Reviewerly/exHarmony huggingface dataset:
Pose-Based Composition Improvement for Portrait Photographs

This paper studies the composition in portrait paintings and develops an algorithm to improve the composition of portrait photographs based on example portrait paintings. A study of portrait paintings shows that the placement of the face and the figure is pose-related. Based on this observation, this paper develops an algorithm to improve the composition of a portrait photograph by learning the placement of the face and the figure from an example portrait painting. This example portrait painting is selected based on the similarity of its figure pose to that of the input photograph. This similarity measure is modeled as a graph matching problem. Finally, space cropping is performed using an optimization function to assign a similar location for each body part of the figure in the photograph with that of the figure in the example portrait painting. The experimental results demonstrate the effectiveness of the proposed method. A user study shows that the proposed pose-based composition improvement is preferred more than rule-based methods and learning-based methods.
Recurrence and Transience of Continuous-Time Open Quantum Walks

This paper is devoted to the study of continuous-time processes known as continuous-time open quantum walks (CTOQW). A CTOQW represents the evolution of a quantum particle constrained to move on a discrete graph, but which also has internal degrees of freedom modeled by a state (in the quantum mechanical sense). CTOQW contain as a special case continuous-time Markov chains on graphs. Recurrence and transience of a vertex are an important notion in the study of Markov chains, and it is known that all vertices must be of the same nature if the Markov chain is irreducible. In the present paper we address the corresponding result in the context of irreducible CTOQW. Because of the "quantum" internal degrees of freedom, CTOQW exhibit non standard behavior, and the classification of recurrence and transience properties obeys a "trichotomy" rather than the classical dichotomy. Essential tools in this paper are the so-called "quantum trajectories" which are jump stochastic differential equations which can be associated with CTOQW.
High-Performance Memory Snapshotting for Real-Time, Consistent, Hypervisor-Based Monitors

This paper presents a concurrent-computing approach-high-performance memory snapshotting-to improving security-introspection of virtual machine guest memory. Efficient introspection improves security monitoring in existing hypervisor systems with real-time, consistent memory introspection capabilities. Efficient introspection has three requirements that each must be met to provide protection against evasive threats: native memory introspection performance, accpetable guest performance, and consistent introspection view of guest memory. Existing introspection systems have provided one or two of these properties but not all three at once. High-performance memory snapshots are evaluated as a solution for meeting all three efficient introspection requirements. In this work we describe how existing system performance can be improved with high-performance snapshotting, present an efficient introspection prototype that has been released as an element of the open-source LibVMI introspection library <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , evaluate the efficient introspection prototype on both applications and microbenchmarks, provide demonstrations of introspection application modules enabled by efficient introspection, and provide performance guidance for developing introspection applications utilizing efficient introspection.
Edgeworth expansions for multivariate random sums

The sum of a random number of independent and identically distributed random vectors has a distribution which is not analytically tractable, in the general case. The problem has been addressed by means of asymptotic approximations embedding the number of summands in a stochastically increasing sequence. Another approach relies on fitting flexible and tractable parametric, multivariate distributions, as for example finite mixtures. Both approaches are investigated within the framework of Edgeworth expansions. A general formula for the fourth-order cumulants of the random sum of independent and identically distributed random vectors is derived and it is shown that the above mentioned asymptotic approach does not necessarily lead to valid asymptotic normal approximations. The problem is addressed by means of Edgeworth expansions. Both theoretical and empirical results suggest that mixtures of two multivariate normal distributions with proportional covariance matrices satisfactorily fit data generated from random sums where the counting random variable and the random summands are Poisson and multivariate skew-normal, respectively.
Contextual Search via Intrinsic Volumes

We study the problem of contextual search, a multidimensional generalization of binary search that captures many problems in contextual decision-making. In contextual search, a learner is trying to learn the value of a hidden vector $v \in [0,1]^d$. Every round the learner is provided an adversarially chosen context $u_t \in \R^d$, submits a guess $p_t$ for the value of $\langle u_t, v\rangle$, learns whether $p_t < \langle u_t, v\rangle$, and incurs loss $\ell(\langle u_t, v\rangle, p_t)$ (for some loss function $\ell$). The learner's goal is to minimize their total loss over the course of $T$ rounds. We present an algorithm for the contextual search problem for the symmetric loss function $\ell(\theta, p) = |\theta - p|$ that achieves $O_{d}(1)$ total loss. We present a new algorithm for the dynamic pricing problem (which can be realized as a special case of the contextual search problem) that achieves $O_{d}(\log \log T)$ total loss, improving on the previous best known upper bounds of $O_{d}(\log T)$ and matching the known lower bounds (up to a polynomial dependence on $d$). Both algorithms make significant use of ideas from the field of integral geometry, most notably the notion of intrinsic volumes of a convex set. To the best of our knowledge, this is the first application of intrinsic volumes to algorithm design.
Whose Domain and Whose Ontology? Preserving Human Radical Reflexivity over the Efficiency of Automatically Generated Feedback Alone

