Reviewerly/exHarmony huggingface dataset:
Multiple photon effective Hamiltonians in linear quantum optical networks

We give an alternative derivation for the explicit formula of the effective Hamiltonian describing the evolution of the quantum state of any number of photons entering a linear optics multiport. The description is based on the effective Hamiltonian of the optical system for a single photon and comes from relating the evolution in the Lie group that describes the unitary evolution matrices in the Hilbert space of the photon states to the evolution in the Lie algebra of the Hamiltonians for one and multiple photons. Our derivation complements previous results with a point of view which reminds of the Schrödinger picture. These group theory results allow us to prove that quantum optical linear networks obey different proportionality rules which relate the expected photon number at each input and output ports. In particular, we show that for a uniform photon input the average photon number at the output is preserved for any linear multiport.
Cat Swarm Optimization based Functional Link Multilayer Perceptron for Suppression of Gaussian and Impulse Noise from Computed Tomography Images

Background: The Gaussian and impulse noises corrupt the Computed Tomography (CT) images either individually or collectively, and the conventional fixed filters do not have the potential to suppress these noise. Objectives: These spurious noises affect the inherent features of CT image awkwardly. Hence, to handle such a situation adaptive Cat Swarm Optimization based Functional Link Multilayer Perceptron (CSO-FLMLP) has been proposed in this paper to get rid of unwanted noise from the CT images. Methods: Here, the nature-inspired CSO technique which is an optimization algorithm has been employed to assist in updating the weights of FLMLP network. In this work, the cost function considered for CSO is the error between noisy and contextual pixels of reference images which need to minimize. For examining the efficiency of CSO-FLMLP filter, it is compared with the other six competitive adaptive filters. Results: The performance of proposed approach and other state-of-the-art filters are compared on the basis of performance metrics like the structural similarity index (SSIM), peak signal to noise ratio (PSNR), computational time and convergence rate. Supremacy of CSO-FLMLP among the considered adaptive filters is validated through Friedman statistical test. Conclusion: The CSO-FLMLP adaptive filter could successfully re-move the dominant Gaussian, impulse or combination of both noises from the clinical CT images.
Improvement in accuracy for dimensionality reduction and reconstruction of noisy signals. Part II: The case of signal samples

In this paper, a novel interpretation of the problem of dimensionality reduction and reconstruction of random signals is studied. The problem and its solution target highly noisy signals and are considered in terms of signal samples. The solution is given by an iteration procedure where, on each iteration, solution parameters are optimally determined from the minimization of an associated cost function. The associated error diminishes with the increase in the number of iterations. The advantages of the considered technique are discussed and illustrated numerically.
Breaking Steganography

This article describes how to overcome the shortage of steganalysis for small capacity-based embedding. A slight modification method is proposed to break steganography. For a given image, traditional steganalysis methods are first used to achieve a preliminary result. For the “clear” image judged by steganalysis, it is still suspicious because of the incompleteness of steganalysis for small capacity. Thus, slight modifications are made to break the possibility of covert communication. The modifications are made on the locations with minimal distortion to guarantee high quality of the modified image. To this end, a proposed distortion minimization based algorithm using slight modification. Experimental results show that the error rate of secret data extraction is around 50% after implementation, which indicates that the covert communication of steganography is destroyed completely.
Insights into distributed feature ranking

In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing algorithms may not scale well when dealing with huge datasets, and a possible solution is to distribute the data in several nodes. In this work we explore the different ways of distributing the data (by features and by samples) and we evaluate to what extent it is possible to obtain similar results as those obtained with the whole dataset. Trying to deal with the challenge of distributing the feature ranking process, we have performed experiments with different aggregation methods and feature rankers, and also evaluated the effect of distributing the feature ranking process in the subsequent classification performance.
Advanced Arabic Natural Language Processing (ANLP) and its applications: Introduction to the special issue

