Reviewerly/exHarmony huggingface dataset:
On greedy algorithms for binary de Bruijn sequences

We propose a general greedy algorithm for binary de Bruijn sequences, called Generalized Prefer-Opposite (GPO) Algorithm, and its modifications. By identifying specific feedback functions and initial states, we demonstrate that most previously-known greedy algorithms that generate binary de Bruijn sequences are particular cases of our new algorithm.
On algebraic abstractions for concurrent separation logics

Concurrent separation logic is distinguished by transfer of state ownership upon parallel composition and framing. The algebraic structure that underpins ownership transfer is that of partial commutative monoids (PCMs). Extant research considers ownership transfer primarily from the logical perspective while comparatively less attention is drawn to the algebraic considerations. This paper provides an algebraic formalization of ownership transfer in concurrent separation logic by means of structure-preserving partial functions (i.e., morphisms) between PCMs, and an associated notion of separating relations. Morphisms of structures are a standard concept in algebra and category theory, but haven't seen ubiquitous use in separation logic before. Separating relations. are binary relations that generalize disjointness and characterize the inputs on which morphisms preserve structure. The two abstractions facilitate verification by enabling concise ways of writing specs, by providing abstract views of threads' states that are preserved under ownership transfer, and by enabling user-level construction of new PCMs out of existing ones.
Continuous-variable graph states for quantum metrology

Graph states are a unique resource for quantum information processing, such as measurement-based quantum computation. Here, we theoretically investigate the use of continuous-variable graph states for single-parameter quantum metrology, including both phase and displacement sensing. We identified the optimal graph states for the two sensing modalities and showed that Heisenberg scaling of the accuracy for both phase and displacement sensing can be achieved with local homodyne measurements.
RF-AdaCost: WebShell Detection Method that Combines Statistical Features and Opcode

WebShell is called a webpage backdoor. After hackers invade a website, they usually mix backdoor files with normal webpage files in the WEB directory of the website service area. Then, they use a browser to access the backdoor and obtain a command execution environment to control the website server. WebShell detection methods have stringent requirements because of the flexibility of the PHP language and the increasing number of hidden techniques used by hackers. The term frequency–inverse document frequency (TF-IDF) used in the existing random forest–gradient boosting decision tree (RF-GBDT) algorithm does not consider the distribution information and classification capabilities of feature words among classes, and no balance exists between false negative and false positive rates. This work proposes a PHP WebShell detection model called RF-AdaCost, which stands for random forest–misclassification cost-sensitive AdaBoost, based on RF-GBDT. We used the statistical characteristics of PHP source files, including information entropy and index of coincidence, and extracted the opcode sequences of PHP source files, thus merging statistical features and opcode sequences to improve the detection efficiency of the WebShell. Experimental results show that the RF-AdaCost algorithm demonstrates better performance than the RF-GBDT algorithm.
Quantum advantage for differential equation analysis

Quantum algorithms for differential equation solving, data processing, and machine learning potentially offer an exponential speedup over all known classical algorithms. However, there also exist obstacles to obtaining this potential speedup in useful problem instances. The essential obstacle for quantum differential equation solving is that outputting useful information may require difficult postprocessing, and the essential obstacle for quantum data processing and machine learning is that inputting the data is a difficult task just by itself. In this study, we demonstrate that, when combined, these difficulties solve one another. We show how the output of quantum differential equation solving can serve as the input for quantum data processing and machine learning, allowing dynamical analysis in terms of principal components, power spectra, and wavelet decompositions. To illustrate this, we consider continuous-time Markov processes on epidemiological and social networks. These quantum algorithms provide an exponential advantage over existing classical Monte Carlo methods.
Mapping the current landscape of citizen-driven environmental monitoring: a systematic literature review

Citizen observatories can be defined as socio-technical constellations designed to make people who are nonprofessional scientists empowered entities. By enabling citizens to monitor their environment via collection and sharing of data, citizen observatories can be viewed as an application or iteration of citizen science. This article contributes to mapping out the current landscape of citizen engagement and participation in environmental monitoring. We draw on a systematic analysis of 57 peer-reviewed papers and argue that citizen observatories have the potential to make a substantial social impact by bringing together different stakeholders and digital technologies. We also discuss the challenges of civic participation, quality of the data gathered, and long-term sustainability. The article identifies several critical gaps in the field that provide a guide for future studies on citizen observatories. Furthermore, it encourages deeper engagement within developing nations and investigates the impact of this approach on nurturing sustainable societies globally.
Asynchronous parallel stochastic Quasi-Newton methods

Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm is the first one that truly parallelizes L-BFGS with a convergence guarantee. Adopting the variance reduction technique, a prior stochastic L-BFGS, which has not been designed for parallel computing, reaches a linear convergence rate. We prove that our asynchronous parallel scheme maintains the same linear convergence rate but achieves significant speedup. Empirical evaluations in both simulations and benchmark datasets demonstrate the speedup in comparison with the non-parallel stochastic L-BFGS, as well as the better performance than first-order methods in solving ill-conditioned problems.
Low-degree permutation rational functions over finite fields

We determine all degree-4 rational functions f(X) in F_q(X) which permute P^1(F_q), and answer two questions of Ferraguti and Micheli about the number of such functions and the number of equivalence classes of such functions up to composing with degree-one rational functions. We also determine all degree-8 rational functions f(X) in F_q(X) which permute P^1(F_q) in case q is sufficiently large, and do the same for degree 32 in case either q is odd or f(X) is a nonsquare. Further, for most other positive integers n<4096, for each sufficiently large q we determine all degree-n rational functions f(X) in F_q(X) which permute P^1(F_q) but which are not compositions of lower-degree rational functions in F_q(X). Some of these results are proved by using a new Galois-theoretic characterization of additive (linearized) polynomials among all rational functions, which is of independent interest.
The Effect of Low-pass Pre-filtering on Subvoxel Registration Algorithms in Digital Volume Correlation: A revisited study

Abstract In digital volume correlation (DVC), random image noise in volumetric images leads to increased systematic error and random error in the displacements measured by subvoxel registration algorithms. Previous studies in DIC have shown that adopting low-pass pre-filtering to the images prior to the correlation analysis can effectively mitigate the systematic error associated with the classical forward additive Newton-Raphson (FA-NR) algorithm. However, the effect of low-pass pre-filtering on the state-of-the-art inverse compositional Gauss-Newton (ICGN) algorithm has not been investigated so far. In this work, we focus on the effect of low-pass pre-filtering on two mainstream subvoxel registration algorithms (i.e., 3D FA-NR algorithm and 3D IC-GN algorithm) used in DVC. Basic principles and theoretical error analyses of the two algorithms are described first. Then, based on numerical experiments with precisely controlled subvoxel displacements and noise levels, the influences of image noise on the displacements measured by two subvoxel algorithms are examined. Further, the effects of low-pass pre-filtering on these two subvoxel algorithms are examined for simulated image sets with different noise levels and deformation modes. The results show that the low-pass pre-filtering can effectively suppress the systematic errors for the 3D FA-NR algorithm, which is consistent with the previously drawn conclusion in DIC. On the contrary, different form the 3D FA-NR algorithm, the 3D IC-GN algorithm itself can reduce the influence of image noise, and the effect of low-pass pre-filtering on it is not so obvious as on 3D FA-NR algorithm.
Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation

Background Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks. Objective This study aims to enable accurate estimates of the number of patients matching a federated query while providing strong guarantees on the amount of protected medical information revealed. Methods We introduce a novel probabilistic approach to running federated network queries. It combines an algorithm called HyperLogLog with obfuscation in the form of hashing, masking, and homomorphic encryption. It is tunable, in that it allows networks to balance accuracy versus privacy, and it is computationally efficient even for large networks. We built a user-friendly free open-source benchmarking platform to simulate federated queries in large hospital networks. Using this platform, we compare the accuracy, k-anonymity privacy risk (with k=10), and computational runtime of our algorithm with several existing techniques. Results In simulated queries matching 1 to 100 million patients in a 100-hospital network, our method was significantly more accurate than adding aggregate counts while maintaining k-anonymity. On average, it required a total of 12 kilobytes of data to be sent to the network hub and added only 5 milliseconds to the overall federated query runtime. This was orders of magnitude better than other approaches, which guaranteed the exact answer. Conclusions Using our method, it is possible to run highly accurate federated queries of clinical data repositories that both protect patient privacy and scale to large networks.
Innovative Platform for Designing Hybrid Collaborative &amp; Context-Aware Data Mining Scenarios

