Reviewerly/exHarmony huggingface dataset:
Integrally private model selection for decision trees

Privacy attacks targeting machine learning models are evolving. One of the primary goals of such attacks is to infer information about the training data used to construct the models. “Integral Privacy” focuses on machine learning and statistical models which explain how we can utilize intruder’s uncertainty to provide a privacy guarantee against model comparison attacks. Through experimental results, we show how the distribution of models can be used to achieve integral privacy. Here, we observe two categories of machine learning models based on their frequency of occurrence in the model space. Then we explain the privacy implications of selecting each of them based on a new attack model and empirical results. Also, we provide recommendations for private model selection based on the accuracy and stability of the models along with the diversity of training data that can be used to generate the models.
Boolean kernels for rule based interpretation of support vector machines

Machine learning started as an academic-oriented domain, but nowadays it is becoming more and more widespread across diverse domains, such as retail, healthcare, finance, and many more. This non-academic face of machine learning creates a new set of challenges. The usage of such complex methods by non-expert users has increased the need for interpretable models. To this end, in this paper we propose an approach for extracting explanation rules from support vector machines. The core idea is based on using kernels with feature spaces composed by logical propositions. On top of that, a searching algorithm tries to retrieve the most relevant features/rules that can be used to explain the trained model. Experiments on both categorical and real-valued datasets show the effectiveness of the proposed approach.
Co-learning binary classifiers for LP-based multi-label classification

A simple yet practical multi-label learning method, called label powerset (LP), considers each different combination of labels that appear in the training set as a different class value of a single-label classification task. However, because those classes source from multiple labels, there may be some inherent relationships among them. To tackle this challenge, we propose a novel model which aims to co-learn binary classifiers, by combining the training of binary classifiers and the characterizing the relationship among them into a unified objective function. In addition, we develop an alternating optimization algorithm to solve the proposed problem. Extensive experimental results on various kinds of datasets well demonstrate the effectiveness of the proposed model.
Matrix Product States: Entanglement, Symmetries, and State Transformations

We analyze entanglement in the family of translationally invariant matrix product states (MPS). We give a criterion to determine when two states can be transformed into each other by local operations with a nonvanishing probability, a central question in entanglement theory. This induces a classification within this family of states, which we explicitly carry out for the simplest, nontrivial MPS. We also characterize all symmetries of translationally invariant MPS, both global and local (inhomogeneous). We illustrate our results with examples of states that are relevant in different physical contexts.
Enhanced semantic similarity measure based on two‐level retrieval model

Summary With the rapid spread of services in the Cloud computing environment, it is difficult for users to find the right service. Therefore, the necessity of a search engine with semantic focused crawler becomes a fundamental requirement. However, the huge size and varied functionalities of Cloud services on the Web, together with the lack of standardized and coherent description of services available, have a great effect for crawlers in order to provide effective Cloud services. To solve these issues, we propose a Cloud service discovery crawler that employs a two‐level semantic similarity measure based on both TF‐IDF and LDA models. Moreover, in order to automatically discover and categorize Cloud services, we present a Cloud Service Ontology (CSOnt) that contains a set of concepts defining Cloud service categories. Experimental results show that the proposed method enhances the performance of the focused crawlers and presents an efficient way to parse the Web and collect Web pages relevant to Cloud services.
Quantile and expectile smoothing based on L1-norm and L2-norm fuzzy transforms

The fuzzy transform (F-transform), introduced by I. Perfilieva, is a powerful tool for the construction of fuzzy approximation models; it is based on generalized fuzzy partitions and it is obtained by minimizing a quadratic (L 2 -norm) error function.In this paper, within the discrete setting, we describe an analogous construction by minimizing an L 1 -norm error function, so obtaining the L 1 -norm F-transform, which is again a general approximation tool.The L 1 -norm and L 2 -norm settings are then used to construct two types of fuzzyvalued F-transforms, by defining expectile (L 2 -norm) and quantile (L 1 -norm) extensions of the transforms.This allows to model an observed time series in terms of fuzzy-valued functions, whose level-cuts can be interpreted in the setting of expectile and quantile regression.The proposed methodology is illustrated on some financial daily time series.
POLAR: Attention-Based CNN for One-Shot Personalized Article Recommendation