Motivated by the practical needs of enhancing social media rumor refutation effectiveness, this paper is dedicated to develop a proper rumor refutation effectiveness index (REI), identify key factors influencing REI and propose decision making suggestions for rumor refutation platforms. 298,118 pieces of comments and 185,209 pieces of the reposters’ verification status of 248 rumor refutation microblogs on Sina Weibo (the Chinese equivalent of Twitter) are collected during a 1-year period using a web crawler. To extract the text characteristics and analyze the sentiment of the rumor refutation microblogs, Natural Language Processing (NLP) approaches are applied. To explore the relationship between REI and the content and contextual factors of the rumor refutation microblogs, four regression models based on the collected data are established, namely linear regression model, Support Vector regression model (SVR), Extreme Gradient Boosting regression model (XGBoostRegressor) and Light Gradient Boosting Machine regression model (LGBMRegressor). The LGBMRegressor has the best goodness-of-fit among the compared regression models. Then, SHapley Additive exPlanations (SHAP) is employed to visualize and explain the LGBMRegressor results. Decision making suggestions for rumor refutation platforms on how to organize rumor refutation microblogs under different situations such as rumor category, author’s influence and heat of topics are proposed.
JRDP

Despite the high number of people with disabilities, there are only a few job recommender systems that cater to their needs. This study analyses the implementation of an ontology based recommender system (JRDP) that offers suitable jobs to the disabled. The system considers whether or not an assistive technology exists to address certain disabilities in a specific application domain. If so, a list of available jobs from the application domain would be recommended to the disabled workforces. Two modules are considered as main components of the framework; knowledge-based and recommendation modules. 10 applicant with various disabilities participated in the testing session. The T-test evaluation demonstrates that JRDP requires less time for the recommendation process compared to ontology-less recommendation systems. The mean values for each construct of questionnaire can be calculated using the usability test, with the average mean reported to be 4.55. Cronbach's Alpha was used for testing the reliability of questionnaire, reporting a value of (0.759), which confirms its reliability.
Nonclassicality of photon-subtracted squeezing-enhanced coherent state

We investigate nonclassical properties of the quantum state generated by subtracting any number of photons from the squeezing-enhanced coherent state. It is found that the normalization factor of this new state is related univariate Hermite polynomials. The second-order correlation function, Q-function and degree of squeezing are also derived analytically. A compact expression for the fidelity between the new state and the squeezed coherent state is also derived by using the technique of integration within an ordered product of operators. Especially, this novel state exhibits stronger squeezing and some new statistical properties than the normal squeezed coherent state.
Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge

Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.
Shape principal component analysis as a targetless photogrammetric technique for condition monitoring of rotating machines

Rotating machines are widely used in engineering for applications which include power generation and machine propulsion systems. These machines have to be accurately monitored and maintained to avoid system failures. Vibration analysis, which involves the use of contact and non-contact measurement techniques to capture vibrational data indicative of the condition of a machine, is normally used for this purpose. 3D Point Tracking (3DPT) and Digital Image Correlation (DIC) constitute photogrammetric-based optical non-contact measurement techniques that have proven to be efficient for the vibration analysis of rotating machinery. In addition to complex image processing software and tracking algorithms, these two approaches typically require surface preparation in the form of markers and speckle patterns. These requirements limit the applicability of photogrammetry as a condition monitoring tool, especially when it comes to industrial environments. This paper proposes 2D shape analysis for target-less non-contact measurement in condition monitoring of rotating machines. Through comparison to measurements captured using conventional proximity probes on an experimental test setup, it is also illustrated how different dynamic characteristics of a rotating system can be distinguished using this measurement approach.
Identification of hydrocarbon microseepage induced alterations with spectral target detection and unmixing algorithms

