Reviewerly/exHarmony huggingface dataset:
Mirror symmetry detection in curves represented by means of the Slope Chain Code

Symmetry is an important feature in natural and man-made objects; particularly, mirror symmetry is a relevant task in fields such as computer vision and pattern recognition. In the current work, we propose a new method to characterize mirror-symmetry in open and closed curves represented by means of the Slope Chain Code. This representation is invariant under scale, rotation, and translation, highly desirable properties for object recognition applications. The proposed method detects symmetries through simple inversion, concatenation and reflection operations on the chains, thus allowing the classification of symmetrical and asymmetrical contours. It also introduces a measure to quantify the degree of symmetry in quasi-mirror-symmetrical objects. Furthermore, it allows the identification of multiple symmetry axes and their location. Results show high performances in symmetrical/asymmetrical classification (0.9 recall, 0.9 accuracy, 0.97 precision) and axes' detection (0.8 recall, 0.84 accuracy, 0.99 precision). Compared to other methods, the proposed algorithm provides properties such as: global, local, and multiple axes' detection, as well as the capability to classify symmetrical objects, which makes it adequate for several practical applications, like the three exemplified in the paper.
Big Data and Creativity

Big data and machine learning techniques are increasingly applied to creative tasks, often with strong reactions of both awe and concern. But we have to be careful about where to attribute the creative agency. Is it really the machine that paints like van Gogh, or is it a human that uses a high-level tool to impart one pattern upon another, based on her aesthetic preferences? In this paper, the author analyses the problem of machine creativity, focusing on four central themes: the inherent convergence of machine learning and big data techniques, their dependence on assumptions and incomplete data, the possibility of explorative search as a new creative paradigm, and the related problem of the opacity of results from such methods. The Google Deep Dream project is brought in as an example to illustrate the discussion. Information and complexity are brought into the discussion as central concepts for both creative processes and the resulting artefacts, concluding that the complexity of the interaction between the creative agent and the environment during the creative process is a crucial parameter for meaningful creative output. Based on the exposed limitations in current technologies, the author concludes that the principal creative agency still lies in the developers and users of the tools, not in the data processing itself. Human effort and input still matters. But we can take a constructive approach, regarding big data techniques as tools one order of magnitude more complex than what was available before, allowing artists to work with abstractions previously unfeasible for computational work.
Determining relevant training data for effort estimation using Window-based COCOMO calibration

A software estimation model is often built using historical project data. As software development practices change over time, however, a model based on past data may not make accurate predictions for a new project. We investigate the use of moving windows to determine relevant training data for COCOMO calibration. We present a windowing calibration approach to calibrating COCOMO and assess performance of effort estimation models calibrated using windows and all data. Our results show that calibrating COCOMO using small windows of the most recently completed projects generates superior estimates than using all available historical projects. Large windows tend to produce worse estimates. This study provides empirical evidence to support the use of small windows of projects completed so far to calibrate models when COCOMO-like data is available. Additionally, when the change in software development over time is rapid, the use of windows is more justifiable for improving estimation accuracy.
A Two-tier Security Solution for Storing Data Across Public Cloud

Background: Data integrity protection in Cloud Computing becomes very challenging since the user no longer has the possession of their own data. This makes cloud data storage security of critical importance. The users can resort to legal action against the cloud provider if the provider fails to maintain the integrity of the shared data, but it also raises a need to secure users' private data across the public cloud. Methods: In this paper, we propose a novel end-to-end solution to ensure the security of data stored over a public cloud. It is a two-tier approach where the data is stored in an encrypted format and only the owner of the data can have access to the original data shared across the cloud. The algorithm OwnData: Encryption and Decryption is based on AES file encryption, which has the flexibility to be implemented across different cloud platforms. Results: The proposed OwnData model to provide privacy and confidentiality to the user data successfully secures data in an encrypted format. The users can gain full access control over the accessibility of their data. The application has been improvised to minimize page load time which enables it to achieve improvements in scalability. Algorithm and concatenation operators (dot) give minimum computation load during uploading of data to the cloud platform or downloading the same. Conclusion: The algorithm is robust, scalable and secure and It gives the user complete authorization and control over the data even when data is being stored remotely or in any other cloud premises.
From Attribute-Labels to Faces: Face Generation Using a Conditional Generative Adversarial Network

