Reviewerly/exHarmony huggingface dataset:
Harder–Narasimhan theory for linear codes (with an appendix on Riemann–Roch theory)

In this text we develop some aspects of Harder–Narasimhan theory, slopes, semistability and canonical filtration, in the setting of combinatorial lattices. Of noticeable importance is the Harder–Narasimhan structure associated to a Galois connection between two lattices. It applies, in particular, to matroids. We then specialize this to linear codes. This could be done from at least three different approaches: using the sphere-packing analogy, or the geometric view, or the Galois connection construction just introduced. A remarkable fact is that these all lead to the same notion of semistability and canonical filtration. Relations to previous propositions toward a classification of codes, and to Wei's generalized Hamming weight hierarchy, are also discussed. Last, we study the two important questions of the preservation of semistability (or more generally the behavior of slopes) under duality, and under tensor product. The former essentially follows from Wei's duality theorem for higher weights—and its matroid version—which we revisit in an appendix, developing analogues of the Riemann–Roch, Serre duality, Clifford, and gap and gonality sequence theorems. Likewise the latter is closely related to the bound on higher weights of a tensor product, conjectured by Wei and Yang, and proved by Schaathun in the geometric language, which we reformulate directly in terms of codes. From this material we then derive semistability of tensor product.
Block Stability for MAP Inference

Recent work (Lang et al., 2018) has shown that some popular approximate MAP inference algorithms perform very well when the input instance is stable. The simplest stability condition assumes that the MAP solution does not change at all when some of the pairwise potentials are adversarially perturbed. Unfortunately, this strong condition does not seem to hold in practice. We introduce a significantly more relaxed condition that only requires portions of an input instance to be stable. Under this block stability condition, we prove that the pairwise LP relaxation is persistent on the stable blocks. We complement our theoretical results with an evaluation of real-world examples from computer vision, and we find that these instances have large stable regions.
Mining Maximal Induced Bicliques using Odd Cycle Transversals

Many common graph data mining tasks take the form of identifying dense subgraphs (e.g. clustering, clique-finding, etc). In biological applications, the natural model for these dense substructures is often a complete bipartite graph (biclique), and the problem requires enumerating all maximal bicliques (instead of identifying just the largest or densest). The best known algorithm in general graphs is due to Dias et al., and runs in time O(M|V|4), where M is the number of maximal induced bicliques (MIBs) in the graph. When the graph being searched is itself bipartite, Zhang et al. give a faster algorithm where the time per MIB depends on the number of edges in the graph. In this work, we present a new algorithm for enumerating MIBs in general graphs, whose run time depends on how “close to bipartite” the input is. Specifically, the runtime is parameterized by the size k of an odd cycle transversal (OCT), a vertex set whose deletion results in a bipartite graph. Our algorithm runs in time O(M|V‖E|k23k/3), which is an improvement on Dias et al. whenever k ≤ 3 log3 |V|. We implement our algorithm alongside a variant of Dias et al.'s in open-source C++ code, and experimentally verify that the OCT-based approach is faster in practice on graphs with a wide variety of sizes, densities, and OCT decompositions.MSC codesbicliquesodd cycle transversalparameterized algorithmsenumerationbipartite
A Holistic View on Organizational IT Security: The Influence of Contextual Aspects During IT Security Decisions

Decisions regarding organizational IT security are often approximated by models drawing on normative statistical decision theories even though several IS researchers and studies in cognate disciplines have argued for the importance of contextual aspects.Based on findings in organizational and behavioral science and 25 expert interviews, this paper proposes a framework, postulating that IT security (investment) decisions are largely influenced by such contextual aspects: organizational, environmental, economic, and not least of all by cognitive and behavioral aspects of decision-makers.Subsequently, we review organizational IT security literature building on Straub and Welke's Security Risk Planning Model and the previously postulated conceptual framework.This critical literature review highlights the scarcity of studies analyzing IT security decision-making from a behavioral, environmental, and organizational perspective and thus argues for the importance and future consideration of contextual aspects regarding IT security decisions.
Local Turn-Boundedness: A Curvature Control for a Good Digitization

This paper focuses on the classical problem of the control of information loss during the digitization step. The properties proposed in the literature rely on smoothness hypotheses that are not verified by the curves including angular points. The notion of turn introduced by Milnor in the article On the Total Curvature of Knots generalizes the notion of integral curvature to continuous curves. Thanks to the turn, we are able to define the local turn-boundedness. This promising property of curves do not require smoothness hypotheses and shares several properties with the par(r)-regularity, in particular well-composed digitizations. Besides, the local turn-boundedness enables to constraint spatially the continuous curve in function of its digitization.
A Computational Approach to Authorship Verification of Johann Wolfgang Goethe’s Contributions to the &lt;i&gt;Frankfurter gelehrte Anzeigen&lt;/i&gt; (1772–73)