In this chapter, we challenge an increase in the uncritical application of algorithmic processes for providing automatically generated feedback for students, within a neoliberal framing of contemporary higher education. Initially, we discuss our concerns alongside networked learning principles, which developed as a critical pedagogical response to new online learning programmes and platforms. These principles now overlap too, with the notion that we are living in ‘postdigital’ times, where automatically generated feedback never stands alone, but is contested and supplemented by physical encounters and human feedback. First, we make observations on the e-marking platform Turnitin, alongside other rapidly developing artificial intelligence (AI) systems. When generic (but power-laden) maps are incorporated into both student and staff ‘perceived’ spaces through AI, we surface the aspects of feedback that risk being lost. Second, we draw on autoethnographic understandings of our own lived experience of performing radically reflexive feedback within a Master’s in Education programme. A radically reflexive form of feedback may not follow a pre-defined map, but it does offer a vehicle to restore individual student and staff voices and critical self-navigation of both physical and virtual learning spaces. This needs to be preserved in the ongoing shaping of the contemporary ‘postdigital’ university.
On the circular correlation coefficients for bivariate von Mises distributions on a torus

This paper studies circular correlations for the bivariate von Mises sine and cosine distributions. These are two simple and appealing models for bivariate angular data with five parameters each that have interpretations connected to those in the ordinary bivariate normal model. However, the variability and association of the angle pairs cannot be easily deduced from the model parameters unlike the bivariate normal. Thus to compute such summary measures, tools from circular statistics are needed. We derive analytic expressions and study the properties of the Jammalamadaka–Sarma and Fisher–Lee circular correlation coefficients for the von Mises sine and cosine models. Likelihood-based inference of these coefficients from sample data is then presented. The correlation coefficients are illustrated with numerical and visual examples, and the maximum likelihood estimators are assessed on simulated and real data, with comparisons to their non-parametric counterparts. Implementations of these computations for practical use are provided in our R package BAMBI.
Formalising Mathematics in Simple Type Theory

Despite the considerable interest in new dependent type theories, simple type theory (which dates from 1940) is sufficient to formalise serious topics in mathematics. This point is seen by examining formal proofs of a theorem about stereographic projections. A formalisation using the HOL Light proof assistant is contrasted with one using Isabelle/HOL. Harrison’s technique for formalising Euclidean spaces is contrasted with an approach using Isabelle/HOL’s axiomatic type classes. However, every formal system can be outgrown, and mathematics should be formalised with a view that it will eventually migrate to a new formalism.
Several classes of permutation trinomials from Niho exponents over finite fields of characteristic 3

Permutation polynomials have important applications in cryptography, coding theory, combinatorial designs, and other areas of mathematics and engineering. Finding new classes of permutation polynomials is therefore an interesting subject of study. Permutation trinomials attract people’s interest due to their simple algebraic forms and additional extraordinary properties. In this paper, based on a seventh-degree and a fifth-degree Dickson polynomial over the finite field [Formula: see text], two conjectures on permutation trinomials over [Formula: see text] presented recently by Li–Qu–Li–Fu are partially settled, where [Formula: see text] is a positive integer.
Incorporating Privileged Information to Unsupervised Anomaly Detection

We introduce a new unsupervised anomaly detection ensemble called SPI which can harness privileged information—data available only for training examples but not for (future) test examples. Our ideas build on the Learning Using Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [17, 19], which we extend to unsupervised learning and in particular to anomaly detection. SPI (for Spotting anomalies with Privileged Information) constructs a number of frames/fragments of knowledge (i.e., density estimates) in the privileged space and transfers them to the anomaly scoring space through "imitation" functions that use only the partial information available for test examples. Our generalization of the LUPI paradigm to unsupervised anomaly detection shepherds the field in several key directions, including (i) domain-knowledge-augmented detection using expert annotations as PI, (ii) fast detection using computationally-demanding data as PI, and (iii) early detection using "historical future" data as PI. Through extensive experiments on simulated and real datasets, we show that augmenting privileged information to anomaly detection significantly improves detection performance. We also demonstrate the promise of SPI under all three settings (i–iii); with PI capturing expert knowledge, computationally-expensive features, and future data on three real world detection tasks. Code related to this paper is available at: http://www.andrew.cmu.edu/user/shubhras/SPI .
Designing and implementing algorithms for the closest string problem

