Reviewerly/exHarmony huggingface dataset:
A Kernel Estimation For Blurry Image Using Maximum a Posteriori Framework with Elastic-Net Prior

Blind image deblurring problem, estimating the blur kernel and latent image from a single blurry image, is the a severely ill-posed problem. Since the blur kernel plays an important role in image deblurring problem, we focus on how to effectively apply Elastic-Net prior(ENP) to estimate blur kernel. During the estimation of kernel blur, the ENP is used to maintain the slight edge and removing insignificant texture of intermediate image. Thus, a kernel estimation method is proposed, based on Maximum a Posteriori(MAP) method with ENP. And then, the half-quadratic splitting method is used to solve the non-convex problem of this model. Finally, the latent image can be estimated by using the hyper-Laplacian priors and the estimated blur kernel. Experimental results show that the proposed method can obtain better blur kernel and improve the quality of the latent image estimation, against with the state-of-the art methods.
WASOS: An Ontology for Modelling Traditional Knowledge of Sustainable Water Stewardship

Recent work and publications concerning sustainable water stewardship in Rajasthan (India) highlight how contemporary challenges are eroding traditional, communal approaches to water stewardship through mechanised extraction beyond the renewable capacities of ecosystems. Our work is focused on developing a formal ontology for modelling the knowledge of traditional water stewardship in India’s drylands by capturing the key constitutional elements of regenerative methods. Our method follows an iterative evolving prototype process for delivering the first version of the Ontology for Sustainable Water Stewardship (WASOS). The ontology contains a moderate number of high-level classes and properties that represent the water management decision-making process. By making key relationships visible, we aim to support decision-making in complex catchments particularly where there are contested urban and rural claims on water.
Pose-aware Person Re-Identification with Spatial-temporal Attention

The Person re-ID task objective is to search for a specified target pedestrian image in a large gallery. With the development of deep learning technology, the accuracy of personnel re-identification has been greatly improved. However, previous methods lacked sufficient attention to local features when focusing on global features, causing background and occlusion objects to affect the foreground. The framework we proposed specifically designed this problem to optimize local features through human pose key-points and self-attention mechanisms. And the effectiveness of the method is demonstrated in multiple datasets.
Engineering Cloud-Based Technological Infrastructure

In the last decade cloud development has grown exponentially and increasingly companies and institutions decide to make the leap and provide their solutions into a cloud platform. This is mainly because of the evident benefits of using cloud-based solutions like high availability, scalability or redundancy, which are impossible or too much costly to be achieved on-premises. Cloud platforms may reduce considerably the costs and difficulty of maintenance, while offering flexible solutions to each need (Talukder and Zimmerman in Computing. computer communications and networks. Springer, London, 2010 [1]). Enterprises have a lot of services available to their needs, some of them very basic like a database, a web site or storage, but also many other advanced services for business intelligence, big data, artificial intelligence and other emerging technologies. However, from the architectural point of view, cloud-based solutions suppose some challenges not existing in traditional on-premises solutions that must be tackled during the engineering process. Whether a monolithic application may fit well on-premises, cloud environments work better with modularization and separation of services. This separation and distribution of services requires additional work to define communications between components and provide security techniques to guarantee data protection against potential risks like data loss, privacy breaks, unwanted access, etc. (Ghaffari et al. in 2016 8th international symposium on telecommunications (IST). IEEE, pp 105–110, 2016 [2]). Performance is also critical in cloud-based solutions, especially when services are located in different nodes on the internet. All these concerns suppose a quite big learning curve to the technical team that prevents some enterprises to move to cloud, especially when they try to migrate already existing solutions. This chapter discusses about the ins and outs of engineering a cloud-based solution applied to a real use case.
Parallel Architecture Based Iterative Segmentation Optimal Cyclic Block Kernel Learning Algorithm

The kernel learning algorithm has been widely used to solve the generalization problem existing in reinforcement learning. However, when the state-action space of the sample is large, the computation and storage burden in the kernel learning algorithm will increase. In such case, there will be long running time and the real-time performance of the control system cannot be guaranteed. Therefore, in order to enhance the computational speed, this paper proposes a parallel architecture based iterative segmentation optimal cyclic block kernel learning algorithm. The experimental results show that the proposed method can significantly improve the computational efficiency of the kernel learning algorithm, which has important engineering practice significance.
Dissimilarity-based multi-instance learning using dictionary learning and sparse coding ensembles

