Reviewerly/exHarmony huggingface dataset:
On certain self-orthogonal AG codes with applications to Quantum error-correcting codes

In this paper a construction of quantum codes from self-orthogonal algebraic geometry codes is provided. Our method is based on the CSS construction as well as on some peculiar properties of the underlying algebraic curves, named Swiss curves. Several classes of well-known algebraic curves with many rational points turn out to be Swiss curves. Examples are given by Castle curves, GK curves, generalized GK curves and the Abdón–Bezerra–Quoos maximal curves. Applications of our method to these curves are provided. Our construction extends a previous one due to Hernando, McGuire, Monserrat, and Moyano-Fernández.
Evaluating the Morphosyntactic Well-formedness of Generated Texts

Text generation systems are ubiquitous in natural language processing applications.However, evaluation of these systems remains a challenge, especially in multilingual settings.In this paper, we propose L'AMBRE -a metric to evaluate the morphosyntactic wellformedness of text using its dependency parse and morphosyntactic rules of the language.We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks.To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers.We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages. 1
Excitability in an all-fiber laser with a saturable absorber section

We report the first demonstration of excitability in an all-fiber laser. We present clear experimental evidence in this passively <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> -switched device with gain and absorber sections for defining properties of excitability, including a threshold-based excitable response and a decreasing reaction delay between input pulse and excitatory response with increasing perturbation amplitude. Our experimental results are consistent with the homoclinic/saddle-loop mechanism that forms the basis of excitability as demonstrated by a study of the Yamada model; they pave the way toward novel and reliable all-fiber architectures for neuro-inspired photonic memory and computing applications.
StyHighNet: Semi-Supervised Learning Height Estimation from a Single Aerial Image via Unified Style Transferring

Recovering height information from a single aerial image is a key problem in the fields of computer vision and remote sensing. At present, supervised learning methods have achieved impressive results, but, due to domain bias, the trained model cannot be directly applied to a new scene. In this paper, we propose a novel semi-supervised framework, StyHighNet, for accurately estimating the height of a single aerial image in a new city that requires only a small number of labeled data. The core is to transfer multi-source images to a unified style, making the unlabeled data provide the appearance distribution as additional supervision signals. The framework mainly contains three sub-networks: (1) the style transferring sub-network maps multi-source images into unified style distribution maps (USDMs); (2) the height regression sub-network, with the function of predicting the height maps from USDMs; and (3) the style discrimination sub-network, used to distinguish the sources of USDMs. Among them, the style transferring sub-network shoulders dual responsibilities: On the one hand, it needs to compute USDMs with obvious characteristics, so that the height regression sub-network can accurately estimate the height maps. On the other hand, it is necessary that the USDMs have consistent distribution to confuse the style discrimination sub-network, so as to achieve the goal of domain adaptation. Unlike previous methods, our style distribution function is learned unsupervised, thus it is of greater flexibility and better accuracy. Furthermore, when the style discrimination sub-network is shielded, this framework can also be used for supervised learning. We performed qualitatively and quantitative evaluations on two sets of public data, Vaihingen and Potsdam. Experiments show that the framework achieved superior performance in both supervised and semi-supervised learning modes.
Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles

A vastly growing literature on explaining deep learning models has emerged. This paper contributes to that literature by introducing a global gradient-based model-agnostic method, which we call Marginal Attribution by Conditioning on Quantiles (MACQ). Our approach is based on analyzing the marginal attribution of predictions (outputs) to individual features (inputs). Specificalllly, we consider variable importance by mixing (global) output levels and, thus, explain how features marginally contribute across different regions of the prediction space. Hence, MACQ can be seen as a marginal attribution counterpart to approaches such as accumulated local effects (ALE), which study the sensitivities of outputs by perturbing inputs. Furthermore, MACQ allows us to separate marginal attribution of individual features from interaction effect, and visually illustrate the 3-way relationship between marginal attribution, output level, and feature value.
Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

Non-rigid image registration is a well-established method for estimating cardiac motion on 3D echocardiographic images. However, such images have relatively poor spatio-temporal resolution making registration challenging. Some of the main challenges are extracting features relevant to the registration problem and defining a suitable geometrical transformation to be applied. The latter can be tackled using a fuzzy inference system considering its potential in transformation modeling. From this point of view, feature-based image registration can be considered an identification problem in which the transformation parameters are computed through an optimization process. This study, thus, aims to estimate cardiac motion on 3D echocardiographic images based on feature-based non-rigid image registration through sets of modified fuzzy rules. The 3D volume features were extracted with the popular scale-invariant feature transform (SIFT) descriptors in 3D space. Sets of fuzzy rules were generated according to the extracted features to register every two consecutive frames. Finally, some supplementary rules modified the registration rule for estimating cardiac motion. Applying the fuzzy feature-based inference system on the STRAUS synthetic database showed the proposed method to be competitive with other well-established registration algorithms in terms of tracking error and accuracy of strain estimates. The proposed algorithm yielded a tracking error of 1 mm and a relative circumferential strain error of 0.82±4.69%. In addition, the potential of the proposed algorithm for clinical applications was confirmed by evaluating its performance on an in-vivo database called CETUS. This paper proposes a novel registration method based on fuzzy logic which was shown to enable tracking complex cardiac deformations in 3D echocardiographic images with high accuracy.
Computation And Communication Efficient Chinese Remainder Theorem Based Multi-Party Key Generation Using Modified RSA