Given a set of n strings of length L and a radius d, the closest string problem (CSP for short) asks for a string tsol that is within a Hamming distance of d to each of the given strings. It is known that the problem is NP-hard and its optimization version admits a polynomial time approximation scheme (PTAS). A number of parameterized algorithms have been then developed to solve the problem when d is small. Among them, the relatively new ones have not been implemented before and their performance in practice was unknown. In this study, we implement all of them by careful engineering. For those that have been implemented before, our implementation is much faster. For some of those that have not been implemented before, our experimental results show that there exist huge gaps between their theoretical and practical performances. We also design a new parameterized algorithm for the binary case of CSP. The algorithm is deterministic and runs in O(n2L+n2d⋅6.16d) time, while the previously best deterministic algorithm runs in O(nL+nd3⋅6.731d) time.
STOP, You’re on Camera: The Evidentiary Admissibility and Probative Value of Digital Records in Europe

Under the rule of law, the punishment of an accused is possible only when his or her guilt is proven before the judiciary in accordance with legal safeguards including the rules of evidence law. Digital community policing systems—mobile device policing applications and associated networks and servers—collect, transmit and store digital records relevant to violations of law, which can be used by law enforcement agencies as tips to collect further evidence and by prosecutors to prove the guilt of the accused. These digital records must meet the law requirements with regard to the admissibility and probative value of evidence: relevance, legality, authenticity and reliability. Therefore, digital community policing systems should have measures that: (1) enable the identification of the source of the digital records and the time, date and location of their creation; (2) protect the digital records from alteration, manipulation or damage during the collection, transmission or storage; (3) ensure that the digital records are what they purport to be and (4) verify whether the digital records have suffered any omissions.
Optimizing Autonomic Resources for the Management of Large Service-Based Business Processes

Cloud Computing, as a distributed computing paradigm, consists of the provisioning of infrastructure, platform, and software resources as services. This paradigm is being increasingly used for the deployment and execution of service-based business processes. To efficiently manage them according to the autonomic computing paradigm, service-based business processes can be associated with autonomic managers that monitor these processes, analyze monitoring data, plan configuration actions, and execute these actions on these processes. Although, during these last years, autonomic management of cloud services has received increasing attention, the optimization of autonomic managers to be assigned to cloud services remains not well explored. In fact, almost all the existing solutions on autonomic computing have been interested in modeling and implementing autonomic mechanisms without making any effort to optimize the number of used autonomic managers. Moreover, when it comes to large service-based business processes, optimization of management resources becomes a critical issue. To overcome this issue, we present in this paper a novel approach to determine how many autonomic managers to use for the management of large service-based business processes in order to minimize their cost while avoiding management bottlenecks. Experiments conducted on three different types of datasets highlight the effectiveness of our approach.
Capacity-Approaching Non-Binary Balanced Codes Using Auxiliary Data

It is known that, for large user word lengths, the auxiliary data can be used to recover most of the redundancy losses of Knuth's simple balancing method compared with the optimal redundancy of balanced codes for the binary case. Here, this important result is extended in a number of ways. First, an upper bound for the amount of auxiliary data is derived that is valid for all codeword lengths. This result is primarily of theoretical interest, as it defines the probability distribution of the number of balancing indices that results in optimal redundancy. This result is equally valid for particular non-binary generalizations of Knuth's balancing method. Second, an asymptotically exact expression for the amount of auxiliary data for the ternary case of a variable length realization of the modified balanced code construction is derived, that, in all respects, is the analogue of the result obtained for the binary case. The derivation is based on a generalization of the binary random walk to the ternary case and a simple modification of an existing generalization of Knuth's method for the non-binary balanced codes. Finally, a conjecture is proposed regarding the probability distribution of the number of balancing indices for any alphabet size.
A Bilinear Ranking SVM for Knowledge Based Relation Prediction and Classification

As an important and challenging problem, knowledge representation and inference are typically carried out in a knowledge embedding framework over a multi-relational knowledge graph, and thus have a wide range of applications such as semantic retrieval and question answering. In this paper, we propose a bilinear learning framework which performs cross-entity knowledge relation analysis in the continuous vector space (derived from knowledge embedding). In the framework, we effectively model the intrinsic correlations among different types of knowledge relations within a max-margin multi-relational ranking scheme, which jointly optimizes the tasks of entity embedding and cross-entity relation prediction in terms of multi-relational structures of the knowledge graph. Specifically, we devise a bilinear scoring function that aims to evaluate the confidence degree of semantic relation prediction for entity pairs through a multi-relational learning-to-rank pipeline. In essence, the pipeline formulates the problem of relation prediction for entity pairs as that of learning relation-specific ranking functions by max-margin optimization. Experimental results demonstrate the effectiveness of the proposed framework on two common benchmark datasets.
Towards incorporating honeywords in n‐session recording attack resilient unaided authentication services