Facial attributes are instrumental in semantically characterizing faces. Automated classification of such attributes (i.e., age, gender, ethnicity) has been a well studied topic. We here seek to explore the inverse problem, namely given attribute-labels the generation of attribute-associated faces. The interest in this topic is fueled by related applications in law enforcement and entertainment. In this work, we propose two models for attribute-label based facial image and video generation incorporating 2D and 3D deep conditional generative adversarial networks (DCGAN). The attribute-labels serve as a tool to determine the specific representations of generated images and videos. While these are early results, our findings indicate the methods’ ability to generate realistic faces from attribute labels.
Sparse Gaussian Processes on Discrete Domains

Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contrast, probabilistic kernel methods like Gaussian Processes offer uncertainty estimates in addition to good predictive performance but fall short in terms of scalability. While the scalability of Gaussian processes can be improved using sparse inducing point approximations, the selection of these inducing points remains challenging. We explore different techniques for selecting inducing points on discrete domains, including greedy selection, determinantal point processes, and simulated annealing. We find that simulated annealing, which can select inducing points that are not in the training set, can perform competitively with support vector machines and full Gaussian processes on synthetic data, as well as on challenging real-world DNA sequence data.
Fast predictive multi-fidelity prediction with models of quantized fidelity levels

In this paper, we introduce a novel approach for the construction of multi-fidelity surrogate models with “discrete” fidelity levels. The notion of a discrete level of fidelity is in contrast to a mathematical model, for which the notion of refinement towards a high-fidelity model is relevant to sending a discretization parameter toward zero in a continuous way. Our notion of discrete fidelity levels encompasses cases for which there is no notion of convergence in terms of a fidelity parameter that can be sent to zero or infinity. The particular choice of how levels of fidelity are defined in this framework paves the way for using models that may have no apparent physical or mathematical relationship to the target high-fidelity model. However, our approach requires that models can produce results with a common set of parameters in the target model. Hence, fidelity level in this work is not directly representative of the degree of similarity of a low-fidelity model to a target high-fidelity model. In particular, we show that our approach is applicable to competitive ecological systems with different numbers of species, discrete-state Markov chains with a different number of states, polymer networks with a different number of connections, and nano-particle plasmonic arrays with a different number of scatterers. The results of this study demonstrate that our procedure boasts computational efficiency and accuracy for a wide variety of models and engineering systems.
Disjunctive belief rule base spreading for threat level assessment with heterogeneous, insufficient, and missing information

The threat level assessment of a suspect object is instrumental for developing combatant responses and effecting situational awareness. In practical conditions, it suffers from four challenges: (1) inputs are gathered from heterogeneous sources, which could also result in heterogeneous formats and types, (2) historic records are insufficient owing to limited number of suspect objects and under-developed detection technologies and devices, (3) missing information resulted from inadequacy of input gathering, exchanging, and storage, and (4) the assessment process should be accessible to experts and decision makers to provide reasonability. In this study, the belief rule base (BRB) under a disjunctive assumption (disjunctive BRB) is applied because it can handle heterogeneous and insufficient information as well as provide access to experts and decision makers. Furthermore, a new disjunctive BRB spreading approach with six steps is proposed to specifically handle the missing information challenge. First, multiple sub-BRBs are initialized and then optimized using heterogeneous and insufficient information. Sub-BRBs are in different belief structures, which are part of the complete belief structure. Second, a self-organizing map (SOM)-based procedure is proposed to integrate multiple sub-BRBs into one final BRB (f-BRB) in the complete belief structure with consideration of the weights of sub-BRBs. A practical threat level assessment case is studied to validate the efficiency of the proposed disjunctive BRB spreading approach. The case study results show that (1) optimization can help improve the modeling accuracy of sub-BRBs to different degrees while initial sub-BRBs have shown varied performances, and (2) f-BRB produces the highest modeling accuracy on both historic records and newly detected suspect objects.
Context-driven Multi-stream LSTM (M-LSTM) for Recognizing Fine-Grained Activity of Drivers