The group key generation is a cryptographic mechanism, designed to provide a common key for a group, some common challenges to this type of algorithm is based on computation and communication cost. However, the involvement of high computational and communication operations to the algorithms then system with low configuration always faces problem in major time delay. The proposed approach is designed specifically to provide computation and communication efficient algorithm for low configuring systems, without violating the security compared to existing approaches. For making our approach computationally efficient, we replaced modular exponential operation with modular multiplication operation, helpful for low configuration system, making task early. For making our approach communication efficient, we used Chinese remainder theorem based multi-party key generation using modified RSA approach, reduced the message transmission cost. The aim of this approach is generating the common key for the group in less computation and communication cost. The major drawback of this approach is that the computation and communication cost becomes expensive when going for communication within the group because the usage of the modular exponential operation. The approach is secure compared to the existing approach, but expensive in terms of computation cost when communication starts, but the major task is to generate the common key for the group in less computation time for a low configuration system. The proposed approach used the modular multiplication operation in common group key generation and also reduced the threat of the factoring problem as the repetition value increased.
Hierarchical Overlapping Belief Estimation by Structured Matrix Factorization

Much work on social media opinion polarization focuses on a flat categorization of stances (or orthogonal beliefs) of different communities from media traces. We extend in this work in two important respects. First, we detect not only points of disagreement between communities, but also points of agreement. In other words, we estimate community beliefs in the presence of overlap. Second, in lieu of flat categorization, we consider hierarchical belief estimation, where communities might be hierarchically divided. For example, two opposing parties might disagree on core issues, but within a party, despite agreement on fundamentals, disagreement might occur on further details. We call the resulting combined problem a hierarchical overlapping belief estimation problem. To solve it, this paper develops a new class of unsupervised Non-negative Matrix Factorization (NMF) algorithms, we call Belief Structured Matrix Factorization (BSMF). Our proposed unsupervised algorithm captures both the latent belief intersections and dissimilarities, as well as hierarchical structure. We discuss properties of the algorithm and evaluate it on both synthetic and real-world datasets. In the synthetic dataset, our model reduces error by 40%. In real Twitter traces, it improves accuracy by around 10%. The model also achieves 96.08% self-consistency in a sanity check.
Why Visualize? Arguments for Visual Support in Decision Making

Just like the numerous applications of visualization, there are plenty of theoretical arguments for why visualization can aid in knowledge generation and communication.Meanwhile, to date, these arguments have been presented independently, which hinders the exploitation of underlying properties of visualization in practice.We present a network of arguments that explicate not only how different arguments build on each other, but also which arguments demand for tradeoffs in the design of visualizations.Furthermore, we call for the extension of our network to the application of visualization in decision making, as well as visual analytics.We highlight the practical applicability of our theoretical approach with examples, including one from the text analysis.
Detection of white blood cells using optimized qGWO

This paper presents an optimized quantum Grey Wolf Optimization algorithm (qGWO), which is an enhanced version of the Grey Wolf optimization algorithm for feature selection of blood cells, which can further used for the detection of WBCs. White blood cells count in the human body determines the immune system of the human body. A deviation in the count of WBCs from the normal cell count in the human body may indicate an abnormal condition. The proposed model uses a quantum grey wolf optimization algorithm for the detection of White Blood cells among the dataset of various types of blood cells. Quantum Grey Wolf algorithm is used to find the minimal set of optimal features from the set of available features to detect the White Blood Cells optimally. As the ordinary Grey Wolf Optimization algorithm also used to find the minimal set of optimal features, but the features selected by qGWO are better in terms of computational time. Further, several classification algorithms such as Support Vector Machine (SVM), Random Forest algorithm, K Nearest Neighbor(KNN) algorithm are applied to the model to predict its accuracy for the selected subset of features after feature selection. The performance of several classifiers is compared, and the model attained the maximum accuracy of 97.8% using KNN with minimum computational time. The result obtained shows that the algorithm proposed is capable of finding an optimal subset of features and maximizing the accuracy.
Metric Learning for Session-Based Recommendations

Session-based recommenders, used for making predictions out of users' uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users' events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study.
Optimal Motion Planning for Localization of Avalanche Victims by Multiple UAVs

This letter proposes a method for localization of avalanche victims by multiple UAVs. The method consists of three main parts. First, assuming that the UAVs and the victim are equipped with ARTVA receivers and a transmitter, respectively, we introduce a mathematical model of the receiver, which is used to estimate the position of the victim. Second, we derive a closed-form expression indicating the performance of this estimator. In particular, we show that the victim's observability index is captured by the persistency of excitation of a function of the UAVs trajectories. Third, we design and implement a motion planning algorithm that uses the estimation and the estimator's performance function for the (near) real-time generation of trajectories that guarantee feasible, safe, and time-efficient localization of avalanche victims.
Robust Anomaly Detection Using Reconstructive Adversarial Network

