Reviewerly/exHarmony huggingface dataset:
A two-step hypergraph reduction based fitting method for unbalanced data

In this paper, we propose a two-step hypergraph reduction based fitting for unbalanced data. A hypergraph effectively characterizes the relationship between model hypotheses and data points for model fitting. However, a hypergraph-based fitting method often suffers from the problem of high computational cost due to the complex relationship between hyperedges and vertices. Hypergraph reduction algorithms are used to alleviate this problem, but they cannot work well for unbalanced data. To deal with the unbalanced model fitting problem, we first locally remove hyperedges corresponding to the same model instances in data, and then globally remove hyperedges corresponding to the bad model hypotheses. Moreover, we extend the binary incident matrix of a normal hypergraph to a continuous (soft) generalization, to improve the accuracy of hypergraph partition for model fitting. Experimental results on both synthetic data and real images demonstrate that the proposed method has significant superiority over several other state-of-the-art fitting methods.
Weighted similarity and distance metric learning for unconstrained face verification with 3D frontalisation

In this study, the authors focus on the challenging problem of verifying faces captured under unconstrained conditions. Unconstrained face images often vary largely in poses, illuminations, expressions, occlusions, and ages. To address these challenges, they combine face frontalisation method with metric learning. To deal with the variations of poses, they apply an improved 3D face frontalisation method to generate the frontal view of the face images. Recent studies observed that bilinear similarity and Mahalanobis distance have a promising performance on measuring the similarity of two images. Based on these studies, they propose a weighted similarity and distance metric learning method which balances the role of bilinear similarity and Mahalanobis distance to better measure the similarity of an image pair. All the experiments are conducted based on the labelled faces in the wild database, and the experimental results show the effectiveness of their method.
MetAcsl: Specification and Verification of High-Level Properties

Modular deductive verification is a powerful technique capable to show that each function in a program satisfies its contract. However, function contracts do not provide a global view of which high-level (e.g. security-related) properties of a whole software module are actually established, making it very difficult to assess them. To address this issue, this paper proposes a new specification mechanism, called meta-properties. A meta-property can be seen as an enhanced global invariant specified for a set of functions, and capable to express predicates on values of variables, as well as memory related conditions (such as separation) and read or write access constraints. We also propose an automatic transformation technique translating meta-properties into usual contracts and assertions, that can be proved by traditional deductive verification tools. This technique has been implemented as a Frama-C plugin called MetAcsl and successfully applied to specify and prove safety- and security-related meta-properties in two illustrative case studies.
Local Geometric Deformations in the DHT Domain With Applications

Local rotation, translation, and scaling of the image domain represent a basic toolkit in adaptive image processing, such as image registration, template matching, local invariant feature detection, and super-resolution imaging, among others. In this paper, it is shown how the local rotation, scaling, and translations can be performed in the discrete Hermite transform (DHT) domain. As the DHT satisfies the generalized steerability property, basic geometric operations are expressed as linear mappings in the DHT domain and hence can facilitate the solution of many image processing problems. The local rotation and scaling were previously shown in the continuous domain using the Hermite Transform, the former is used here as a good approximation for discrete images, whereas the latter is extended to a discrete domain. In addition, the local translation operation is fully developed in the discrete domain. The application of these three operations is illustrated with three exemplar applications including: 1) mathematical morphology; 2) template matching; and 3) depth from defocus. The simple yet effective methods presented in the paper indicate that local image decompositions satisfying the steerability property, such as the DHT, are desirable for solving a number of interesting image processing problems.
An experience in automatically building lexicons for affective computing in multiple target languages

Affective Computing in text attempts to identify the emotional charge reflected in it, trying to analyse the moods transmitted while writing. There are several techniques and approaches to perform Affective Computing in texts, but lexicons are their common point. However, it is difficult to find solutions for specific languages different from English. Thus, this article presents an experience in automatically generating lexicons to perform Affective Computing following a multiple- target languages approach. The experience starts with some initial seeds of words in English that define the emotions we want to identify. It then expands them as much as possible with related words in a bootstrapping process and finally obtains a lexicon by processing the context sentences from parallel translated text where the terms have been used. We have checked the resulting lexicons by conducting an exploratory analysis of the affective fingerprint on a parallel corpus with books translated from and to different languages. The obtained results look promising, showing really similar affective fingerprints in different language translations for the same books.
Reconstructing de facto software development methods

