Reviewerly/exHarmony huggingface dataset:
Probabilistic and exact frequent subtree mining in graphs beyond forests

Motivated by the impressive predictive power of simple patterns, we consider the problem of mining frequent subtrees in arbitrary graphs. Although the restriction of the pattern language to trees does not resolve the computational complexity of frequent subgraph mining, in a recent work we have shown that it gives rise to an algorithm generating probabilistic frequent subtrees, a random subset of all frequent subtrees, from arbitrary graphs with polynomial delay. It is based on replacing each transaction graph in the input database with a forest formed by a random subset of its spanning trees. This simple technique turned out to be quite powerful on molecule classification tasks. It has, however, the drawback that the number of sampled spanning trees must be bounded by a polynomial of the size of the transaction graphs, resulting in less impressive recall even for slightly more complex structures beyond molecular graphs. To overcome this limitation, in this work we propose an algorithm mining probabilistic frequent subtrees also with polynomial delay, but by replacing each graph with a forest formed by an exponentially large implicit subset of its spanning trees. We demonstrate the superiority of our algorithm over the simple one on threshold graphs used e.g. in spectral clustering. In addition, providing sufficient conditions for the completeness and efficiency of our algorithm, we obtain a positive complexity result on exact frequent subtree mining for a novel, practically and theoretically relevant graph class that is orthogonal to all graph classes defined by some constant bound on monotone graph properties.
Dynamic Watermarking-Based Integrity Protection of Homomorphically Encrypted Databases – Application to Outsourced Genetic Data

In this paper, we propose a dynamic database crypto-watermarking scheme that enables a cloud service provider (CSP) to verify the integrity of encrypted databases outsourced by different users. This scheme takes advantage of the semantic security property most homomorphic cryptosystems have, so as to embed a watermark into encrypted data without altering users’ data. The incorrect detection of the watermark, not only informs the CSP the database has been illegally modified but also indicates which data have been altered. In addition, the proposed scheme is dynamic in the sense the watermarking and integrity verification processes can be conducted along the database lifecycle, i.e. even when the database owner updates his or her data (i.e. addition, suppression or modification of database elements). Experimental results carried out with the Paillier cryptosystem on a genetic database demonstrate that our method can efficiently detect different illegal data tamper with a high location precision.
A Scalable System Architecture for Activity Detection with Simple Heuristics

The analysis of video footage regarding the identification of persons at defined locations or the detection of complex activities is still a challenging process. Nowadays, various (semi-)automated systems can be used to overcome different parts of these challenges. Object detection and their classification reach even higher detection rates when making use of the latest cutting-edge convolutional neural network frameworks. Integrated into a scalable infrastructure as a service data base system, we employ the combination of such networks by using the Detectron framework within Docker containers with case-specific engineered tracking and motion pattern heuristics in order to detect several activities with comparatively low and distributed computing efforts and reasonable results.
Optimized fuzzy-based group recommendation with parallel computation

Rapid web growth and associated applications have proven of colossal importance for recommender systems. In the current digital world, a recommender system aims to acquire high-level prediction-based accuracy. However, many studies have suggested diversity-based recommendations are required for hig h-level accuracy. Group recommendation systems (GRS) recommend lists of items to a group of users according to their social activities, such as planning for a holiday tour, watching movies, etc. Using GRS, preferences/choices shared by users affected all the available aggregation with GRS leads to information loss and negatively affects ‘diversity.’ To handle the problem of ‘information loss,’ which is caused by aggregation, this paper proposes fuzzy-based GRS and argues that communicating such hesitant information will prove beneficial to generating recommendations. To find the valuable suggestions, greater focus must be dedicated to avoiding lack of variety and interest in the complete list of recommendations. In this article, we propose a novel Parallel Computing Group Recommendation System, which quantifies different approaches, chooses the right approach for group recommendation, and quickly generates optimal results. This proposed approach is an ensemble model of parallel ranking and matrix factorization that facilitates a diversified group recommendation list. Experimental evaluation signals that our model achieves higher diversity positively packed with user satisfaction.
Extraction and Forecasting Time Series of Production Processes