The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique-CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms-k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform.
Directionally-Unbiased Unitary Optical Devices in Discrete-Time Quantum Walks

The optical beam splitter is a widely-used device in photonics-based quantum information processing. Specifically, linear optical networks demand large numbers of beam splitters for unitary matrix realization. This requirement comes from the beam splitter property that a photon cannot go back out of the input ports, which we call "directionally-biased". Because of this property, higher dimensional information processing tasks suffer from rapid device resource growth when beam splitters are used in a feed-forward manner. Directionally-unbiased linear-optical devices have been introduced recently to eliminate the directional bias, greatly reducing the numbers of required beam splitters when implementing complicated tasks. Analysis of some originally directional optical devices and basic principles of their conversion into directionally-unbiased systems form the base of this paper. Photonic quantum walk implementations are investigated as a main application of the use of directionally-unbiased systems. Several quantum walk procedures executed on graph networks constructed using directionally-unbiased nodes are discussed. A significant savings in hardware and other required resources when compared with traditional directionally-biased beam-splitter-based optical networks is demonstrated.
Central Limit Theorems for Open Quantum Random Walks on the Crystal Lattices

We consider the open quantum random walks on the crystal lattices and investigate the central limit theorems for the walks. On the integer lattices the open quantum random walks satisfy the central limit theorems as was shown by Attal, {\it et al}. In this paper we prove the central limit theorems for the open quantum random walks on the crystal lattices. We then provide with some examples for the Hexagonal lattices. We also develop the Fourier analysis on the crystal lattices. This leads to construct the so called dual processes for the open quantum random walks. It amounts to get Fourier transform of the probability densities, and it is very useful when we compute the characteristic functions of the walks. In this paper we construct the dual processes for the open quantum random walks on the crystal lattices providing with some examples.
Enhanced-PCA based Dimensionality Reduction and Feature Selection for Real-Time Network Threat Detection

With the rise of the data amount being collected and exchanged over networks, the threat of cyber-attacks has also increased significantly. Timely and accurate detection of any intrusion activity in networks has become a crucial task in order to safeguard data and other valuable assets. While manual moderation and programmed logic have been used for this purpose, the use of machine learning algorithms for superior pattern mapping is desired. The system logs in a network tend to include many parameters, and not all of them provide indications of an impending network threat. The selection of the right features is thus important for achieving better results. There is a need for accurate mapping of high dimension features to low dimension intermediate representations while retaining crucial information. In this paper, an approach for feature reduction and selection when working on the task of network threat detection is proposed. This approach modifies the traditional Principal Component Analysis (PCA) algorithm by working on its shortcomings and by minimizing the false detection rates. Specifically, work has been done upon the calculation of symmetric uncertainty and subsequent sorting of features. The performance of the proposed approach is evaluated on four standard-sized datasets that are collected using the Microsoft SYSMON real-time log collection tool. The proposed method is found to be better than the standard PCA and FAST methods for data reduction. The proposed approach makes a strong case as a dimensionality reduction and feature selection technique for minimizing false detection rates when operating on real-time data.
ActiveIter: Meta Diagram based Active Learning in Social Networks Alignment

Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several reasons, i.e., lack of training data, network heterogeneity and one-to-one constraint. Existing network alignment works usually require a large number of training instances, but such a demand can hardly be met in applications, as manual anchor link labeling is extremely expensive. Significantly different from other homogeneous network alignment works, information in online social networks is usually of heterogeneous categories, the incorporation of which in model building is not an easy task. Furthermore, the one-to-one cardinality constraint on anchor links renders their inference process intertwistingly correlated. To resolve these three challenges, a novel network alignment model, namely ActiveIter (Active Iterative Alignment), is introduced in this paper. The model ActiveIter defines a set of inter-network meta diagrams for anchor link feature extraction, adopts active learning for effective label query and uses greedy link selection for anchor link cardinality filtering. Extensive experiments were performed on a real-world aligned networks dataset, and the experimental results have demonstrated the effectiveness of ActiveIter compared with other state-of-the-art baseline methods.
Distillation of Greenberger–Horne–Zeilinger States by Combinatorial Methods