Software development is a complex process that requires disciplined engineering approaches. Empirical studies show that companies still don?t document their development practice, or if they do, these are not up-to-date and do not reflect how they really develop software. The main objective of this paper is to propose an approach that can help companies in documenting their real development practice. Comparing to existing approaches that require substantial effort on the side of project members, our approach extracts information on development practice directly from software repositories. Five companies have been studied to identify information that can be retrieved from software repositories. Based on this, an approach to reconstruct development practice has been developed. The approach has been evaluated on a real software repository shared by an additional company. The results confirm that software repository information suffice for the reconstruction of various aspects of development process, i.e. disciplines, activities, roles, and artifacts.
Interpoint Similarity-Based Uncertainty Measure for Robust Learning

Identifying reliable information from the information ocean is a natural talent of human being, which can be hardly formalized by machines. In this paper, we show how to measure the degree of uncertainty in terms of interpoint similarity. For applications under complex uncertainty, it is desirable that we provide a systematic way for users to identify reliable information. In our approach, the similarity uncertainty for different kinds of data sets is defined according to the Shannon entropy theory. Then, similarity constrained models are designed to guarantee superior learning performance under uncertainty. Experiments using both simulation data set and several public data sets, can clearly demonstrate significant improvements of the proposed method under large uncertainty.
A constructive characterization of vertex cover Roman trees

A Roman dominating function on a graph G = (V (G), E(G)) is a function f : V (G) → {0, 1, 2} satisfying the condition that every vertex u for which f (u) = 0 is adjacent to at least one vertex v for which f (v) = 2.The Roman dominating function f is an outer-independent Roman dominating function on G if the set of vertices labeled with zero under f is an independent set.The outer-independent Roman domination number γ oiR (G) is the minimum weight w(f ) = v∈V (G) f (v) of any outer-independent Roman dominating function f of G.A vertex cover of a graph G is a set of vertices that covers all the edges of G.The minimum cardinality of a vertex cover is denoted by α(G).A graph G is a vertex cover Roman graph
Mobile Visual Search Compression With Grassmann Manifold Embedding

With the increasing popularity of mobile phones and tablets, the explosive growth of query-by-capture applications calls for a compact representation of the query image feature. Compact descriptors for visual search (CDVS) is a recently released standard from the ISO/IEC moving pictures experts group, which achieves state-of-the-art performance in the context of image retrieval applications. However, they did not consider the matching characteristics in local space in a large-scale database, which might deteriorate the performance. In this paper, we propose a more compact representation with scale invariant feature transform (SIFT) descriptors for the visual query based on Grassmann manifold. Due to the drastic variations in image content, it is not sufficient to capture all the information using a single transform. To achieve more efficient representations, a SIFT manifold partition tree (SMPT) is initially constructed to divide the large dataset into small groups at multiple scales, which aims at capturing more discriminative information. Grassmann manifold is then applied to prune the SMPT and search for the most distinctive transforms. The experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the standard benchmark CDVS dataset.
Wi-Fi password stealing program using USB rubber ducky

A minute is all it takes for a hacker to gain informations from your computer, such as Wi-Fi password. Due to the limited capability of people to remember a lot of complex and unique password, people tend to use the same password for most of their account. This paper aimed to implement Wi-Fi password stealing program in USB Rubber Ducky using USB Rubber Ducky Scripting, Visual Basic Script, Web Server, Command Prompt, and Ducky Toolkit to obtain clear text Wi-Fi password that ever connected to the computer. In the testing phase, the success rate of Wi-Fi password stealing program reached 94.28% with 87.87% obtained personal password is still categorized as guessable password and the password reuse rate reached 81.81%. Thus, Wi-Fi password stealing program can be very dangerous as most of the personal password was used in lots of account and still categorized as guessable.
Relative error in out-of-plane measurement due to the object illumination

In this work the sensitivity vector is analyzed for collimated and divergent oblique illumination for an out-of-plane arrangement. In the case of collimated illumination, the variation of the sensitivity vector components was considered and a geometric model was proposed for its evaluation. The geometry of the optical setup used allowed us to find sensitivity mostly along the pulling direction; the other two components of the sensitivity vector were relatively small. We measured the displacement induced only along the pulling direction. Errors in the displacement measurement associated with divergent and collimated illumination can be predicted when the sensitivity vector is considered constant. Experimental and theoretical results are presented for the out-of-plane electronic speckle pattern interferometer. The analyzed object was an aluminum plate.
Plan-Length Bounds: Beyond 1-Way Dependency