In this paper, we propose POLAR, an attention-based CNN combined with one-shot learning for personalized article recommendation. Given a query, POLAR uses an attention-based CNN to estimate the relevance score between the query and related articles. The attention mechanism can help significantly improve the relevance estimation. For example, on AMiner, this can help achieve a +5.0% improvement in terms of NDCG@3. One more challenge in personalized article recommendation is how to collect statistically sufficient training data for a recommendation model. POLAR combines a one-shot learning function into the recommendation model, which further gains significant improvements. For example, on AMiner, with only 1.6 feedbacks on average, POLAR achieves 2.7% improvement by NDCG@3. We evaluate the proposed POLAR on three different datasets: AMiner, Patent, and RARD. Experimental results demonstrate the effectiveness of the proposed model. Recently, we have successfully deployed POLAR into AMiner as the recommendation engine for article recommendation, which further confirms the effectiveness of the proposed model. Data related to this paper is available at: https://doi.org/10.6084/m9.figshare.7297319 .
Variational EM method for blur estimation using the spike-and-slab image prior

Most state-of-the-art blind image deconvolution methods rely on the Bayesian paradigm to model the deblurring problem and estimate both the blur kernel and latent image. It is customary to model the image in the filter space, where it is supposed to be sparse, and utilize convenient priors to account for this sparsity. In this paper, we propose the use of the spike-and-slab prior together with an efficient variational Expectation Maximization (EM) inference scheme to estimate the blur in the image. The spike-and-slab prior, which constitutes the gold standard in sparse machine learning, selectively shrinks irrelevant variables while mildly regularizing the relevant ones. The proposed variational Expectation Maximization algorithm is more efficient than usual Markov Chain Monte Carlo (MCMC) inference and, also, proves to be more accurate than the standard mean-field variational approximation. Additionally, all the prior model parameters are estimated by the proposed scheme. After blur estimation, a non-blind restoration method is used to obtain the actual estimation of the sharp image. We investigate the behavior of the prior in the experimental section together with a series of experiments with synthetically generated and real blurred images that validate the method's performance in comparison with state-of-the-art blind deconvolution techniques.
Computing programs for generalized planning using a classical planner

Generalized planning is the task of generating a single solution (a generalized plan) that is valid for multiple planning instances. In this paper we introduce a novel formalism for representing generalized plans that borrows two mechanisms from structured programming: control flow and procedure calls. On one hand, control flow structures allow to compactly represent generalized plans. On the other hand, procedure calls allow to represent hierarchical and recursive solutions as well as to reuse existing generalized plans. The paper also presents a compilation from generalized planning into classical planning which allows us to compute generalized plans with off-the-shelf planners. The compilation can incorporate prior knowledge in the form of auxiliary procedures which expands the applicability of the approach to more challenging tasks. Experiments show that a classical planner using our compilation can compute generalized plans that solve a wide range of generalized planning tasks, including sorting lists of variable size or DFS traversing variable-size binary trees. Additionally the paper presents an extension of the compilation for computing generalized plans when generalization requires a high-level state representation that is not provided a priori. This extension brings a new landscape of benchmarks to classical planning since classification tasks can naturally be modeled as generalized planning tasks, and hence, as classical planning tasks. Finally the paper shows that the compilation can be extended to compute control knowledge for off-the-shelf planners and solve planning instances that are difficult to solve without such additional knowledge.
Multiple Context Features in Siamese Networks for Visual Object Tracking

Siamese networks have been successfully utilized to learn a robust matching function between pairs of images. Visual object tracking methods based on siamese networks have been gaining popularity recently due to their robustness and speed. However, existing siamese approaches are still unable to perform on par with the most accurate trackers. In this paper, we propose to extend the SiamFC tracker [1] to extract features at multiple context and semantic levels from very deep networks. We show that our approach effectively extracts complementary features for siamese matching from different layers, which provides a significant performance boost when fused. Experimental results on VOT and OTB datasets show that our multi-context tracker is comparable to the most accurate methods, while still being faster than most of them. In particular, we outperform several other state-of-the-art siamese methods.
Document reordering for faster intersection

A lot of research has studied how to optimize inverted index structures in search engines through suitable reassignment of document identifiers. This approach was originally proposed to allow for better compression of the index, but subsequent work showed that it can also result in significant speed-ups for conjunctive queries and even certain types of disjunctive top-k algorithms. However, we do not have a good understanding of why this happens, and how we could directly optimize an index for query processing speed. As a result, existing techniques attempt to optimize for size, and treat speed increases as a welcome side-effect. In this paper, we take an initial but important step towards understanding and modeling speed increases due to document reordering. We define the problem of minimizing the cost of queries given an inverted index and a query distribution, relate it to work on adaptive set intersection, and show that it is fundamentally different from that of minimizing compressed index size. We then propose a heuristic algorithm for finding a document reordering that minimizes query processing costs under suitable cost models. Our experiments show significant increases in the speed of intersections over state-of-the-art reordering techniques.
Square it up!