In multi-instance learning problems, samples are represented by multisets, which are named as bags. Each bag includes a set of feature vectors called instances. This differs multi-instance learning problems from classical supervised learning problems. In this paper, to convert a multi-instance learning problem into a supervised learning problem, fixed-size feature vectors of bags are computed using a dissimilarity based method. Then, dictionary learning based bagging and random subspace ensemble classification models are proposed to exploit the underlying discriminative structure of the dissimilarity based features. Experimental results are obtained on 11 different datasets from different multi-instance learning problem domains. It is shown that the proposed random subspace based dictionary ensemble algorithm gives the best results on 8 datasets in terms of classification accuracy and area under curve.
Visualizing Out-of-synchronization in Group Dancing

Prior work on choreographic learning support systems lacks supports of group dancers. In particular, existing systems cannot quantify the degree of synchronization, which is a critical factor for successful group dancing. In this paper, we create a system to support multi-person choreographic learning. The system visualizes body parts of dancers which are out of synchronization. Our interface aims to enable dancers to quickly identify moments where they need additional practice. This paper presents our current prototype interface and demonstration.
Person Inpainting with Generative Adversarial Networks

The use of Generative Adversarial Networks (GAN) in image inpainting tasks where large holes in images are predicted have proven to be successful. Previous methods were tested on various texture, face, nature datasets but to our knowledge no prior work exists where masked human body parts are predicted. In this work, we use a deep generative network where a Generalized Loss Sensitive GAN (GLS-GAN) is utilized for the people inpainting problem. We show GLS-GAN performance in the human body inpainting domain.
Research on Phase Unwrapping Based on Improved Ant Colony Algorithm

Phase unwrapping is a key step in data processing of interferometric SAR. The key to the traditional entanglement method is the establishment of branch tangents. The shorter the length of the tangential line, the better the untangling effect. Under the idea that the business travel problem (TSP) solves the shortest path, the ant colony algorithm and the particle swarm algorithm are proposed. Based on the two algorithms, the cluster analysis method can be introduced to establish a shorter branch tangent than the traditional unwrapping method, to avoid the "island effect" in the process of untangling. The feasibility and effectiveness of the method are verified by simulation.
A fringe projection profilometry scheme based on embedded speckle patterns and robust principal component analysis

Phase unwrapping is one of the key steps for fringe projection profilometry (FPP)-based 3D shape measurements. Conventional spatial phase unwrapping schemes are sensitive to noise and discontinuities, which may suffer from low accuracies. Temporal phase unwrapping is able to improve the reliability but often requires the acquisition of additional patterns, increasing the measurement time or hardware costs. This paper introduces a novel phase unwrapping scheme that utilizes composite patterns consisting of the superposition of standard sinusoidal patterns and randomly generated speckles. The low-rankness of the deformed sinusoidal patterns is studied. This is exploited together with the sparse nature of the speckle patterns and a robust principal component analysis (RPCA) framework is then deployed to separate the deformed fringe and speckle patterns. The cleaned fringe patterns are used for generating the wrapped phase maps using the standard procedures of phase shift profilometry (PSP) or Fourier Transform profilometry (FTP). Phase unwrapping is then achieved by matching the deformed speckle patterns that encode the phase order information. In order to correct the impulsive fringe order errors, a recently proposed postprocessing step is integrated into the proposed scheme to refine the phase unwrapping results. The analysis and simulation results demonstrate that the proposed scheme can improve the accuracy of FPP-based 3D shape measurements by effectively separating the fringe and speckle patterns.
Short and Long Term Memory in Regenerative Spiking Micropillar Lasers

Summary form only given. Micropillar lasers with integrated saturable absorber (SA) have been demonstrated to show important neuromimetic properties such as ultrafast excitable behavior, temporal summation, relative and absolute refractory periods and spike latency [1-4]. It has been observed that living nervous systems have recurrent loops involving two or more neurons in regions essential for memory [5]. We recreate such recurrent connection in our photonic system with the help of a delayed optical feedback. We study experimentally and theoretically the dynamics of the micropillar laser with SA with delayed optical feedback and demonstrate two distinct regimes: in the short term this system can store a great variety of temporal spike patterns, whereas in the long term only specific patterns can be stored, giving rise to an associative memory. In an excitable laser, a perturbation above a given threshold gives rise to a calibrated response spike (all -or -none kind of response). With a delayed optical feedback, a single perturbation above the excitable threshold can trigger one regenerative spike train which can exist in the external cavity for many roundtrips (>0(10 4 )) and whose repetition period is given by the delay. Additional perturbations can result in changing the timing phase of an existing spike train, triggering an additional spike train or removing one. Once a spike pattem is excited within the delay loop, it can repeat itself almost unchanged for several hundreds of round trips of the external cavity, thus resulting in an optical memory. This transient state can be considered as a short term memory of the system where an arbitrary temporal sequence can be preserved [6,7]. In the long term however, the transient state converges through spike interaction (repelling or attraction) towards one of the finite number of multistable pulsing modes accessible to the system [8]. This behaviour is similar to the one reported in the Hodgkin -Huxley model for biological neurons [5]. A bifurcation analysis and numerical simulations of the Yamada model with delayed optical feedback are presented and are in good agreement with the experimental observations. All the observed dynamics of spike trains viz pulse attraction and repelling can be explained solely based on the intemal dynamics of carriers in the gain and saturable absorber regions of the laser. In these results we have studied the delay dynamics of excitable spikes in single micropillar lasers. This understanding can help in the future development of larger networks with multiple spatially and temporally coupled nodes for e.g. artificial and ultrafast spiking recurrent neural networks
Generative Image Inpainting for Person Pose Generation