Automatic recognition of in-vehicle activities has significant impact on the next generation intelligent vehicles. In this paper, we present a novel Multi-stream Long Short-Term Memory (M-LSTM) network for recognizing driver activities. We bring together ideas from recent works on LSTMs, transfer learning for object detection and body pose by exploring the use of deep convolutional neural networks (CNN). Recent work has also shown that representations such as hand-object interactions are important cues in characterizing human activities. The proposed M-LSTM integrates these ideas under one framework, where two streams focus on appearance information with two different levels of abstractions. The other two streams analyze the contextual information involving configuration of body parts and body-object interactions. The proposed contextual descriptor is built to be semantically rich and meaningful, and even when coupled with appearance features it is turned out to be highly discriminating. We validate this on two challenging datasets consisting driver activities.
On Hilbert transform, analytic signal, and modulation analysis for signals over graphs

We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate-symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a ‘one-sided’ spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to highlight anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that graph analytic signal, and associated amplitude and frequency modulations reveal complementary information in speech signals.
Quantum criticality of quantum speed limit for a two-qubit system in the spin chain with the Dzyaloshinsky–Moriya interaction

We study the quantum speed limit (QSL) time of a two-qubit system coupled to a spin–chain model with the Dzyaloshinsky–Moriya (DM) interaction. For the Bell state coupled to the Ising model or anisotropic XY model, we find that there is a prominent corresponding relationship between the QSL time and quantum phase transition in a spin–chain environment with larger scale, and the DM interaction can strongly enhance or suppress the response relation. Remarkably, when the surrounding environment is set to the XX model, the DM interaction makes it possible for us to witness the quantum phase transition by the local anomalous enhancement of the QSL time near the critical point. In addition, our analyses indicate that the entanglement can speed-up the system evolution in many-body environment.
Latent multi-feature co-regression for visual recognition by discriminatively leveraging multi-source models

Learning a visual category with few labeled samples is a challenging problem in machine learning, which has motivated the multi-source adaptation learning technique, which exploits to transfer multiple prior discriminative models to target domain. Under this paradigm, however, different visual features at hand cannot be effectively exploited to represent a target object with versatility for boosting the adaptation performance. Besides, existing multi-source adaptation schemes mostly focus on either visual understanding or feature learning, independently. This may lead to the so-called semantic gap between the low-level features and the high-level semantics. Last but not the least, how to discriminatively select the prior models is yet another unresolved issue. To address these issues, we propose a novel co-regression framework with Multi-Source adaptation Multi-Feature Representation (MSMFR) for visual recognition, which jointly explores robust multi-feature co-regression, latent space learning, and representative sources selection, by integrating them into a unified framework for joint visual understanding and feature learning. Specifically, MSMFR conducts the multi-feature co-regression by simultaneously uncovering multiple latent spaces and minimizing the co-regression residual by taking correlations among multiple feature representations into account. Furthermore, MSMFR also automatically selects the representative (or discriminative) source models for each target feature representation via formulating a row-sparsity pursuit problem. The validity of our method is examined by three challenging visual domain adaptation tasks on several benchmark datasets, which demonstrate the superiority of our method in comparison with several state-of-the-arts.
An End-to-End Algorithm for Solving Circuit Problems

This paper presents an end-to-end algorithm for solving circuit problems in secondary physics. A key challenge in solving circuit problems is to automatically understand circuit problems over the modals of both text and schematic. Existing methods have a limited capacity in problem understanding due to the they cannot deal with the numerous expressions of problems in natural language and the various circuit diagrams. In fact that this paper, a batch of methods is proposed to work against the challenge of solving circuit problems. The problem understanding is modeled as a problem of relation extraction and a scheme is proposed to extract relations from both text and schematic. A syntax–semantics model is adopted to extract explicit relations from text, whereas a unit-theorem-based method is proposed to extract implicit relations. And a mesh search method is proposed to extract relations from schematic. Based on the result of problem understanding, an algorithm is proposed to produce the solutions of circuit problems, in which the solutions are presented in a readable way. The experimental results demonstrate the effectiveness of the proposed algorithm in solving circuit problems. To the best of our knowledge, this paper is the first literature which reports the quantitative results in understanding and solving circuit problems.
Fast Face Image Synthesis With Minimal Training

