Reviewerly/exHarmony huggingface dataset:
Highway Driving Dataset for Semantic Video Segmentation

Scene understanding is an essential technique in semantic segmentation. Although there exist several datasets that can be used for semantic segmentation, they are mainly focused on semantic image segmentation with large deep neural networks. Therefore, these networks are not useful for real time applications, especially in autonomous driving systems. In order to solve this problem, we make two contributions to semantic segmentation task. The first contribution is that we introduce the semantic video dataset, the Highway Driving dataset, which is a densely annotated benchmark for a semantic video segmentation task. The Highway Driving dataset consists of 20 video sequences having a 30Hz frame rate, and every frame is densely annotated. Secondly, we propose a baseline algorithm that utilizes a temporal correlation. Together with our attempt to analyze the temporal correlation, we expect the Highway Driving dataset to encourage research on semantic video segmentation.
Scattering quantum random walks on square grids and randomly generated mazes

The scattering-quantum-random-walk scheme has found success as a basis for search algorithms on highly symmetric graph structures. In this paper we examine its effectiveness at locating a specially marked vertex on square grid graphs, consisting of ${N}^{2}$ nodes. We simulate these quantum systems using classical computational methods and find that the probability distributions that arise are very favorable for a hybrid quantum-classical algorithm. We then examine how this hybrid algorithm handles varying types of randomness in both location of the special vertex and later random obstacles placed throughout the geometry, showing that the algorithm is resilient to both cases.
A proof-theoretic approach to scope ambiguity in compositional vector space models

We investigate the extent to which compositional vector space models can be used to account for scope ambiguity in quantified sentences (of the form Every man loves some woman). Such sentences introduce one reading for each quantifier. We combine a polarised focussed sequent calculus for the non-associative Lambek Calculus NL as described in Moortgat and Moot (2011) with a non-compositional ap- proach to quantifier scope in vector space semantics introduced by Hedges and Sadrzadeh (2016) and (Sadrzadeh 2016). In particular, we obtain an automatic and compositional procedure for obtaining quantifier scope ambiguity in vector space models of meaning.
Importance of Soft Tactility on Robot-Assisted Therapy

Robot-assisted therapy has gained attention as one of mental health measures. In this paper, we investigated the influences of soft tactility on psychological and physiological stress relief effects to verify whether soft robots or hard robots are effective as therapy robots. Resultantly, we confirmed that psychological stress relief effects obtained by interaction with soft robots are significantly higher than that with hard robots, particularly reducing tension and depression, improving vigor. And we also verified that physiological stress relief effects, especially increasing alpha waves, obtained by interaction with soft robots are significantly higher, too. In addition, we found that soft tactility may have effects of inhibiting reduction users' motivation for interaction. These results suggest that soft robots are more effective as therapy robots.
A physical perspective on classical cloning

The celebrated quantum no-cloning theorem states that an arbitrary quantum state cannot be cloned perfectly. This raises questions about cloning of classical states, which have also attracted attention. Here, we present a physical approach to the classical cloning process showing how cloning can be realised using Hamiltonians. After writing down a canonical transformation that clones classical states, we show how this can be implemented by Hamiltonian evolution. We then propose an experiment using the tools of nonlinear optics to realise the ideas presented here. Finally, to understand the cloning process in a more realistic context, we introduce statistical mechanical noise to the system and study how this affects the cloning process. While most of our work deals with linear systems and harmonic oscillators, we give some examples of cloning maps on manifolds and show that any system whose configuration space is a group manifold admits a cloning canonical transformation.
A Fast and Efficient Two-Dimensional Chien Search Algorithm and Design Architecture

We develop an efficient procedure for finding the roots of a bi-variate polynomial over GF(q) by extending the Chien search procedure to two-dimensions. The complexity of the Chien search is further reduced to an order of the number of conjugacy classes over GF(q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sup> ) leading to a significant reduction in the computational complexity. We also provide an efficient design architecture for our algorithm towards a circuit realization that produces the roots in `1' clock cycle.
Spectacle problem removal from facial images based on detail preserving filtering schemes