Filling large missing regions of an image with semantically meaningful and visually coherent information is a challenging task. Traditional approaches use textures from surrounding pixels for filling in the missing patches and are suitable for filling the missing data with a generic background. The dataset provided in the ECCV’18 Satellite Workshop Chalearn LAP Inpainting Competition Track 1-Inpainting of still images of humans contains masked images with randomly placed square masks, occluding up to 70% of the image data. The masks are human-centered. In order to inpaint the human-centric images, we use a generative model which can generate patches that do not appear anywhere in the image. This allows it to complete images with detailed features like faces. Our model can inpaint images with multiple holes of different sizes at various locations and can handle a wide variety of scenes. Our model produces decent results in reconstructing not only the occluded human parts but also the background. Most of our work is inspired by previous work on Image Generation and Image Inpainting.
Innovation-Modified Kalman Filtering for A Class of Stochastic Nonlinear Systems

This paper is concerned with the innovation-detected Kalman filtering problem for a class of stochastic nonlinear systems. Owing to the temporal failure of sensors caused by undesirable environmental circumstances, the measurement outputs of sensors might suffer from unexpected outliers. In this case, a detection scheme of the sensor measurement is employed to alleviate its unfavorable effect on the filtering performance. With the aid of the stochastic analysis techniques, a sufficient condition is established to guarantee that there exists an upper bound of the filtering error variance. Then, the filter gain matrix is determined by minimizing such an upper bound. A simulation example is finally presented to illustrate the validity of the proposed filtering scheme.
Building an Early Warning Model for Detecting Environmental Pollution of Wastewater in Industrial Zones

In this paper, we present two soft computing techniques, which are support vector regression (SVR) and fuzzy logic, to build an early warning model for detecting environmental pollution of waste-water in industrial zones. To determine the proper number of inputs for the model, we use an algorithm to find the embedding dimension space for a time series. Our proposed model, which has a high accuracy and short training time, to helps waste-water processing station operators take early action and avoid environmental pollution.
A new phase analysis method using mean intensity on speckle interferometry for measuring dynamic phenomena

A new phase analysis method using mean intensity values on electronic speckle pattern interferometry is proposed for measuring dynamic phenomena with variations of the mean intensity and the modulation amplitude values. In this method, phase differences are calculated by least-squares in kernels using interfered intensity values and mean intensity values of laser speckle pattern images on series deformation states. The interfered images and the mean intensity images are captured simultaneously for each state. For the phase difference calculation, initial phase values are required only for first step. The initial phases are obtained by another phase analysis method. The effectiveness of the proposed method is evaluated using simulation images. As a result, it is verified that the high accuracy phase analysis is possible under the situation with variations of the mean intensity and the modulation amplitude values. Additionally, an interferometer that two images, the interfered speckle image and the mean intensity image, are captured on a single image on a single camera is constructed for measuring displacement distribution. Experiments are then performed for validating availability of the proposed method.
A fast feature selection method in polynomial echo state networks

Recently, a polynomial echo state network (PESN), an extension of the original ESN, was proposed. Its polynomial output weights were augmented by the high order information of full input features. However, there may be noisy and redundant features to construct the polynomial function as the output weights, which results in the high computational complexity and degrade the testing accuracy. To eliminate the insignificant features and reduce computational burden, a fast feature selection method in polynomial ESN (FS-PESN) is presented. Firstly, a feature selection criterion is utilized to choose the appropriate features to construct the p-order reduced polynomial function as the output weights in PESN. Then, an iterative strategy is given to reduce the training computational burden in FS-PESN. Finally, ten benchmark regression data sets experiments are done which show that the proposed approach can obtain better prediction accuracy and less testing time than the PESN.
Optimization Algorithms and Tools Applied in Agreements Negotiation