We prove a lower bound on the rate of Greenberger-Horne-Zeilinger states distillable from pure multipartite states by local operations and classical communication (LOCC). Our proof is based on a modification of a combinatorial argument used in the fast matrix multiplication algorithm of Coppersmith and Winograd. Previous use of methods from algebraic complexity in quantum information theory concerned transformations with stochastic LOCC (SLOCC), resulting in an asymptotically vanishing success probability. In contrast, our new protocol works with an asymptotically vanishing error.
Loophole-free plug-and-play quantum key distribution

Abstract Robust, simple, and flexible quantum key distribution (QKD) is vital for realizing practical applications of this technique. Contrary to typical phase-coded QKD schemes, the plug-and-play QKD design requires only one arm-length-insensitive interferometer without active feedback, in which the noise is automatically compensated by the two-way structure. However, there are certain possible loopholes in the typical plug-and-play designs, which require consideration and strict monitoring. This study proposes a new design of theoretically loophole-free plug-and-play QKD scheme with two-way protocol and presents an experimental demonstration of said scheme. The security is analyzed under a collective attack scenario assisted by the decoy state method. The scheme was implemented in a 50.4 km commercial fibre without active feedback. The system showed highly robust performance with an ultra-low error rate and maintained an ultra-high visibility of 0.9947 ± 0.0002 through significant environmental changes over 24 hours.
Polygamy relations of multipartite systems

We investigate the polygamy relations of multipartite quantum states. General polygamy inequalities are given in the $$\alpha $$ th $$(\alpha \ge 2)$$ power of concurrence of assistance, $$\beta $$ th $$(\beta \ge 1)$$ power of entanglement of assistance, and the squared convex-roof extended negativity of assistance (SCRENoA).
Adversarial Attacks Hidden in Plain Sight

Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making any desired incorrect classification, potentially with very high certainty. Several defensive approaches increase robustness against adversarial attacks, demanding attacks of greater magnitude, which lead to visible artifacts. By considering human visual perception, we compose a technique that allows to hide such adversarial attacks in regions of high complexity, such that they are imperceptible even to an astute observer. We carry out a user study on classifying adversarially modified images to validate the perceptual quality of our approach and find significant evidence for its concealment with regards to human visual perception.
A Dataset Schema for Cooperative Learning from Demonstration in Multi-robot Systems

Multi-Agent Systems (MASs) have been used to solve complex problems that demand intelligent agents working together to reach the desired goals. These Agents should effectively synchronize their individual behaviors so that they can act as a team in a coordinated manner to achieve the common goal of the whole system. One of the main issues in MASs is the agents' coordination; it is common for domain experts observing MASs execution to disapprove agents' decisions. Even if the MAS was designed using the best methods and tools for agents' coordination, this difference in decisions between experts and MAS is confirmed. Therefore, this paper proposes a new dataset schema to support learning the coordinated behavior in MASs from demonstration. The results of the proposed solution are validated in a Multi-Robot System (MRS) organizing a collection of new cooperative plan recommendations from the demonstration by domain experts.
Acceleration with a Ball Optimization Oracle

Consider an oracle which takes a point $x$ and returns the minimizer of a convex function $f$ in an $\ell_2$ ball of radius $r$ around $x$. It is straightforward to show that roughly $r^{-1}\log\frac{1}{\epsilon}$ calls to the oracle suffice to find an $\epsilon$-approximate minimizer of $f$ in an $\ell_2$ unit ball. Perhaps surprisingly, this is not optimal: we design an accelerated algorithm which attains an $\epsilon$-approximate minimizer with roughly $r^{-2/3} \log \frac{1}{\epsilon}$ oracle queries, and give a matching lower bound. Further, we implement ball optimization oracles for functions with locally stable Hessians using a variant of Newton's method. The resulting algorithm applies to a number of problems of practical and theoretical import, improving upon previous results for logistic and $\ell_\infty$ regression and achieving guarantees comparable to the state-of-the-art for $\ell_p$ regression.