Accurate detection and localization of eye features under spectacles - is quite a relevant but challenging problem in the application field of Human-Computer Interactive (HCI) systems. The ill-effects caused by the usage of spectacles like occlusion, glare and secondary reflection formation are te rmed as “The Spectacle Problem.” In this paper, the authors alleviate the spectacle problem by employing a two-image based data fusion approach. Detail-preserving filters like Joint Bilateral Filter (JBF) and Guided Image Filter (GIF) are compared individually, to find out the suitability and consistency of the filters in the proposed data fusion approach. Experimentation on CASIA NIR-VIS 2.0 and self-generated facial database demonstrate that GIF based filtering approach has higher local and global eye feature preservation capability while mitigating the spectacle problem.
An Efficient Tool for Searching Maximal and Super Maximal Repeats in Large DNA/Protein Sequences via Induced-Enhanced Suffix Array

Background: DNA and Protein sequences of an organism contain a variety of repeated structures of various types. These repeated structures play an important role in Molecular biology as they are related to genetic backgrounds of inherited diseases. They also serve as a marker for DNA mapping and DNA fingerprinting. Efficient searching of maximal and super maximal repeats in DNA/Protein sequences can lead to many other applications in the area of genomics. Moreover, these repeats can also be used for identification of critical diseases by finding the similarity between frequency distributions of repeats in viruses and genomes (without using alignment algorithms). Objective: The study aims to develop an efficient tool for searching maximal and super maximal repeats in large DNA/Protein sequences. Methods: The proposed tool uses a newly introduced data structure Induced Enhanced Suffix Array (IESA). IESA is an extension of enhanced suffix array. It uses induced suffix array instead of classical suffix array. IESA consists of Induced Suffix Array (ISA) and an additional array-Longest Common Prefix (LCP) array. ISA is an array of all sorted suffixes of the input sequence while LCP array stores the lengths of the longest common prefixes between all pairs of consecutive suffixes in an induced suffix array. IESA is known to be efficient w.r.t. both time and space. It facilitates the use of secondary memory for constructing the large suffix-array. Results: An open source standalone tool named MSR-IESA for searching maximal and super maximal repeats in DNA/Protein sequences is provided at https://github.com/sanjeevalg/MSRIESA. Experimental results show that the proposed algorithm outperforms other state of the art works w.r.t. to both time and space. Conclusion: The proposed tool MSR-IESA is remarkably efficient for the analysis of DNA/Protein sequences, having maximal and super maximal repeats of any length. It can be used for identification of well-known diseases.
On the effect of fractional statistics on quantum ion acoustic waves

In this paper, I study the effect of a small deviation from the Fermi–Dirac statistics on the quantum ion acoustic waves. For this purpose, a quantum hydrodynamic model is developed based on the Polychronakos statistics, which allows for a smooth interpolation between the Fermi and Bose limits, passing through the case of classical particles. The model includes the effect of pressure as well as quantum diffraction effects through the Bohm potential. The equation of state for electrons obeying fractional statistics is obtained and the effect of fractional statistics on the kinetic energy and the coupling parameter is analyzed. Through the model, the effect of fractional statistics on the quantum ion acoustic waves is highlighted, exploring both linear and weakly nonlinear regimes. It is found that fractional statistics enhance the amplitude and diminish the width of the quantum ion acoustic waves. Furthermore, it is shown that a small deviation from the Fermi–Dirac statistics can modify the type structures, from bright to dark soliton. All known results of fully degenerate and non-degenerate cases are reproduced in the proper limits.
Enhanced prediction of vulnerable Web components using Stochastic Gradient Boosting Trees