In this paper we introduce new mechanisms of dispute resolution as a helping tool in legal procedures for lawyers, mediators and judges with the objective to reach an agreement between the parties; in some situations. The primary objectives are the following: to apply algorithmic mechanisms to the solution of certain national and cross-border civil matters, including matrimonial regimes, successions and trusts, commercial law and consumer law, facilitating the agreement process among the parties; to demonstrate the efficacy of an algorithmic approach and apply it into the allocation of goods, or the resolution of issues, in disputes, leading the parties to a friendly solution before or during the trial comparison. We will focus on the algorithms used for resolving disputes that involve a division of goods between agents, e.g. inheritance, divorces and company law.
Accelerating Decoding Step in Image Captioning on Smartphones

In recent years, many efforts have been conducted to increase the accuracy of neural image captioning as one of the diverse applications of deep neural networks. Text-based image retrieval can be considered as one of the important applications of the image captioning. Moreover, improving the quality of life for visually impaired people is another crucial application of the image captioning. Accordingly, rapid and optimal implementations that can work effectively on mobile processors seems to be necessary. Despite the numerous image captioning approaches presented so far, few solutions are provided that consider the mobile computational capabilities. In this paper, we practically focused on the decoding step for the implementation of image captioning in android applications. Actually, iteration over variable lengths sequences can be performed using dynamic control flow. In other words, implementing such iterative algorithms using dynamic control flow may prevent unrolling the computation to a fixed maximum length. Using this facility will result in increased speed of the decoding routine in image captioning on smartphone devices. Experimental results on execution time validate the proposed approach.
Determining whether a given cryptographic function is a permutation of another given cryptographic function—A problem in intellectual property

Imagine that, in order to avoid patent fees, licensing agreements, or export restrictions, someone permutes the plaintext bits, ciphertext bits, or key bits of a block cipher. All security properties of the block cipher would be preserved. There are many possible such permutations (e.g. 23116.32 for the Advanced Encryption Standard, AES-256). It might seem infeasible to detect this fraud, and even harder to determine the permutation matrices used. Instead of a block cipher, it could be the compression function of a cryptographic hash, or any other cryptographic function. This paper presents an algorithm whereby this fraud could be easily detected, by means of a SAT-Solver—a standard off-the-shelf software package that solves small-to-medium sized instances of the logical satisfiability problem. This paper also presents how this problem can be modeled in a system of polynomial equations (e.g. in the context of algebraic cryptanalysis). Moreover, this problem is related to the "isomorphism of polynomials" problem and that connection is explored at length.
Syntax-Aware Attention for Natural Language Inference with Phrase-Level Matching

Natural language inference (NLI) aims to predict whether a premise sentence can infer another hypothesis sentence. Models based on tree structures have shown promising results on this task, but the performance still falls below that of sequential models. In this paper, we present a syntax-aware attention model for NLI, by which phrase-level matching between two sentences is allowed. We design tree-structured semantic composition function that builds phrase representations according to syntactic trees. We then introduce cross sentence attention to learn interaction information based on phrase-level representations between two sentences. Moreover, we additionally explore a self-attention mechanism to enhance semantic representations by capturing the context from syntactic tree. Experimental results on SNLI and SciTail datasets demonstrate that our model has the ability to model NLI more precisely and significantly improves the performance.
Logical Thinking Becomes Automatic

In the first phase of AI research, the search for general problem-solving methods was successful at least in formal logic. A mechanical procedure was given to prove the logical truth of formulas. The procedure could also be carried out by a computer program and introduced automatic proving in computer science.The basic idea is easy to understand. In algebra, letters x, y, z… are used by arithmetic operations such as add (+) or subtract (−). The letters serve as spaces (variables) to insert numbers. In formal logic, propositions are represented by variables A, B, C…, which are connected by logical connectives such as (∧), “or” (∨)′ “if-then” (→), “not” (¬). The propositional variables serve as blanks to use statements that are either true or false. For example, the logical formula A ∧ B, by using the true statements 1 + 3 = 4 for A and 4 = 2 + 2 for B, is transformed into the true statement 1 + 3 = 4 ∧ 4 = 2 + 2. In arithmetic, this leads to the true conclusion 1 + 3 = 4 ∧ 4 = 2 + 2→1 + 3 = 2 + 2. But, in general, the conclusion A ∧ B → C is not true? On the other hand, the conclusion A ∧ B → A logically generally valid, since for the insertion of any true or false statements for A and B there is always a true overall statement.