The manufacturing processes of the aircraft factory are analyzed to improve the quality of management decisions. Production processes models based on time series models are proposed. The applying of fuzzy smoothing of time series is considered. A new technique for extracting fuzzy trends for forecasting time series proposed. The use of type-2 fuzzy sets for making new models of time series with the aim of improving the quality of the forecast considered. An information system is being built to calculate the production capacity using these models. The system implements the algorithms for the calculation of a production capacity based on a methodology approved in the industry. The information extracted from the production processes is supposed to be used as a component of the models. An experiment with checking the quality of smoothing of time series is described. The experiment shows the possibility and advantages of modeling time series using type-2 fuzzy sets.
User graph topic model

Finding coherent topics in Twitter data is difficult task because of the sparseness and informal language. Tweets also provide rich contextual and auxiliary metadata which can be used to supervise the topic modeling to get more coherent topics. In this paper, a novel topic model is proposed which e xtends Author Topic Model for twitter. Standard Author Topic Model cannot be used on Twitter data as every tweet has exactly one author. The proposed User Graph Topic Model (UGTM) considers the semantic relationships among tweet users based on the contextual information like hashtags, user mentions and replies to make a user graph. Related users of author of a tweet are found and used in tweet generation process. Related user information from the user graph is used to obtain the dirichlet prior for user generation. Empirical results show that the proposed UGTM outperforms standard Author Topic Model (ATM) on experimental data.
Quadratic unconstrained binary optimization formulation for rectified-linear-unit-type functions

We propose a quadratic unconstrained binary optimization (QUBO) formulation of rectified-linear-unit (ReLU) type functions. Different from the $q$-loss function proposed by Denchev et al. [in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, edited by J. Langford and J. Pineau (Omnipress, Madison, USA, 2012)], a simple discussion based on the Legendre duality is not sufficient to obtain the QUBO formulation of ReLU-type functions. In addition to the Legendre duality, we employ the Wolfe duality, and the QUBO formulation of ReLU type is derived. The QUBO formulation is available in Ising-type annealing methods, including quantum annealing machines.
A Modal Aleatoric Calculus for Probabilistic Reasoning

We consider multi-agent systems where agents actions and beliefs are determined aleatorically, or "by the throw of dice". This system consists of possible worlds that assign distributions to independent random variables, and agents who assign probabilities to these possible worlds. We present a novel syntax and semantics for such system, and show that they generalise Modal Logic. We also give a sound and complete calculus for reasoning in the base semantics, and a sound calculus for the full modal semantics, that we conjecture to be complete. Finally we discuss some application to reasoning about game playing agents.
On the evaluation and selection of classifier learning algorithms with crowdsourced data

In many current problems, the actual class of the instances, the ground truth, is unavailable. Instead, with the intention of learning a model, the labels can be crowdsourced by harvesting them from different annotators. In this work, among those problems we focus on those that are binary classification problems. Specifically, our main objective is to explore the evaluation and selection of models through the quantitative assessment of the goodness of evaluation methods capable of dealing with this kind of context. That is a key task for the selection of evaluation methods capable of performing a sensible model selection. Regarding the evaluation and selection of models in such contexts, we identify three general approaches, each one based on a different interpretation of the nature of the underlying ground truth: deterministic, subjectivist or probabilistic. For the analysis of these three approaches, we propose how to estimate the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve within each interpretation, thus deriving three evaluation methods. These methods are compared in extensive experimentation whose empirical results show that the probabilistic method generally overcomes the other two, as a result of which we conclude that it is advisable to use that method when performing the evaluation in such contexts. In further studies, it would be interesting to extend our research to multiclass classification problems.
HiBsteR: Hierarchical Boosted Deep Metric Learning for Image Retrieval

When the number of categories is growing into thousands, large-scale image retrieval becomes an increasingly hard task. Retrieval accuracy can be improved by learning distance metric methods that separate categories in a transformed embedding space. Unlike most methods that utilize a single embedding to learn a distance metric, we build on the idea of boosted metric learning, where an embedding is split into a boosted ensemble of embeddings. While in general metric learning is directly applied on fine labels to learn embeddings, we take this one step further and incorporate hierarchical label information into the boosting framework and show how to properly adapt loss functions for this purpose. We show that by introducing several sub-embeddings which focus on specific hierarchical classes, the retrieval accuracy can be improved compared to standard flat label embeddings. The proposed method is especially suitable for exploiting hierarchical datasets or when additional labels can be retrieved without much effort. Our approach improves R@1 over state-of-the-art methods on the biggest available retrieval dataset (Stanford Online Products) and sets new reference baselines for hierarchical metric learning on several other datasets (CUB-200-2011, VegFru, FruitVeg-81). We show that the clustering quality in terms of NMI score is superior to previous works.
Evaluation of competitiveness of university with remote education