Purpose Effective and efficient software security inspection is crucial as the existence of vulnerabilities represents severe risks to software users. The purpose of this paper is to empirically evaluate the potential application of Stochastic Gradient Boosting Trees (SGBT) as a novel model for enhanced prediction of vulnerable Web components compared to common, popular and recent machine learning models. Design/methodology/approach An empirical study was conducted where the SGBT and 16 other prediction models have been trained, optimized and cross validated using vulnerability data sets from multiple versions of two open-source Web applications written in PHP. The prediction performance of these models have been evaluated and compared based on accuracy, precision, recall and F-measure. Findings The results indicate that the SGBT models offer improved prediction over the other 16 models and thus are more effective and reliable in predicting vulnerable Web components. Originality/value This paper proposed a novel application of SGBT for enhanced prediction of vulnerable Web components and showed its effectiveness.
Wavelet based simulation of thermal regenerator

Abstract Thermal regenerators are the heat exchanging devices used for transferring heat from hot fluid to cold fluid via porous matrix of solid material. The transfer of heat energy in the thermal regenerator can be expressed by coupled partial differential equations (solid phase and fluid phase). In the present work, the governing equations for thermal regenerators are solved and compared by the finite difference method and wavelet optimized finite difference method. Validation of previous experimental results has also been done by using the present simulation. A methodology based on Sparse Point Representation (SPR) is used to adapt the effective grid pattern using wavelet transformation. Temperature profiles along the bed length are shown for the heating process by both the methods. The modified grid patterns based upon SPR, at different time levels are represented with the help of appropriate figures, and it is observed that the use of wavelet transformation in numerical solution increases the efficiency of the method by reducing CPU time.
Set Matching with applications: An enhancement of the Hales–Jewett pairing strategy

When solving k-in-a-Row games, the Hales–Jewett pairing strategy [Trans. Amer. Math. Soc. 106 (1963), 222–229] is a well-known strategy to prove that specific positions are (at most) a draw for the first player. For non-overlapping possible winning lines (groups) it requires two empty squares per g roup to be marked, i.e., with a coverage ratio of 2.0. In this paper we present a new strategy, called Set Matching.11This work is an extension of research presented at the ACG 2017 conference, which was published [in: Advances in Computer Games: 15th International Conference, ACG 2017, Springer, 2017, pp. 38–50]. A matching set consists of a set of nodes (the markers), a set of possible winning lines (the groups), and a coverage set indicating how all groups are covered after every first black move. This strategy needs less than two markers per group. As such it is able to prove positions in k-in-a-Row games to be draws, which cannot be proven using the Hales–Jewett pairing strategy. We show several efficient configurations with their matching sets. These include Cycle Configurations, BiCycle Configurations, and PolyCycle Configurations involving more than two cycles. Depending on configuration, the coverage ratio can be reduced to as low as 1.14. Many examples in the domain of solving k-in-a-Row games are given, including the direct proof (not based on search) that the empty 4×4 board is a draw for 4-in-a-Row. To illustrate the power of the method we also show two applications, which prove that 9-in-a-Row and 8-in-a-Row on infinite boards (and hence on any finite board as well) are draws, in a much more rigid way than by case analysis.
Discriminant Chronicle Mining

Sequential pattern miningDauxais, Yann attempts to extract frequent behaviors from a sequential dataset. When sequences are labeled, it is interesting to extract behaviors thatGross-Amblard, David characterize each sequence class. This taskGuyet, Thomas is called discriminant pattern mining. In this paper, we introduce discriminant chronicle mining. Conceptually, aHappe, André chronicle is a temporal graph whose vertices are events and whose edges represent numerical temporal constraints between these events. We propose DCM, an algorithm that mines discriminant chronicles. It is based on rule learning methods that extract the temporal constraints. Computational performances and discriminant power of extracted chronicles are evaluated on synthetic and real data. Finally, we apply this algorithm to the case study consisting in analyzing care pathways of epileptic patients.
Non-Autoregressive Machine Translation with Auxiliary Regularization