We propose an algorithm to generate realistic face images of both real and synthetic identities (people who do not exist) with different facial yaw, shape and resolution. The synthesized images can be used to augment datasets to train CNNs or as massive distractor sets for biometric verification experiments without any privacy concerns. Additionally, law enforcement can make use of this technique to train forensic experts to recognize faces. Our method samples face components from a pool of multiple face images of real identities to generate the synthetic texture. Then, a real 3D head model compatible to the generated texture is used to render it under different facial yaw transformations. We perform multiple quantitative experiments to assess the effectiveness of our synthesis procedure in CNN training and its potential use to generate distractor face images. Additionally, we compare our method with popular GAN models in terms of visual quality and execution time.
A Signal-Level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems

Machine learning algorithms, which form the core intelligence of wearables, traditionally deduce a computational model from a set of training data to detect events of interest. However, in the dynamic environment in which wearables operate, the accuracy of a computational model drops whenever changes in configuration or context of the system occur. In this paper, using transfer learning as an organizing principle, we propose a novel design framework to enable autonomous reconfiguration of wearable systems. More specifically, we focus on the cases where the specifications of sensor(s) or the subject vary compared to what is available in the training data. We develop two new algorithms for data mapping (the mapping is between the training data and the data for the current operating setting). The first data mapping algorithm combines effective methods for finding signal similarity with network-based clustering, while the second algorithm is based on finding signal motifs. The data mapping algorithms constitute the centerpiece of the transfer learning phase in our framework. We demonstrate the efficacy of the data mapping algorithms using two publicly available datasets on human activity recognition. We show that the data mapping algorithms are up to two orders of magnitude faster compared to a brute-force approach. We also show that the proposed framework overall improves activity recognition accuracy by up to 15 percent for the first dataset and by up to 32 percent for the second dataset.
Bayesian LDA for mixed-membership clustering analysis: The Rlda package

The goal of this paper is to present the Rlda package for mixed-membership clustering analysis based on the Latent Dirichlet Allocation model adapted to different types of data (i.e., Multinomial, Bernoulli, and Binomial entries). We present the corresponding statistical models and illustrate their use with several examples using the Rlda package. Because these types of data frequently emerge in fields as disparate as ecology, remote sensing, marketing, and finance, we believe this package will be of broad interest for users interested in unsupervised pattern recognition, particularly mixed-membership clustering analysis for categorical data.
Robust visible-infrared image matching by exploiting dominant edge orientations

Finding the correspondences between visible and infrared images is a challenging task due to the image spectral inconsistency which leads to large differences of gradient distributions between these images. To alleviate this problem, we propose a novel feature descriptor for visible and infrared image matching based on Log-Gabor filters. The descriptor employs multi-orientation and multi-scale Log-Gabor filters to encode the edge information statistically. Furthermore, the descriptor provides rotation invariance by estimating the dominant orientation which is based on accumulated edge orientations. The experimental results demonstrate the effectiveness of the proposed rotation invariant descriptor for matching visible and long wave infrared images as compared with state-of-the-art descriptors.
Efficient cloud-aided verifiable secret sharing scheme with batch verification for smart cities

With the wide usage of the information and communication technology (ICT) in smart cities, people’s lives become easier and more convenient. Cloud computing, as a burgeoning technology of the ICT, provides consumers with unlimited computing capabilities and storage resources. Using the cloud to promote the progress of the ICT-based applications meets the requirement of the practical usage, and is also in line with the sustainable development. As we all know, the secret sharing is a hot topic in the security community. Many security-assurance applications can be realized with the assistance of secret sharing. In this paper, an efficient cloud-aided verifiable secret sharing scheme is proposed based on the polynomial commitment for smart cities, which can be used in a variety of practical applications such as electronic voting and revocable electronic cash. In the proposed scheme, users can verify the received shares from the cloud. Moreover, in order to meet the requirement of the real-world usage, we extend our scheme to support the batch verification with the aid of a third-party arbitration center. In addition, the aggregate signature is used to verify whether a subset of users possess the shares that indeed sent by the cloud. The security analysis shows that the proposed scheme can satisfy the security requirements of the verifiable secret sharing (VSS) and the performance analysis shows that our scheme is more efficient than previous schemes in terms of the communication and the computation.