Hydrocarbon micro and macro seeps alter chemical and mineral composition of the Earth’s surface, providing prospects for detection with remote sensing tools. There have been several studies focusing on mapping these anomalies by utilizing ever evolving multispectral and hyperspectral imaging instruments, which has proven their capacity for mapping both hydrocarbons and hydrocarbon-induced alterations so far. These studies broadly comprise of methods like calculating band ratios, spectral angle mapping, spectral feature fitting, and principal component analysis as detection techniques. However, there is a lack of concentration on advanced signature based detection algorithms and unmixing methods for mapping surface manifestations of hydrocarbon microseeps. Signature based detection algorithms utilize target spectra to correlate with each pixel’s spectrum in order to allocate possible target locations. Unmixing methods, on the other hand, require no input spectra beforehand, aiming to resolve each pixel’s spectral constituents and their corresponding abundance fractions. In this paper, the potential of all these methods in mapping microseepage related anomalies are evaluated by implementing and comparing them for Gemrik Anticline, one of the prospective hydrocarbon exploration fields in Turkey. Hence, it provides a complete knowledge on determination surface manifestations of hydrocarbon microseeps with the help of well known supervised target detection algorithms and hyperspectral unmixing algorithms. The study area is located in the Southeastern Anatolia, between the cities of Adıyaman and Şanlıurfa. The spectral signatures were collected with Analytical Spectral Devices Inc. (ASD) spectrometer during the field studies conducted by Avcıoğlu (2010), to be utilized as an input to the signature based detection algorithms as well as a reference to select the related abundance map among the outputs of unmixing methods. Advanced Space Borne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction before running the algorithms, is selected for the applications. Among the applied algorithms, Simplex Identification via Split Augmented Lagrangian (SISAL) is selected as a base of comparison, as it possess minimum calculated error metrics in the experiments. Another unmixing method, the Minimum Volume Simplex Algorithm (MVSA), and signature-based techniques, Desired Target Detection and Classification Algorithm (DTDCA) & Spectral Matched Filter (SMF) follow the success of the SISAL, respectively. The Crosta technique, which is performed as a conventional approach for experimental comparisons, has also shown its capability, succeeding these algorithms. The study provides an overall assessment for methodologies to be used for hydrocarbon microseepage mapping, which also serves guidance for further exploration studies in the region. The potential of ASTER data for hydrocarbon-induced alterations is also emphasized as a cost effective tool for the future applications.
Rapid Production of Many-Body Entanglement in Spin-1 Atoms via Cavity Output Photon Counting

We propose a simple and efficient method for generating metrologically useful quantum entanglement in an ensemble of spin-1 atoms that interacts with a high-finesse optical cavity mode. It requires straightforward preparation of $N$ atoms in the ${m}_{F}=0$ sublevel, tailoring of the atom-field interaction to give an effective Tavis-Cummings model for the collective spin-1 ensemble, and a photon counting measurement on the cavity output field. The photon number provides a projective measurement of the collective spin length $S$, which, for the chosen initial state, is heavily weighted around values $S\ensuremath{\simeq}\sqrt{N}$, for which the corresponding spin states are strongly entangled and exhibit Heisenberg scaling of the metrological sensitivity with $N$, as quantified by the quantum Fisher information.
GreenDB: Energy-Efficient Prefetching and Caching in Database Clusters

In this study, we propose an energy-efficient database system called GreenDB running on clusters. GreenDB applies a workload-skewness strategy by managing hot nodes coupled with a set of cold nodes in a database cluster. GreenDB fetches popular data tables to hot nodes, aiming to keep cold nodes in the low-power mode in increased time periods. GreenDB is conducive to reducing the number of power-state transitions, thereby lowering energy-saving overhead. A prefetching model and an energy saving model are seamlessly integrated into GreenDB to facilitate the power management in database clusters. We quantitatively evaluate GreenDB's energy efficiency in terms of managing, fetching, and storing data. We compare GreenDB's prefetching strategy with the one implemented in Postgresql. Experimental results indicate that GreenDB conserves the energy consumption of the existing solution by up to 98.4 percent. The findings show that the energy efficiency of GreenDB can be optimized by tuning system parameters, including table size, hit rates, number of nodes, number of disks, and inter-arrival delays.
LDA classifier monitoring in distributed streaming systems

An important problem in real systems for mining data streams is to detect changes in the dynamic model describing the temporal data. Such changes indicate that the underlying data has undergone a transition which may well require attention. A distributed setting poses one of the main challenges in this type of change detection. In a distributed setting, model training requires centralizing the data from all nodes (hereafter, synchronization), which is very costly in terms of communication. In order to minimize communication, a monitoring algorithm should be executed locally at each node, while preserving the validity of the global model (that is, the model that will be computed if a synchronization takes place). To achieve this goal, we propose the first communication-efficient algorithm for monitoring a classification model over distributed, dynamic data streams. While the approach is general, here we concentrate on Linear Discriminant Analysis (LDA), a popular method for classification and dimensionality reduction in many fields. We mainly apply tools from the realms of linear algebra and multi-variate analysis in order to solve the problem at hand. The resulting implementation is quite straightforward. The emphasis of this work is not on solving the distributed optimization problem that corresponds to finding a classifier over the distributed data; instead, we continuously monitor the current classifier to check that it still fits the data. In addition to the theoretical guarantee of the proposed monitoring algorithm, we demonstrate how it reduces communication volume by up to two orders of magnitude (compared to synchronization in every round) on synthetic data as well as three real datasets from different worlds of content. Our approach monitors the classification model itself as opposed to its misclassifications, which makes it possible to detect the change before misclassifications occur.
A distributed cooperative learning algorithm based on Zero-Gradient-Sum strategy using Radial Basis Function Network