Unaided authentication services provide the flexibility to login without being dependent on any external hardware. n-Session recording attack resilient unaided authentication services (n-SRRUASs) are known for setting high security standards against different client side threats. However, because of their authentication procedure, the authors have identified that these services cope poorly with handling the server side issues. Though modern days’ research heavily depends on the honeywords (or fake passwords) as a countermeasure of server side threats, they have shown that the honeywords cannot be directly applied to n-SRRUAS. The authors’ analysis shows that the idea of incorporating the honeywords directly into an n-SRRUAS is particularly difficult as it prevents the system from storing passwords after applying password-based key derivation function or in the form of a hashed string. In this study, they have proposed few generic principles for incorporating the honeywords into n-SRRUAS and show that the proposed principles are sufficient for incorporating the honeywords into any n-SRRUAS. Furthermore, with the help of an existing n-SRRUAS, they have shown that the proposed idea is truly implementable in practice to fill the existing gap.
Model Checking Based Web Service Verification: A Systematic Literature Review

Model checking is a popular formal technique facilitating automatic verification of finite-state transition systems, and it has been applied to almost all of the Web service verification aspects, such as control-flow, data-flow, interaction, time requirements, quality of service, security requirements, etc. However, no systematic literature review focused on model checking of Web services is available. Motivated by this fact, in this paper, we systematically review existing research works on model checking based Web service verification appeared during the period of 2002-2017. We present a verification goal based classification of the collected articles, and for each paper, we identify the verification technique, target Web service standard, flavor of the employed model checking technique and tool, and other supplementary techniques, if any. Further, we highlight some of the issues, gaps, key challenges in this area and propose some future directions.
Remarks on fixed point assertions in digital topology, 5

As in [6, 3, 4, 5], we discuss published assertions concerning fixed points in “digital metric spaces” - assertions that are incorrect or incorrectly proven, or reduce to triviality.
VEBO

This work proposes Vertex- and Edge-Balanced Ordering (VEBO): balance the number of edges and the number of unique destinations of those edges. VEBO balances edges and vertices for graphs with a power-law degree distribution, and ensures an equal degree distribution between partitions. Experimental evaluation on three shared-memory graph processing systems (Ligra, Polymer and GraphGrind) shows that VEBO achieves excellent load balance and improves performance by 1.09× over Ligra, 1.41× over Polymer and 1.65× over GraphGrind, compared to their respective partitioning algorithms, averaged across 8 algorithms and 7 graphs. VEBO improves GraphGrind performance with a speedup of 2.9× over Ligra on average.
Towards Accurate Georeferenced Video Search With Camera Field of View Modeling

Positioning data and other sensor measurements, such as camera orientation, have become important contextual features generated by mobile devices during video recording, which proved to be increasingly beneficial to video search. To enable access to videos based on their metadata (e.g., geo-properties produced by GPS and digital compass), a model representing camera field of view (FOV) is needed. Vector model of previous work, which simplifies FOV by ignoring the viewable angle for search efficiency, has become popular in georeferenced video search. However, when the viewable angle is large, many false positives and false negatives occur, which are undesirable for filtering of georeferenced video search. This paper proposes a new model, which can appropriately represent the actual FOV as a filtering step, without any false positive or false negative. Based on this model, we investigate how to process five types of overlap queries for searching videos as spatio-temporal objects. To verify the effectiveness of our model and the corresponding query processing algorithms, experiments on a real data set we collected and a large synthetic data set are conducted. The results show that the proposed model can perform much better compared with the existing vector model, and the accuracy of its search results remains almost steady, even when the angle changes.
Creating Dynamic Documents with R and Python as a Computational and Visualization Tool for Teaching Differential Equations

The goal of this paper is to promote the use of R and Python as high-level, free, open-source programming environments that can be used as a computational and visualization tool for teaching differential equations. Both R and Python also allow for creating reproducible dynamic documents using Markdown, which combines live code, plain text, and expressions to generate different formats (pdf, html, Word), including the numerical and graphical output from the code, along with the code itself, properly formatted with minimum effort. This technology can be used for creating interactive teaching documents, randomized exams, homework and project reports, and even publication quality papers. We illustrate this technology with several examples implemented in R and Python. This approach has been classroom-tested, with some promising results. This article has online supplemental material posted on the PRIMUS website.