spam and is utilized in accumulating information of an individual or marketing to cause offense against people. Spam detection in Twitter is a major issue because of small text and elevated language inconsistency in social media. Thus, it is imperative to devise a spam detection model that poses the ability to detect spam messages using Twitter data. This paper devises a novel spam detection model using a stream of Twitter data. Here, the data transformation is done on the input data using Yeo-Jhonson (YJ) transformation for making the data suitable for processing. The feature fusion is performed using Renyi entropy and Deep Belief Network (DBN). Moreover, the spam detection is performed using the Generative Adversial Network (GAN), which is trained by the proposed Conditional Autoregressive Value at Risk-Sail Fish (CAViaR-SF) algorithm. The proposed CAViaR-SF algorithm is devised by integrating Sail Fish optimizer (SFO) and Conditional Autoregressive Value at Risk (CAViaR) algorithm. The proposed CAViaR-SF offered maximal precision of 97.3%, recall of 99.2%, and F-measure of 98.2%.
Generation of Entangled Light from a Nondegenerate Three-Level Laser Coupled to a Two-Mode Vacuum Reservoir

The quantum properties of a nondegenerate three-level cascade laser coupled to a two-mode vacuum reservoir are throughly analyzed with the use of the pertinent master equation and stochastic differential equations associated with the normal ordering. Particularly, the enhancement of squeezing and the amplification of photon entanglement of the two-mode cavity light are investigated. It is found that the two cavity modes are strongly entangled, and the degree of entanglement is directly related to the two-mode squeezing. Moreover, the squeezing and entanglement of the cavity radiation enhance with the rate of atomic injection.
On the Foundations of Grounding in Answer Set Programming

Abstract We provide a comprehensive elaboration of the theoretical foundations of variable instantiation, or grounding, in Answer Set Programming (ASP). Building on the semantics of ASP’s modeling language, we introduce a formal characterization of grounding algorithms in terms of (fixed point) operators. A major role is played by dedicated well-founded operators whose associated models provide semantic guidance for delineating the result of grounding along with on-the-fly simplifications. We address an expressive class of logic programs that incorporates recursive aggregates and thus amounts to the scope of existing ASP modeling languages. This is accompanied with a plain algorithmic framework detailing the grounding of recursive aggregates. The given algorithms correspond essentially to the ones used in the ASP grounder gringo .
Uniform convergence rates for wavelet curve estimation in sup-norm loss

This paper presents the rates of uniform strong consistency of wavelet estimation for nonparametric function in sup-norm loss by introducing an empirical process approach. A compact support assumption on the explanatory variable is commonly used in nonparametric regression analysis. In the article, we consider the wavelet estimation analysis without any assumption on the compacity of the support of the explanatory variable. The optimal uniform convergence rates of the wavelet estimators are achieved by suitably choosing resolution level. These results are useful for wavelet theory on nonparametric signal recovery and analysis.
ScreenshotMatcher: Taking Smartphone Photos to Capture Screenshots

Taking screenshots is a common way of capturing screen content to share it with others or save it for later. Even though all major desktop operating systems come with a screenshot function, a lot of people also use smartphone cameras to photograph screen contents instead. While users see this method as faster and more convenient, image quality is significantly lower. With ScreenshotMatcher, we present a system that allows for capturing a high-fidelity screenshot by taking a smartphone photo of (part of) the screen. A smartphone application sends a photo of the screen region of interest to a program running on the PC which retrieves the matching screen region and sends it back to the smartphone. Comparing four feature matching algorithms and multiple parameters, we identified a combination of ORB keypoint detection (feature limit 2000) and a brute force feature matcher using Hamming distance as the best solution for this task (success rate: 85%, processing time: 90 ms). This raw performance results in a real-world success rate of 47% and a mean response time per screenshot of 878 ms as measured in a remote user study (N=19). Released as open-source code, ScreenshotMatcher may be used as a basis for applications and research prototypes that bridge the gap between PC and smartphone.
The HT-TBD Algorithm for Large Maneuvering Targets with Fewer Beats and More Groups