The article proposes a computational stylometric authorship verification approach to contribute to the solution of the longstanding philological problem concerning Johann Wolfgang Goethe's (1749-1832) anonymous contributions to the journal Frankfurter gelehrte Anzeigen of 1772/73 (FgA).
Analyzing Emergent Users’ Text Messages Data and Exploring Its Benefits

While users in the developed world can choose to adopt the technology that suits their needs, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emergent</i> users cannot afford this luxury, and hence, they adapt themselves to the technology that is readily available. When technology is designed, such as the mobile-phone technology, it is an implicit assumption that it would be adopted by the emergent users in due course. However, such user groups have different needs, and they follow different usage patterns as compared to users from the developed world. In this paper, we target an emergent user base, i.e., users from a university in Pakistan, and analyze their texting behavior on mobile phones. We see interesting results, such as the long-term linguistic adaptation of users in the absence of reasonable Urdu keyboards, the overt preference for communicating in Roman Urdu, and the social forces related to textual interaction. We also present two case studies on how a single dataset can effectively help understand emergent users, improve usability of some tasks, and also help users perform previously difficult tasks with ease.
Few matches or almost periodicity: faster pattern matching with mismatches in compressed texts

A fundamental problem on strings in the realm of approximate string matching is pattern matching with mismatches: Given a text t, a pattern p, and a number k, determine whether some substring of t has Hamming distance at most k to p; such a substring is called a k-match.As real-world texts often come in compressed form, we study the case of searching for a small pattern p in a text t that is compressed by a straight-line program. This grammar compression is popular in the string community, since it is mathematically elegant and unifies many practically relevant compression schemes such as the Lempel-Ziv family, dictionary methods, and others. We denote by m the length of p and by n the compressed size of t. While exact pattern matching, that is, the case k = 0, is known to be solvable in near-linear time O (n + m) [Jez TALG'15], despite considerable interest in the string community, the fastest known algorithm for pattern matching with mismatches runs in time [MATH HERE] [Gawrychowski, Straszak ISAAC'13], which is far from linear even for very small k.In this paper, we obtain an algorithm for pattern matching with mismatches running in time O((n + m) poly(k)). This is near-linear in the input size for any constant (or slightly superconstant) k. We obtain analogous running time for counting and enumerating all k-matches.Our algorithm is based on a new structural insight for approximate pattern matching, essentially showing that either the number of k-matches is very small or both text and pattern must be almost periodic. While intuitive and simple for exact matches, such a characterization is surprising when allowing k mismatches.
An effective general-purpose NR-IQA model using natural scene statistics (NSS) of the luminance relative order

Blind/no-reference image quality assessment (NR-IQA) aims to assess the quality of an image without any reference image. In this paper, we propose an effective and efficient general-purpose NR-IQA model using natural scene statistics (NSS) of the luminance relative order, based on the observation that the variation of the marginal distribution of the relative order coefficients effectively reflect the degree of warping caused by different types of image distortions. In the literature, gradient-relevant methods have had a big success in full-reference (FR) IQA and reduced-reference (RR) IQA. Inspired by these, we extend it to NR-IQA in this paper. Notice that the NSS-based models usually extract their features derived from the spatial, wavelet, DCT and spectral domain etc. Unlike these metrics, the proposed method firstly extracts 32 natural scene statistics features of the luminance relative order, obtained from the log histograms of log horizontal, vertical, main-diagonal and secondary-diagonal derivatives, along with kurtosis, variance, differential entropy and entropy at two scales. Then a mapping is learned to predict the quality score using a support vector regression. The experimental results on several benchmark databases showed that the proposed method is comparable with the state-of-the-art methods and has a relatively low complexity.
Structural evolution of two ore-bearing Palaeoproterozoic metasupracrustal belts in the Kiruna area, Northwestern Fennoscandian Shield

In this project, two key study areas in the northwestern Fennoscandian Shield are under investigation. The “Western supracrustal belt” and “Central Kiruna area” are both located along lithotectonic ...
Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling

State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still construct search trees of considerable size, which could limit the performance due to restricted memory resources. In this paper, we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory bounded approach to openloop planning in large POMDPs, which optimizes a fixed size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four large benchmark problems and compare its performance with different tree-based approaches. We show that POSTS achieves competitive performance compared to tree-based open-loop planning and offers a performancememory tradeoff, making it suitable for partially observable planning with highly restricted computational and memory resources.
Validation of Growing Knowledge Graphs by Abductive Text Evidences

This paper proposes a validation mechanism for newly added triples in a growing knowledge graph. Given a logical theory, a knowledge graph, a text corpus, and a new triple to be validated, this mechanism computes a sorted list of explanations for the new triple to facilitate the validation of it, where an explanation, called an abductive text evidence, is a set of pairs of the form (triple, window) where appending the set of triples on the left to the knowledge graph enforces entailment of the new triple under the logical theory, while every sentence window on the right which is contained in the text corpus explains to some degree why the triple on the left is true. From the angle of practice, a special class of abductive text evidences called TEP-based abductive text evidence is proposed, which is constructed from explanation patterns seen before in the knowledge graph. Accordingly, a method for computing the complete set of TEP-based abductive text evidences is proposed. Moreover, a method for sorting abductive text evidences based on distantly supervised learning is proposed. To evaluate the proposed validation mechanism, four knowledge graphs with logical theories are constructed from the four great classical masterpieces of Chinese literature. Experimental results on these datasets demonstrate the efficiency and effectiveness of the proposed mechanism.
Smooth Deep Image Generator from Noises

Generative Adversarial Networks (GANs) have demonstrated a strong ability to fit complex distributions since they were presented, especially in the field of generating natural images. Linear interpolation in the noise space produces a continuously changing in the image space, which is an impressive property of GANs. However, there is no special consideration on this property in the objective function of GANs or its derived models. This paper analyzes the perturbation on the input of the generator and its influence on the generated images. A smooth generator is then developed by investigating the tolerable input perturbation. We further integrate this smooth generator with a gradient penalized discriminator, and design smooth GAN that generates stable and high-quality images. Experiments on real-world image datasets demonstrate the necessity of studying smooth generator and the effectiveness of the proposed algorithm.
Studying the impact of the Full-Network embedding on multimodal pipelines

The current state of the art for image annotation and image retrieval tasks is obtained through deep neural network multimodal pipelines, which combine an image representation and a text representation into a shared embedding space. In this paper we evaluate the impact of using the Full-Network emb edding (FNE) in this setting, replacing the original image representation in four competitive multimodal embedding generation schemes. Unlike the one-layer image embeddings typically used by most approaches, the Full-Network embedding provides a multi-scale discrete representation of images, which results in richer characterisations. Extensive testing is performed on three different datasets comparing the performance of the studied variants and the impact of the FNE on a levelled playground, i.e., under equality of data used, source CNN models and hyper-parameter tuning. The results obtained indicate that the Full-Network embedding is consistently superior to the one-layer embedding. Furthermore, its impact on performance is superior to the improvement stemming from the other variants studied. These results motivate the integration of the Full-Network embedding on any multimodal embedding generation scheme.
Evaluating Password Behavior at a Small University

No matter how sophisticated an organization's security system is, it remains vulnerable due to the human factor. In this study, we surveyed and analyzed the patterns practiced by users when generating passwords at a small-sized university. We found that users are not as aware of security requirements and practices as they think. Moreover, the vast majority of users' passwords are breakable within days or shorter. Interestingly, we found that the use of numbers and uppercase letters is prevalent among users. However, numbers are mostly used at the end of the passwords and uppercase letters are mostly used at the beginning of passwords. The existence of such trends makes it easier for attackers to generate more effective dictionaries. Based on the analysis in this study, we make recommendations to the IT department to improve the password policy. Additionally, we provide recommendations to the faculty, staff, and students on how to strengthen their passwords.
Preliminary study on Sina (Sina Intraoperative Neurosurgical Assist) APP assisted localization of supratentorial lesions by smart phone

This article is aimed at investigating the use of smart phone software Sina in the localization of supratentorial lesions, finding the error between Sina method and neuronavigation, and identifying the reliability of the new method. Neuronavigation and Sina measurement were used to locate the lesion in the patients whose lesion lengths are between 2 cm and 6 cm. The reference point was the center of the lesion. We found the error of Sina method is 13.6 ± 0.55 mm comparing with neuronavigation, so the Sina method still cannot replace the localization method of the neuronavigation system. However, in practical clinical work, with the help of the new method, the lesion can be located more precisely and easily.
Does Really Economic Freedom Matter for Growth in South Asia? Empirical Evidences from Pre-Economic Crises and Post-Economic Crises Period