In this paper, we explore how we can model students' response times to predict student performance in Intelligent Tutoring Systems. Related research suggests that response time can provide information with respect to correctness. However, time is not consistently used when modeling students' performance. Here, we build on previous work that indicated that the relationship between response time and student performance is non-linear. Based on this concept, we compare three models: a standard Additive Factors Analysis Model (AFM), an AFM model enhanced with a linear step duration parameter and an AFM model enhanced with a quadratic, step duration parameter. The results of this comparison show that the AFM model that is enhanced with the quadratic step duration parameter outperforms the other models over four different datasets and for most of the metrics we used to evaluate the models in cross validation and prediction.
Protocol of a quantum walk in circuit QED

Implementation of a discrete-time quantum walk (DTQW) with superconducting qubits is difficult since on-chip superconducting qubits cannot hop between lattice sites. We propose an efficient protocol for the implementation of DTQW in circuit quantum electrodynamics (QED), in which only $N+1$ qutrits, $N$ cavities, and other assistant devices are needed for an $N$-step DTQW. The operation of each DTQW step is very quick because only resonant processes are adopted. The numerical simulations show that high-similarity DTQW with a number of steps up to 20 is feasible with present-day circuit QED techniques. This protocol can help to study properties and applications of many-step DTQW in experiments, and can be extended to implement multidimensional DTQW in circuit QED or other quantum systems.
Quantitative Variability Modeling and Analysis

The explicit management of variability in the development cycle of software-intensive systems has led to a plethora of modeling and analysis techniques tailored to deal with behavioral validation of such configurable systems. Most of the work, however, focuses on qualitative (i.e. functional) requirements. Recently, there is growing interest in variability modeling and analysis techniques that do explicitly consider quantitative (i.e. non-functional) requirements, such as dependability, energy consumption, security, and cost.
Performance analysis of machine learning classifiers on improved concept vector space models

This paper provides a comprehensive performance analysis of parametric and non-parametric machine learning classifiers including a deep feed-forward multi-layer perceptron (MLP) network on two variants of improved Concept Vector Space (iCVS) model. In the first variant, a weighting scheme enhanced with the notion of concept importance is used to assess weight of ontology concepts. Concept importance shows how important a concept is in an ontology and it is automatically computed by converting the ontology into a graph and then applying one of the Markov based algorithms. In the second variant of iCVS, concepts provided by the ontology and their semantically related terms are used to construct concept vectors in order to represent the document into a semantic vector space. We conducted various experiments using a variety of machine learning classifiers for three different models of document representation. The first model is a baseline concept vector space (CVS) model that relies on an exact/partial match technique to represent a document into a vector space. The second and third model is an iCVS model that employs an enhanced concept weighting scheme for assessing weights of concepts (variant 1), and the acquisition of terms that are semantically related to concepts of the ontology for semantic document representation (variant 2), respectively. Additionally, a comparison between seven different classifiers is performed for all three models using precision, recall, and F1 score. Results for multiple configurations of deep learning architecture are obtained by varying the number of hidden layers and nodes in each layer, and are compared to those obtained with conventional classifiers. The obtained results show that the classification performance is highly dependent upon the choice of a classifier, and that the Random Forest, Gradient Boosting, and Multilayer Perceptron are among the classifiers that performed rather well for all three models.
Survey on GAN‐based face hallucination with its model development

Face hallucination aims to produce a high-resolution face image from an input low-resolution face image, which is of great importance for many practical face applications, such as face recognition and face verification. Since the structure of the face image is complex and sensitive, obtaining a super-resolved face image is more difficult than generic image super-resolution. Recently, with great success in the high-level face recognition task, deep learning methods, especially generative adversarial networks (GANs), have also been applied to the low-level vision task – face hallucination. This work is to provide a model evolvement survey on GAN-based face hallucination. The principles of image resolution degradation and GAN-based learning are presented firstly. Then, a comprehensive review of the state-of-art GAN-based face hallucination methods is provided. Finally, the comparisons of these GAN-based face hallucination methods and the discussions of the related issues for future research direction are also provided.
Typicality‐based collaborative filtering for book recommendation