Aiming at the problem that the traditional HT-TBD algorithm is not effective in detecting the track of a large maneuvering target, this paper proposes an algorithm that starts with fewer beats and more groups before associating. By reducing the number of accumulated beats, the trajectory of the maneuvering target in a short time is approximated to a straight line. Through the tracking before detection method based on the modified Hough transform, the sub-track of the target in each time period is found, and the trajectory of the large maneuvering target is obtained through the track correlation.
Research on Interaction Design of Chemical Inquiry Virtual Experiment Based on Augmented Reality Technology

The rapid development of Augmented Reality (AR for short) has promoted great changes in teaching methods in recent years. The technical characteristics of AR and its educational value drive middle school chemistry experiments to gradually develop towards the direction of strong interactivity, interest, and visualization. However, how the interactive content of Augmented Reality meets the core demands of users to improve the learning effect in chemical experiments has become a new topic for educators and interaction designers. Under the technical background of Augmented Reality and guided by Inquiry-based Learning theory, how Augmented Reality interactive information can assist and guide users to conduct experimental exploration is explored in this paper with the guidance and middle school chemistry virtual experiment as the main study object. Based on considerable investigations and studies, the experiment of sodium and water reaction is taken as an example to design the human-computer interaction interface in the experimental scenario. In this design, the situational information required by users is superimposed on the real experimental environment in real time, contributing to improving the exploration consciousness of users, reducing the distraction of their attention and cognitive load, and deepening their understanding of the experimental content. In this study, the design of the 3D space interaction interface based on Augmented Reality technology is investigated, providing great reference value to the interaction design based on Augmented Reality applications.
Verification Code Recognition Based on Convolutional Neural Network

Verification code recognition system based on convolutional neural network. In order to strengthen the network security defense work, this paper proposes a novel verification code recognition system based on convolutional neural network. The system combines Internet technology and big data technology, combined with advanced captcha technology, can prevent hackers from brute force cracking behavior to a certain extent. In addition, the system combines convolutional neural network, which makes the verification code combine numbers and letters, which improves the complexity of the verification code and the security of the user account. Based on this, the system uses threshold segmentation method and projection positioning method to construct an 8-layer convolutional neural network model, which enhances the security of the verification code input link. The research results show that the system can enhance the complexity of captcha, improve the recognition rate of captcha, and improve the security of user accounting.
Design of Parity Preserving Arithmetic and Logic Unit using Reversible Logic Gates

Arithmetic and Logical Unit (ALU) is one of the key aspects of the digital world. In generally, it requires a colossal amount of power. This paper outlines a new design of one-bit Parity Preserving Reversible ALU circuit. To design the ALU with low power dissipation, we have used the concept of Reversible Logic computation. Conventional Circuits dissipate an enormous amount of power due to the loss of information bits in computation, but Reversible Circuits has no data loss as one on one mapping between outputs and inputs which leads to the minimization of power dissipation. In our design, we have used Parity Preserving Reversible Gates which are having Fault tolerance property as fault occurring at internal nodes results in an error at the output. So, Parity Preserving Reversible Gates is the one in which Output Parity remains same as of the Inputs. The aimed ALU has been carried out using Xilinx ISE 14.7 version software in Verilog HDL. To demonstrate the efficiency of proposed Parity Preserving Reversible Arithmetic and Logical Unit, each subpart is shown in terms of various parameters such as Quantum cost, Ancilla Inputs, Gate Count and Garbage Outputs and also a comparison with existing work is shown. The intended design is far better than the existing one because of its fault-tolerant capabilities. The proposed ALU extends its application over DNA mapping, Optical computation, cryptography, nanotechnology, quantum computing and digital signal processing.
Design and Evaluation of Visualization Techniques to Facilitate Argument Exploration

Abstract This paper reports the design and comparison of three visualizations to represent the structure and content within arguments. Arguments are artifacts of reasoning widely used across domains such as education, policy making, and science. An argument is made up of sequences of statements (premises) which can support or contradict each other, individually or in groups through Boolean operators. Understanding the resulting hierarchical structure of arguments while being able to read the arguments' text poses problems related to overview, detail, and navigation. Based on interviews with argument analysts we iteratively designed three techniques, each using combinations of tree visualizations (sunburst, icicle), content display (in‐situ, tooltip) and interactive navigation. Structured discussions with the analysts show benefits of each these techniques; for example, sunburst being good in presenting overview but showing arguments in‐situ is better than pop‐ups. A controlleduser study with 21 participants and three tasks shows complementary evidence suggesting that a sunburst with pop‐up for the content is the best trade‐off solution. Our results can inform visualizations within existing argument visualization tools and increase the visibility of ‘novel‐and‐effective’ visualizations in the argument visualization community.
A Categorized Reflection Removal Dataset with Diverse Real-world Scenes

Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR). A pipeline based on RAW data is used to capture perfectly aligned input images and transmission images. The dataset is constructed using diverse glass types under various environments to ensure diversity. By analyzing several reflection removal methods and conducting extensive experiments on our dataset, we show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection. We believe our dataset can help develop novel methods to remove real-world reflection better. Our dataset is available at https://alexzhao-hugga.github.io/Real-World-Reflection-Removal/.
HIDMS-PSO with Bio-inspired Fission-Fusion Behaviour and a Quorum Decision Mechanism

In this study, we propose a new variant of the HIDMS-PSO algorithm with a bio-inspired fission-fusion behaviour and a quorum decision mechanism (FFQ-HIDMS-PSO). In the new algorithm, units are conceptualised as self-organising fission-fusion societies that determine and adopt a suitable behaviour using unit-based quorum decisions. The incorporation of the two bio-inspired mechanisms provide "diversity aware" self-organising units that react to stagnation of particles by adopting a suitable fission-fusion behaviour, leading to a more efficient algorithm capable of maintaining significantly better population diversity throughout the search. The performance of the proposed algorithm was verified with three distinct experiments conducted using CEC’17 and CEC’05 test suites at 30 and 50 dimensions, comparing against 12 state-of-the-art metaheuristics and 12 state-of-the-art PSO variants. The proposed algorithm showed superior performance in these experiments by outperforming all 24 algorithms in all three experiments at 30 and 50 dimensions. The empirical evidence suggests that the proposed method also maintains significantly superior population diversity in comparison to the original HIDMS-PSO.
A Hadoop Allied Security Platform for Seismic Big Data Processing

The importance of seismic big data exploration, especially in gas and oil industries, is indispensable. The processing of such complex data becomes more critical when its size is extremely large. These days the dispose of seismic big data over the network is notably common. Hence, the security of this huge complex data is equally important during its transportation over an insecure channel. Consequently, the application of any security algorithm on complex big seismic data makes it impractical for adopting it in any industry. Numerous researches have been conducted to resolve these issues. However, any unified solution has not been proclaimed by the exiting related studies. Therefore, this research work affirms a unique unified platform that uses the integration of Hadoop and Hive for parallel processing and advanced indexing for faster execution of large complex data. At the same time, it uses a low complex elliptic curve cryptography (ECC) to ensure data security in terms of data confidentiality and integrity. The result shows that the proposed integrated technique offers higher time efficiency in terms of producing higher Throughput than other security combinations. It further shows it produces a low percentage of Data Loss and higher Entropy Value as well as Avalanche Effect which justifies its ability to offer higher data confidentiality and integrity.
On the Security of the K Minimum Values (KMV) Sketch

Data sketches are widely used to accelerate operations in big data analytics. For example, algorithms use sketches to compute the cardinality of a set, or the similarity between two sets. Sketches achieve significant reductions in computing time and storage requirements by providing probabilistic estimates rather than exact values. In many applications, an estimate is sufficient and thus, it is possible to trade accuracy for computational complexity; this enables the use of probabilistic sketches. However, the use of probabilistic data structures may create security issues because an attacker may manipulate the data in such a way that the sketches produce an incorrect estimate. For example, an attacker could potentially inflate the estimate of the number of distinct users to increase its revenues or popularity. Recent works have shown that an attacker can manipulate Hyperloglog, a sketch widely used for cardinality estimate, with no knowledge of its implementation details. This paper considers the security of K Minimum Values (KMV), a sketch that is also widely used to implement both cardinality and similarity estimates. Next sections characterize vulnerabilities at an implementation-independent level, with attacks formulated as part of a novel adversary model that manipulates the similarity estimate. Therefore, the paper pursues an analysis and simulation; the results suggest that as vulnerable to attacks, an increase or reduction of the estimate may occur. The execution of the attacks against the KMV implementation in the Apache DataSketches library validates these scenarios. Experiments show an excellent agreement between theory and experimental results.