We consider the problem of compositionally computing upper bounds on lengths of plans. Following existing work, our approach is based on a decomposition of state-variable dependency graphs (a.k.a. causal graphs). Tight bounds have been demonstrated previously for problems where key dependencies flow in a single direction—i.e. manipulating variable v1 can disturb the ability to manipulate v2 and not vice versa. We develop a more general bounding approach which allows us to compute useful bounds where dependency flows in both directions. Our approach is practically most useful when combined with earlier approaches, where the computed bounds are substantially improved in a relatively broad variety of problems. When combined with an existing planning procedure, the improved bounds yield coverage improvements for both solvable and unsolvable planning problems.
Satisfiability in Strategy Logic Can Be Easier than Model Checking

In the design of complex systems, model-checking and satisfiability arise as two prominent decision problems. While model-checking requires the designed system to be provided in advance, satisfiability allows to check if such a system even exists. With very few exceptions, the second problem turns out to be harder than the first one from a complexity-theoretic standpoint. In this paper, we investigate the connection between the two problems for a non-trivial fragment of Strategy Logic (SL, for short). SL extends LTL with first-order quantifications over strategies, thus allowing to explicitly reason about the strategic abilities of agents in a multi-agent system. Satisfiability for the full logic is known to be highly undecidable, while model-checking is non-elementary.The SL fragment we consider is obtained by preventing strategic quantifications within the scope of temporal operators. The resulting logic is quite powerful, still allowing to express important game-theoretic properties of multi-agent systems, such as existence of Nash and immune equilibria, as well as to formalize the rational synthesis problem. We show that satisfiability for such a fragment is PSPACE-COMPLETE, while its model-checking complexity is 2EXPTIME-HARD. The result is obtained by means of an elegant encoding of the problem into the satisfiability of conjunctive-binding first-order logic, a recently discovered decidable fragment of first-order logic.
Lifted Hinge-Loss Markov Random Fields

Statistical relational learning models are powerful tools that combine ideas from first-order logic with probabilistic graphical models to represent complex dependencies. Despite their success in encoding large problems with a compact set of weighted rules, performing inference over these models is often challenging. In this paper, we show how to effectively combine two powerful ideas for scaling inference for large graphical models. The first idea, lifted inference, is a wellstudied approach to speeding up inference in graphical models by exploiting symmetries in the underlying problem. The second idea is to frame Maximum a posteriori (MAP) inference as a convex optimization problem and use alternating direction method of multipliers (ADMM) to solve the problem in parallel. A well-studied relaxation to the combinatorial optimization problem defined for logical Markov random fields gives rise to a hinge-loss Markov random field (HLMRF) for which MAP inference is a convex optimization problem. We show how the formalism introduced for coloring weighted bipartite graphs using a color refinement algorithm can be integrated with the ADMM optimization technique to take advantage of the sparse dependency structures of HLMRFs. Our proposed approach, lifted hinge-loss Markov random fields (LHL-MRFs), preserves the structure of the original problem after lifting and solves lifted inference as distributed convex optimization with ADMM. In our empirical evaluation on real-world problems, we observe up to a three times speed up in inference over HL-MRFs.
On Lifted Inference Using Neural Embeddings

We present a dense representation for Markov Logic Networks (MLNs) called Obj2Vec that encodes symmetries in the MLN structure. Identifying symmetries is a key challenge for lifted inference algorithms and we leverage advances in neural networks to learn symmetries which are hard to specify using hand-crafted features. Specifically, we learn an embedding for MLN objects that predicts the context of an object, i.e., objects that appear along with it in formulas of the MLN, since common contexts indicate symmetry in the distribution. Importantly, our formulation leverages well-known skip-gram models that allow us to learn the embedding efficiently. Finally, to reduce the size of the ground MLN, we sample objects based on their learned embeddings. We integrate Obj2Vec with several inference algorithms, and show the scalability and accuracy of our approach compared to other state-of-the-art methods.
Marginal Inference in Continuous Markov Random Fields Using Mixtures

Exact marginal inference in continuous graphical models is computationally challenging outside of a few special cases. Existing work on approximate inference has focused on approximately computing the messages as part of the loopy belief propagation algorithm either via sampling methods or moment matching relaxations. In this work, we present an alternative family of approximations that, instead of approximating the messages, approximates the beliefs in the continuous Bethe free energy using mixture distributions. We show that these types of approximations can be combined with numerical quadrature to yield algorithms with both theoretical guarantees on the quality of the approximation and significantly better practical performance in a variety of applications that are challenging for current state-of-the-art methods.
Universally defining finitely generated subrings of global fields