Abstract Nowadays, personalized recommender system placed an important role to predict the customer needs, interest about particular product in various application domains, which is identified according to the product ratings. During this process, collaborative filtering (CF) has been utilized because it is one of familiar techniques in recommender systems. The conventional CF methods analyse historical interactions of user‐item pairs based on known ratings and then use these interactions to produce recommendations. The major challenge in CF is that it needs to calculate the similarity of each pair of users or items by observing the ratings of users on same item, whereas the typicality‐based CF determines the neighbours from user groups based on their typicality degree. Typicality‐based CF can predict the ratings of users with improved accuracy. However, to eliminate the cold start problem in the proposed recommender system, the demographic filtering method has been employed in addition to the typicality‐based CF. A weighted average scheme has been applied on the combined recommendation results of both typicality‐based CF and demographic‐based CF to produce the best recommendation result for the user. Thereby, the proposed system has been able to achieve a coverage ratio of more than 95%, which indicates that the system is able to provide better recommendation for the user from the available lot of products.
APS: Attribute-aware privacy-preserving scheme in location-based services

As one of the most significant factors for privacy protection, side information has been considered in designing privacy-preserving schemes in Location-Based Services (LBSs) over recent years. However, most existing schemes consider this concept through a straightforward way, such as query probability. In this paper, we consider the basic attribute associating with each location and design an Attribute-aware Privacy-preserving Scheme (APS) to enhance mobile user’s location privacy. Specifically, we first extract basic attributes from the local map, and specialize the Attribute-Aware Side Information (AASI). Then we build an attribute-based hierarchical tree (A-tree), which classifies locations into different categories in term of each location’s attribute. Based on such information, we design APS, which consists of two algorithms, Voronoi Dividing Algorithm (VDA) and Dummy Determining Algorithm (DDA). In VDA, we divide the local map into different Voronoi polygons based on the properties of Voronoi Diagram, which guarantees the selected locations are dispersed. In DDA, we utilize the Four Color Map Theorem to color these Voronoi polygons, which helps mobile users to choose the dummy locations as far as possible. Therefore, our APS provides an optimal dummy set to protect mobile user’s location privacy and query privacy. Finally, thorough analysis and evaluation results illustrate the effectiveness and efficiency of our proposed scheme.
The Timeliness Deviation

The decision on what item to learn next in a course can be supported by a recommender system (RS), which aims at making the learning process more efficient and effective. However, learners and learning activities frequently change over time. The question is: how are timely appropriate recommendations of learning resources actually evaluated and how can they be compared?
Compositional Models for Complex Systems

In this chapter, we argue for the use of representations from category theory to support better models for complex systems, and provide an example of what such an application might look like. Our approach rests on the well-known observation that complex system design is a fundamentally recursive discipline, which is formalized in computer science using structures called algebras, coalgebras, and operads, mathematical structures that are intimately linked to labeled tree representations. We then develop two small examples to demonstrate the utility of this approach. The first defines a logical semantics of contracts to organize requirements at different scales in hierarchical systems. The second concerns the integration of artificial intelligence models into a preexisting human-driven process.
Five-Degree-of-Freedom Pose Estimation from an Imaged Ellipsoid of Revolution

Covers advancements in spacecraft and tactical and strategic missile systems, including subsystem design and application, mission design and analysis, materials and structures, developments in space sciences, space processing and manufacturing, space operations, and applications of space technologies to other fields.
Attribute-Based Signcryption From Lattices in the Standard Model

For realizing the fine-grained access control with non-interactive approach, and effectively guaranteeing the comprehensive security for information under the post-quantum environment, this paper proposes an attribute-based signcryption (ABSC) scheme based on the intractability of lattices. The proposed ABSC scheme is proved indistinguishable against the inner adaptive-chosen ciphertext attacks (IND-CCA2) and existentially unforgeable against inner chosen-message attacks (EUF-CMA), in the standard model. The theoretical analysis presents that the public key size and the computational cost of the signcryption operation are both reduced obviously, compared with the signature and then encryption mechanism. An efficient variant is also presented that significantly decreases the computational complexity of unsigncryption operation at the expense of an increase in the ciphertext size.
IBM Scenario Planning Advisor: Plan recognition as AI planning in practice

We present the IBM Research Scenario Planning Advisor (SPA), a decision support system that allows users to generate diverse alternate scenarios of the future and enhance their ability to imagine the different possible outcomes, including unlikely but potentially impactful futures. Our system, take s as input the relevant information from news and social media, representing key risk drivers, as well as the domain knowledge and generates scenarios that explain the key risk drivers and describe the alternative futures. To this end, we provide a characterization of the problem, knowledge engineering methodology, and transformation to AI planning. Furthermore, we describe the computation of the scenarios, lessons learned, and the feedback received from the pilot deployment of the SPA system in IBM.