Purpose The working hypothesis of this research is that specifics of activities of university that provides remote education does not allow using the same evaluation criteria for assessment of its competitiveness that allow assessing competitiveness of university that provides traditional education. The purpose of this paper is to verify the offered hypothesis, study the modern Russian experience of evaluating the competitiveness of university that provides remote education and develop methodological recommendations for improving this process. Design/methodology/approach The authors use the existing scientific approaches to determining competitiveness of a company, including university, for complex and comprehensive study of the set problem. The information and analytical basis of this research is formed of methodological materials of the ranking of best universities of Russia for 2017 according to independent analytical agency “RaExpert” and “Announcement of open competition for state support for leading universities of the Russian Federation for the purpose of raising their competitiveness among the leading global scientific and educational centers of the Ministry of Education and Science of the RF.” Findings The offered hypothesis was proved. It is concluded that modern Russia uses the same (common) criteria for evaluating competitiveness of universities, regardless of the form of provided educational services. This is incorrect from the scientific and methodological point of view, as the work shows that most criteria are characterized by different applicability as to the university that provides traditional education and the university that provides remote education. Originality/value Methodological provision of evaluating the university’s competitiveness that provides remote education is elaborated by the authors by development of additional criteria. It is recommended to assign weight coefficients to all criteria depending on the level of applicability (importance) as to the university that provides remote education.
Evaluation of Spatial Perception in Virtual Reality within a Medical Context

This paper compares three different visualization techniques to improve spatial perception in virtual reality applications. In most virtual reality applications, spatial relations cannot be sufficiently estimated to make precise statements about the locations and positions of objects. Especially in the field of medical applications, it is crucial to correctly perceive the depth and structure of a given object. Thus, visualization techniques need to be developed to support the spatial perception. To address this, we carried out a user study to evaluate different visualization techniques and deal with the question of how glyphs influence spatial perception in a virtual reality application. Therefore, our evaluation compares arrow glyphs, heatmaps with isolines and pseudo-chromadepth in terms of improving the spatial perception within virtual reality. Based on the study results it can be concluded that spatial perception can be improved with the help of glyphs, which should motivate further research in this area.
A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.
One Shot Extrinsic Calibration of a Camera and Laser Range Finder Using Vertical Planes

In this paper, we present a new algorithm for the extrinsic calibration of a camera and laser range finder just using one-shot data. Most previous algorithms require multiple sets of data acquired under different poses. We newly designed a calibration structure where multiple polyhedrons are set in predefined positions on the ground plane which allow the natural application of multiple point-line constraints for the extrinsic calibration of a camera and laser range finder. Control points are extracted from laser range finder’s data by hand. If automatic detection of control points is possible, presented algorithm can be used for in-situ extrinsic calibration of a camera and laser range finder. Even using one-shot of data, presented algorithm gives comparable accuracy compared to conventional algorithm which requires multiple sets of data under different pose.
Going Global—International Personas

In recent years, discussions about interface design and evaluation methods have brought cultural aspects in play, and this goes for personas too. Organisations are developing personas for global markets, but the cultural issues of personas are not clearly defined, and there is very little information on how to account for cultural differences when creating personas (Seidelin et al. in Implementing personas for international markets: a question of UX maturity, 2014).
From textual orality to oral textuality: The case of voice queries

This study analyzes the phenomenon of digital voice search queries against the background of the fluid and changing balance in the orality–literacy osmosis of different historical eras. In attempting to theoretically conceptualize the unique oral characteristics of this new digital feature, this article argues that as the result of technological considerations, voice querying manifests an attempt to discipline oral words – to pronounce them while thinking of their written form. The article also considers the oxymoron of ‘looking up’ information through spoken words; the effect of an interface that stresses the use of the oral words as an event; the devocalization of queries, as they transform into a written form; and the implications of browsing the Internet through oral word searches, especially for young children. It concludes that the integration of these oral features can be explained by the affordances of digital media on the one hand and the ‘revival’ of intuitive preprint features attempting to ease the cognitive demands of print culture on the other hand.
Forward vehicle detection based on deep convolution neural network