This paper aims to propose a distributed cooperative learning (DCL) algorithm for Radial Basis Function Network (RBFN), where training samples are often extremely large-scale, high-dimensional and located on distributed nodes over undirected and connected networks. The algorithm is investigated for RBFN with different basic functions, such as Gaussian function, multi-quadric function, thin plate spline function and so on. The proposed DCL–RBFN algorithm is based on Zero-Gradient-Sum (ZGS) distributed optimization strategy and works in a fully distributed fashion, which has no requirement on central node and communication of raw data from each node during the training process. Moreover, in order to avoid the waste of unnecessary network resources, an event-triggered communication mechanism is applied to our algorithm. Each node exchanges learned information with its neighboring nodes using the ZGS strategy under event-triggered mechanism. The convergence of the proposed DCL–RBFN and event-triggered DCL–RBFN algorithms are presented by using the Lyapunov method. It is proved that the proposed algorithms have exponential convergence speed and require less communication data over networks. Finally, a series of numerical simulations are given to verify the advantages of the proposed algorithms.
On hidden sums compatible with a given block cipher diffusion layer

Sometimes it is possible to embed an algebraic trapdoor into a block cipher. Building on previous research, in this paper we investigate an especially dangerous algebraic structure, which is called a hidden sum and which is related to some regular subgroups of the affine group. Mixing group theory arguments and cryptographic tools, we pass from characterizing our hidden sums to designing an efficient algorithm to perform the necessary preprocessing for the exploitation of the trapdoor.
Addressing the Practical Limitations of Noisy-OR Using Conditional Inter-Causal Anti-Correlation with Ranked Nodes

Numerous methods have been proposed to simplify the problem of eliciting complex conditional probability tables in Bayesian networks. One of the most popular methods -“Noisy-OR”- approximates the required relationship in many real-world situations between a set of variables that are potential causes of an effect variable. However, the Noisy-OR function has the conditional inter-causal independence (CII) property which means that `explaining away' behavior-one of the most powerful benefits of BN inference-is not present when the effect variable is observed as false. Hence, for many real-world problems where the Noisy-OR has been used or proposed, it may be deficient as an approximation of the required relationship. However, there is a very simple alternative solution, namely to define the variables as ranked nodes and to use the ranked node weighted average function. This does not have the CII property-instead, we prove it has the conditional anti-correlation property required to ensure that explaining away works in all cases. Moreover, ranked node variables are not restricted to binary states, and hence provide a more comprehensive and general solution to Noisy-OR in all cases.
Mixed language queries in online searches

Purpose With the increasing number of online multilingual resources, cross-language information retrieval (CLIR) has drawn much attention from the information retrieval (IR) research community. However, few studies have examined how and why multilingual searchers seek information in two or more languages, specifically how they switch and mix language in queries to get satisfying results. The purpose of this paper is to focus on Chinese–English bilinguals’ intra-sentential code-switching behaviors in online searches. The scenarios and reasons of code-switching, factors that may affect code-switching, the patterns of mixed language query formulation and reformulation and how current IR systems and other search tools can facilitate such information needs were examined. Design/methodology/approach In-depth semi-structured interviews were used as the research method. In total, 30 participants were recruited based on their English proficiency, location and profession, using a purposive sampling method. Findings Four scenarios and four reasons for using Chinese–English mixed language queries to cover information needs were identified, and results suggest that linguistic and cultural/social factors are of equivalent importance in code-switching behaviors. English terms and Chinese terms in queries play different roles in searches, and mixed language queries are irreplaceable by either single language queries or other search facilitating features. Findings also suggest current search engines and tools need greater emphasis in the user interface and more user education is required. Originality/value This study presents a qualitative analysis of bilinguals’ code-switching behaviors in online searches. Findings are expected to advance the theoretical understanding of bilingual users’ search strategies and interactions with IR systems, and provide insights for designing more effective IR systems and tools to discover multilingual online resources, including cross-language controlled vocabularies, personalized CLIR tools and mixed language query assistants.