The discussion about the relationship between economic freedom and economic growth is not new and has always been extensively discussed in economic literature. But the question in this area is the consideration of the effects of global financial crises of 2008. This study attempts to answer the question: How does economic crises effect freedom-growth nexus? This study analyses the relationship between economic freedom index (measured by Fraser Institute), individual components of economic freedom and GDP per capita growth of 5 South Asian countries over the period of 1990-2015. Fixed effects regression results reveals that GDP per capita growth is positively affected by economic freedom index and this relationship has weakened by the global economic crises of 2008. It does not mean that increasing economic freedom is good for economic growth since one of the components of economic freedom has negative effect on growth.
Accurate depth estimation of skin surface using a light‐field camera toward dynamic haptic palpation

Abstract Background Haptic skin palpation with three‐dimensional skin surface reconstruction from in vivo skin images in order to acquire both tactile and visual information has been receiving much attention. However, the depth estimation of skin surface, using a light field camera that creates multiple images with a micro‐lens array, is a difficult problem due to low‐resolution images resulting in erroneous disparity matching. Methods Multiple low‐resolution images decoded from a light field camera have limitations to accurate 3D surface reconstruction needed for haptic palpation. To overcome this, a deep learning method, Generative Adversarial Networks, was employed to generate super‐resolved skin images that preserve surface detail without blurring, and then, accurate skin depth was estimated by taking multiple subsequent steps including lens distortion correction, sub‐pixel shifted image generation using phase shift theorem, cost‐volume building, multi‐label optimization, and hole filling and refinement, which is a new approach for 3D skin surface reconstruction. Results Experimental results of the deep‐learning‐based super‐resolution method demonstrated that the textural detail (wrinkles) of super‐resolved skin images is well preserved, unlike other super‐resolution methods. In addition, the depth maps computed with our proposed algorithm verify that our method can produce more accurate and robust results compared to other state‐of‐the‐art depth map computation methods. Conclusion Herein, we first proposed depth map estimation of skin surfaces using a light field camera and subsequently tested it with several skin images. The experimental results established the superiority of the proposed scheme.
An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks

With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users’ daily lives are facilitated by the applications of LBS, but users’ privacy leaking hinders the further development of LBS. In order to solve this problem, techniques such as k-anonymity and l-diversity have been widely adopted. However, most papers that combine with k-anonymity and l-diversity focus on the security of users’ privacy with little consideration of service efficiency. In this paper, we firstly treat the relationship between k-anonymity and l-diversity in the clustering process from a dynamic and global perspective. Then a service category table based algorithm (SCTB) is designed to identify and calculate l-diversity securely and efficiently, which promotes the cooperative efficiency of users in LBS query, especially when the preference privacy that users request in the clustering process have similarities. Finally, theoretical performance analysis and extensive experimental studies are performed to validate the effectiveness of our SCTB algorithm.
An automatic restoration framework based on GPU-accelerated collateral filtering in brain MR images

Image restoration is one of the fundamental and essential tasks within image processing. In medical imaging, developing an effective algorithm that can automatically remove random noise in brain magnetic resonance (MR) images is challenging. The collateral filter has been shown a more powerful algorithm than many existing methods. However, the computation of the collateral filter is more time-consuming and the selection of the filter parameters is also laborious. This paper proposes an automatic noise removal system based on the accelerated collateral filter for brain MR images. To solve these problems, we first accelerated the collateral filter with parallel computing using the graphics processing unit (GPU) architecture. We adopted the compute unified device architecture (CUDA), an application programming interface for the GPU by NVIDIA, to hasten the computation. Subsequently, the optimal filter parameters were selected and the automation was achieved by artificial neural networks. Specifically, an artificial neural network system associated with image feature analysis was adopted to establish the automatic image restoration framework. The best feature combination was selected by the paired t-test and the sequential forward floating selection (SFFS) methods. Experimental results indicated that not only did the proposed automatic image restoration algorithm perform dramatically faster than the traditional collateral filter, but it also effectively removed the noise in a wide variety of brain MR images. A speed up gain of 34 was attained to process an MR image, which completed within 0.1 s. Representative illustrations of brain tumor images demonstrated the capability of identifying lesion boundaries, which outperformed many existing methods. We believe that our accelerated and automated restoration framework is promising for achieving robust filtering in many brain MR image restoration applications.