As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference.
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in \emph{zero-shot image retrieval and clustering}(ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this 'good' embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this 'good' embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to 'confuse' the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to 'confuse' the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-the-art performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. \textcolor[rgb]{1, 0, 0}{Code available at http://www.bhchen.cn/}.
Verifying Robustness of Gradient Boosted Models

Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models. This work introduces VeriGB, a tool for quantifying the robustness of gradient boosted models. VeriGB encodes the model and the robustness property as an SMT formula, which enables state of the art verification tools to prove the model's robustness. We extensively evaluate VeriGB on publicly available datasets and demonstrate a capability for verifying large models. Finally, we show that some model configurations tend to be inherently more robust than others.
Symmetries and physically realisable ensembles for open quantum systems

Abstract A D -dimensional Markovian open quantum system will undergo stochastic evolution which preserves pure states, if one monitors without loss of information the bath to which it is coupled. If a finite ensemble of pure states satisfies a particular set of constraint equations then it is possible to perform the monitoring in such a way that the (discontinuous) trajectory of the conditioned system state is, at all long times, restricted to those pure states. Finding these physically realisable ensembles (PREs) is typically very difficult, even numerically, when the system dimension is larger than 2. In this paper, we develop symmetry-based techniques that potentially greatly reduce the difficulty of finding a subset of all possible PREs. The two dynamical symmetries considered are an invariant subspace and a Wigner symmetry. An analysis of previously known PREs using the developed techniques provides us with new insights and lays the foundation for future studies of higher dimensional systems.
Be Inaccurate but Don’t Be Indecisive: How Error Distribution Can Affect User Experience

System accuracy is a crucial factor influencing user experience in intelligent interactive systems. Although accuracy is known to be important, little is known about the role of the system’s error distribution in user experience. In this paper we study, in the context of background music selection for tabletop games, how the error distribution of an intelligent system affects the user’s perceived experience. In particular, we show that supervised learning algorithms that solely optimize for prediction accuracy can make the system “indecisive”. That is, it can make the system’s errors sparsely distributed throughout the game session. We hypothesize that sparsely distributed errors can harm the users’ perceived experience and it is preferable to use a model that is somewhat inaccurate but decisive, than a model that is accurate but often indecisive. In order to test our hypothesis we introduce an ensemble approach with a restrictive voting rule that instead of erring sparsely through time, it errs consistently for a period of time. A user study in which people watched videos of Dungeons and Dragons sessions supports our hypothesis.
A simple algorithm for computing the document array

We present a simple algorithm for computing the document array given a string collection and its suffix array as input. Our algorithm runs in linear time using constant additional space for strings from constant alphabets.
Learning Embeddings into Entropic Wasserstein Spaces

Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metric. Wasserstein spaces are much larger and more flexible than Euclidean spaces, in that they can successfully embed a wider variety of metric structures. We exploit this flexibility by learning an embedding that captures semantic information in the Wasserstein distance between embedded distributions. We examine empirically the representational capacity of our learned Wasserstein embeddings, showing that they can embed a wide variety of metric structures with smaller distortion than an equivalent Euclidean embedding. We also investigate an application to word embedding, demonstrating a unique advantage of Wasserstein embeddings: We can visualize the high-dimensional embedding directly, since it is a probability distribution on a low-dimensional space. This obviates the need for dimensionality reduction techniques like t-SNE for visualization.
The Technological Gap Between Virtual Assistants and Recommendation Systems.

Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks. Meanwhile, existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions. The repertoire of actions is no longer limited to the one-shot presentation of recommendation lists, which can be insufficient when the goal is to offer decision support for the user, by quickly adapting to his/her preferences through conversations. Such an interactive mechanism is currently missing from recommendation systems. This article sheds light on the gap between virtual assistants and recommendation systems in terms of different technological aspects. In particular, we try to answer the most fundamental research question, which are the missing technological factors to implement a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions. The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life.