Detecting abnormal service performance is significant for Internet-based service management and operation. Recent advances in anomaly detection methods prefer unsupervised learning algorithms since they can work without manually labelled data. However, existing unsupervised methods converge into suboptimal solutions due to their heuristic-based objectives. Moreover, they frequently rely on the strong assumption that noise follows a Gaussian distribution, and their detection accuracy is also highly sensitive to threshold settings. To detect anomalies precisely and robustly, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adran</i> , an unsupervised anomaly detection model that introduces adversarial learning into a reconstructive model, generating a reconstructive adversarial network with an anomaly detection-based training objective. It tolerates non-Gaussian noise by activating the discriminator with a non-smooth function. Our experimental results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adran</i> achieves an improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\geq 32\%$ </tex-math></inline-formula> over the state-of-the-art methods in terms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F-score</i> . Moreover, the robustness analysis demonstrates that it is reasonably easy and straightforward to set an appropriate threshold using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adran</i> .
Quantum Modular Adder over GF(2n − 1) without Saving the Final Carry

Addition is the most basic operation of computing based on a bit system. There are various addition algorithms considering multiple number systems and hardware, and studies for a more efficient addition are still ongoing. Quantum computing based on qubits as the information unit asks for the design of a new addition because it is, physically, wholly different from the existing frequency-based computing in which the minimum information unit is a bit. In this paper, we propose an efficient quantum circuit of modular addition, which reduces the number of gates and the depth. The proposed modular addition is for the Galois Field GF(2n−1), which is important as a finite field basis in various domains, such as cryptography. Its design principle was from the ripple carry addition (RCA) algorithm, which is the most widely used in existing computers. However, unlike conventional RCA, the storage of the final carry is not needed due to modifying existing diminished-1 modulo 2n−1 adders. Our proposed adder can produce modulo sum within the range 0,2n−2 by fewer qubits and less depth. For comparison, we analyzed the proposed quantum addition circuit over GF(2n−1) and the previous quantum modular addition circuit for the performance of the number of qubits, the number of gates, and the depth, and simulated it with IBM’s simulator ProjectQ.
Cross Attentive Pooling for Speaker Verification

The goal of this paper is text-independent speaker verification where utterances come from `in the wild' videos and may contain irrelevant signal. While speaker verification is naturally a pair-wise problem, existing methods to produce the speaker embeddings are instance-wise. In this paper, we propose Cross Attentive Pooling (CAP) that utilises the context information across the reference-query pair to generate utterance-level embeddings that contain the most discriminative information for the pair-wise matching problem. Experiments are performed on the VoxCeleb dataset in which our method outperforms comparable pooling strategies.
Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

Abstract With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization) . We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-theart methods in terms of utility and scalability at the same level of privacy.
Selection and Optimization of Software Development Life Cycles Using a Genetic Algorithm

In the software field, a large number of projects fail, and billions of dollars are spent on these failed projects. Many software projects are also produced with poor quality or they do not exactly meet customers' expectations. Moreover, these projects may exceed project budget and/or time. The complexity of managing software development projects and the poor selection of software development life cycle (SDLC) models are among the top reasons for such failure. Various SDLC models are available, but no model is considered the best or worst. In this work, we propose a new methodology that solves the SDLC optimization problem using a genetic algorithm. The methodology selects the best SDLC and optimizes the completion time of the selected model. This study aims to help project managers in a software development organization select the proper SDLC model for their projects and optimize the selected model by minimizing the project completion time. The proposed SDLC model selection approach is based on a selection matrix that consists of a set of selection criteria and information related to the project's nature given by the project managers. Our methodology optimizes the selected SDLC model by reducing the project completion time and assigning duration for each phase. Several experiments were conducted to obtain the optimal completion time for the selected SDLC models. These experiments showed that our algorithm can optimally minimize the completion time of a given project and assign a duration for each phase. Experimental results showed that our methodology can reduce the completion time of a given project and produce realistic and optimal completion times for different SDLC models.
A Fault Aware Broad Learning System for Concurrent Network Failure Situations

The broad learning system (BLS) framework gives an efficient solution for training flat-structured feedforward networks and flat structured deep neural networks.However, the classical BLS model and other variants focus on the faultless situation only, where enhancement nodes, feature mapped nodes, and output weights of a BLS network are assumed to be realized in a perfect condition.When a trained BLS network suffers from coexistence of weight/node failures, the trained network has a greatly degradation in its performance if a countermeasure is not taken.In order to reduce the effect of weight/node failures on the BLS network's performance, this paper proposes an objective function for enhancing the fault aware performance of BLS networks.The objective function contains a fault aware regularizer term which handles the weight/node failures.A learning algorithm is then derived based on the objective function.The simulation results show that the performance of the proposed fault aware BLS (FABLS) algorithm is superior to the classical BLS and two state-of-the-arts BLS algorithms, namely correntropy criterion BLS (CBLS) and weighted BLS (WBLS).