It is shown that any finitely generated subring of a global field has a universal first-order definition in its fraction field. This covers Koenigsmann's result for the ring of integers and its subsequent extensions to rings of integers in number fields and rings of $S$-integers in global function fields of odd characteristic. In this article a proof is presented which is uniform in all global fields, including the characteristic two case, where the result is entirely novel. Furthermore, the proposed method results in universal formulae requiring significantly fewer quantifiers than the formulae that can be derived through the previous approaches.
Analisis Bentuk Pola Suara Menggunakan Ekstraksi Ciri Mel-Frequencey Cepstral Coefficients (MFCC)

Sound is a part of the human body that is unique and can be distinguished, so its application can be used in sound pattern recognition technology, one of which is used for sound biometrics. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study uses the Melf-Frequency Cepstrum Coefficients (MFCC) method for feature extraction process from speaker speech signals. The MFCC process will convert the sound signal into several feature vectors which will then be displayed in graphical form. Analysis and design of sound patterns using Matlab 2017a software. Tests were carried out on 5 users consisting of 3 men and 2 women, each user said 1 predetermined "LOGIN" word, which for 15 words said. The results of the test are the form of a sound pattern between the characteristics of 1 user with other users. Keywords—Voice, Pattern, Feature Extraction, MFCC
Word Embedding as Maximum A Posteriori Estimation

The GloVe word embedding model relies on solving a global optimization problem, which can be reformulated as a maximum likelihood estimation problem. In this paper, we propose to generalize this approach to word embedding by considering parametrized variants of the GloVe model and incorporating priors on these parameters. To demonstrate the usefulness of this approach, we consider a word embedding model in which each context word is associated with a corresponding variance, intuitively encoding how informative it is. Using our framework, we can then learn these variances together with the resulting word vectors in a unified way. We experimentally show that the resulting word embedding models outperform GloVe, as well as many popular alternatives.
Distinguishing between statistical and systematic errors in quantum process tomography

It is generally assumed that every process in quantum physics can be described mathematically by a completely positive map. However, experimentally reconstructed processes are not necessarily completely positive due to statistical or systematic errors. In this paper, we introduce a test for discriminating statistical from systematic errors which is necessary to interpret experimentally reconstructed, non-completely positive maps. We demonstrate the significance of the test using several examples given by experiments and simulations. In particular, we demonstrate experimentally how an initial correlation between the system to be measured and its environment leads to an experimentally reconstructed map with negative eigenvalues. These experiments are carried out using atomic 171Yb+ ions confined in a linear Paul trap, addressed and coherently manipulated by radio frequency radiation.
Exploring Answer Stance Detection with Recurrent Conditional Attention

Detecting stance from certain types of question-answer pairs is an interesting problem which has not been carefully explored. Unlike previous stance detection tasks, targets here are not given entities or claims but entire questions, which makes it difficult to capture the semantics of targets and build target-dependent representations of answers. To address them, we introduce the Recurrent Conditional Attention (RCA) model which incorporates a conditional attention structure into the recurrent reading process. RCA iteratively guides the distillation of question semantic with answer information and collects stance-oriented text relating to question, further revealing mutual relationship among stance, answer and question. Experiments on a manually labeled Chinese community QA stance dataset show that RCA outperforms four strong baselines by average 2.90% on macro-F1 and 2.66% on micro-F1 respectively.
On the Hardness of Probabilistic Inference Relaxations

A promising approach to probabilistic inference that has attracted recent attention exploits its reduction to a set of model counting queries. Since probabilistic inference and model counting are #P-hard, various relaxations are used in practice, with the hope that these relaxations allow efficient computation while also providing rigorous approximation guarantees.&#x0D; In this paper, we show that contrary to common belief, several relaxations used for model counting and its applications (including probablistic inference) do not really lead to computational efficiency in a complexity theoretic sense. Our arguments proceed by showing the corresponding relaxed notions of counting to be computationally hard. We argue that approximate counting with multiplicative tolerance and probabilistic guarantees of correctness is the only class of relaxations that provably simplifies the problem, given access to an NP-oracle. Finally, we show that for applications that compare probability estimates with a threshold, a new notion of relaxation with gaps between low and high thresholds can be used. This new relaxation allows efficient decision making in practice, given access to an NP-oracle, while also bounding the approximation error.&#x0D; Erratum: This research is supported in part by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: [AISG-RP-2018-005])