Aiming at vehicle target detection in real traffic scenarios, the representative Faster R-CNN framework of deep learning target classification algorithm is applied to transform the problem of target detection in scenarios into a two-class problem of target detection combined with the vehicle data set in ImageNet to detect and recognize vehicle targets. Compared with the other two target detection algorithms, R-CNN and Fast R-CNN, the front vehicle target detection algorithm based on Faster R-CNN has obvious advantages in detection accuracy and execution efficiency. The experimental results show that the recognition accuracy and speed of this method have been significantly improved.
Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model

Face reconstruction is a popular topic in 3D vision system. However, traditional methods often depend on monocular cues, which contain few feature pixels and only use their location information while ignoring a lot of textural information. Furthermore, they are affected by the accuracy of the feature extraction method and occlusion. Here, we propose a novel facial reconstruction framework that accurately extracts the 3D shapes and poses of faces from images captured at multi-views. It extends the traditional method using the monocular bilinear model to the multi-view-based bilinear model by incorporating the feature prior constraint and the texture constraint, which are learned from multi-view images. The feature prior constraint is used as a shape prior to allowing us to estimate accurate 3D facial contours. Furthermore, the texture constraint extracts a high-precision 3D facial shape where traditional methods fail because of their limited number of feature points or the mostly texture-less and texture-repetitive nature of the input images. Meanwhile, it fully explores the implied 3D information of the multi-view images, which also enhances the robustness of the results. Additionally, the proposed method uses only two or more uncalibrated images with an arbitrary baseline, estimating calibration and shape simultaneously. A comparison with the state-of-the-art monocular bilinear model-based method shows that the proposed method has a significantly higher level of accuracy.
MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds

We consider the stochastic optimization of finite sums over a Riemannian manifold where the functions are smooth and convex. We present MASAGA, an extension of the stochastic average gradient variant SAGA on Riemannian manifolds. SAGA is a variance-reduction technique that typically outperforms methods that rely on expensive full-gradient calculations, such as the stochastic variance-reduced gradient method. We show that MASAGA achieves a linear convergence rate with uniform sampling, and we further show that MASAGA achieves a faster convergence rate with non-uniform sampling. Our experiments show that MASAGA is faster than the recent Riemannian stochastic gradient descent algorithm for the classic problem of finding the leading eigenvector corresponding to the maximum eigenvalue. Code related to this paper is available at: https://github.com/IssamLaradji/MASAGA .
Wearables and Workload

Prior research has reported that novelty affects the usage cycle of wearable devices. This chapter investigates the effects of sensation seeking, intensity, novelty, gender, and prior experience on the workload experienced during one aspect of using wearable fitness trackers, the device installation process. Contrary to the authors' hypotheses, prior experience, sensation seeking, intensity, and novelty did not significantly affect workload. The findings suggest that males tend to experience less workload during the setup of wearable fitness trackers; however, only for the Basis B1 and only for some aspects of workload. The claims made by prior research may be limited to specific aspects of the wearable fitness tracker use cycle, and more investigation is needed before broader claims can be made.
An Adaptive Denoising Algorithm for Improving Frequency Estimation and Tracking

We propose a novel adaptive denoising algorithm which, in presence of high levels of noise, significantly improves super-resolution and noise robustness of standard frequency estimation algorithms, such as (root-)MUSIC and ESPRIT. During the course of its operation, the algorithm dynamically estimates the power spectral density of noise and adapts to it. In addition, the proposed denoising front-end allows signal samples to be non-uniform, enabling the standard frequency estimation algorithms to achieve the same super-resolution, accuracy and noise robustness for non-uniformly sampled signals as for uniformly sampled signals of the same sample density. Extensive numerical tests verify superior denoising performance compared to the standard Cadzow method, especially when the noise present is not white. Our algorithm exploits salient features of numerically robust differential operators known as chromatic derivatives and the associated chromatic approximations which provide a method for digital processing of continuous time signals superior to processing which operates directly on their discrete samples.