Reviewerly/exHarmony huggingface dataset:
Pitako - Recommending Game Design Elements in Cicero

Recommender Systems are widely and successfully applied in e-commerce. Could they be used for designƒ In this paper, we introduce Pitako <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixed-initiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.
Building a controllable expressive speech synthesis system with multiple emotion strengths

Emotion is considered to be an essential element in the performance of human-computer interactions. In expressive synthesis speech, it is important to generate emotional speech that reflects subtle and complex emotional states. However, there has been limited research on how to effectively synthesize emotional speech using different levels of emotion strength with intuitive control, which is difficult to be modeled effectively. In this paper, we explore an expressive speech synthesis model that can be used to produce speech with multiple emotion strengths. Unlike previous studies that encoded emotions into discrete codes, we propose an embedding vector to continuously control the emotion strength, which is a data-driven method to synthesize speech with a fine control over the emotions. Compared with the models using the retraining technique or a one-hot vector, our proposed model using an embedding vector can explicitly learn the high-level emotion strength from the low-level acoustic features. As a result, we can control the emotion strength of synthetic speech in a relatively predictable and globally consistent way. The objective and subjective evaluation tests show that our proposed model achieves state-of-the-art performance in terms of model flexibility and controllability.
A local search algorithm with reinforcement learning based repair procedure for minimum weight independent dominating set

The minimum weight independent dominating set problem (MWIDS) is a famous NP-hard combinatorial optimization problem. We herein propose a local search algorithm with reinforcement-learning-based repair procedure (LSRR). The proposed algorithm combines local search with repair procedure based on the mind of reinforcement learning. This algorithm iterates through three procedures: the greedy procedure to improve the initial solution, the local search procedure to further improve the solution, and the repair procedure to destroy the initial solution and then reconstruct a new solution. In addition, because of the particularity of the weight functions in all benchmarks, we propose three novel scoring functions. Experiments are performed on two types of graphs including random graphs and random geometric graphs. Experimental results display that LSRR outperforms the previous MWIDS algorithms significantly.
An Improved Method for Obtaining Solar Irradiation Data at Temporal High-Resolution

Solar irradiation that is received on a terrestrial surface at minor scale of an hour does not have many records, since the current solar irradiation databases generally only have data recorded on a daily (most) and hourly (some less) scale. For places where there are no records of solar irradiation, there are a lot of methods that are used to synthetically or artificially generate these data, and again they are usually methods that generate data on a daily or hourly scale. Currently, for all types of applications, especially in the field of photovoltaic solar energy, irradiation data are needed at minor scale of an hour. In this case, there are very few methods to generate such data. For this purpose, a new methodology to generate series of solar irradiation at temporal high-resolution. In this paper, it is presented on a 10-min basis. A comparative study with real data has been done and the conclusion, as it will be explained is that the proposed methodology provides very good results.
Generative image deblurring based on multi-scaled residual adversary network driven by composed prior-posterior loss

Conditional Generative Adversarial Networks (CGANs) have been introduced to generate realistic images from extremely degraded inputs. However, these generative models without prior knowledge of spatial distributions has limited performance to deal with various complex scenes. In this paper, we proposed a image deblurring network based on CGANs to generate ideal images without any blurring assumption. To overcome adversarial insufficiency, an extended classifier with different attribute domains is formulated to replace the original discriminator of CGANs. Inspired by residual learning, a set of skip-connections are cohered to transfer multi-scaled spatial features to the following high-level operations. Furthermore, this adversary architecture is driven by a composite loss that integrates histogram of gradients (HoG) and geodesic distance. In experiments, an uniformed adversarial iteration is circularly applied to improve image degenerations. Extensive results show that the proposed deblurring approach significantly outperforms state-of-the-art methods on both qualitative and quantitative evaluations.
On Empirical Methodology, Constraints, and Hierarchy in Artificial Grammar Learning

Abstract This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long‐distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns.
Video salient object detection via spatiotemporal attention neural networks

Recently, deep convolutional neural networks have been widely introduced into image salient object detection and achieve good performance in this community. However, as the complexity of video scenes, video salient object detection with deep learning models is still a challenge. The specific difficulties come from two aspects. First of all, the deep networks on image saliency detection cannot capture robust motion cues in video sequences. Secondly, as for the spatiotemporal fusing features, the existing methods simply exploit element-wise addition or concatenation, which not fully explores the contextual information and complementary correlation, thus they cannot produce more robust spatiotemporal features. To address these issues, we propose a two-stream based spatiotemporal attention neural network (STAN) for video salient object detection. We amply extract the motion information in terms of long short term memory (LSTM) network and 3D convolutional operation from optical flow-based prior and video sequences. Moreover, an attentive module is designed to integrate the different types of spatiotemporal feature maps by learning the corresponding weights. Meanwhile, in order to generate sufficient pixel-wise annotated video frames, we manually generate lots of coarse labels, which are well utilized to train a robust saliency prediction network. Experiments on the widely used challenging datasets (e.g., FBMS and DAVIS) prove that the proposed STAN has competitive performances among salient object detection methods.
Constrained Relational Topic Models

Relational topic models (RTM) have been widely used to discover hidden topics in a collection of networked documents. In this paper, we introduce the class of Constrained Relational Topic Models (CRTM), a semi-supervised extension of RTM that, apart from modeling the structure of the document network, explicitly models some available domain knowledge. We propose two instances of CRTM that incorporate prior knowledge in the form of document constraints. The models smooth the probability distribution of topics such that two constrained documents can either share the same topics or denote distinct themes. Experimental results on benchmark relational datasets show significant performances of CRTM on a semi-supervised document classification task.
Hyperstate Space Graphs for Automated Game Analysis

Automatically analysing games is an important challenge for automated game design, general game playing, and co-creative game design tools. However, understanding the nature of an unseen game is extremely difficult due to the lack of a priori design knowledge and heuristics. In this paper we formally define hyperstate space graphs, a compressed form of state space graphs which can be constructed without any prior design knowledge about a game. We show how hyperstate space graphs produce compact representations of games which closely relate to the heuristics designed by hand for search-based AI agents; we show how hyperstate space graphs also relate to modern ideas about game design; and we point towards future applications for hyperstates across game AI research.
Joint Sampling and Recovery of Correlated Sources

Sampling enables physical signals to be processed using digital hardware. When multiple signals are sampled, the spatial correlation between them may be utilized to reduce the overall reconstruction error. In this work we study joint sampling and reconstruction of multiple correlated stochastic sources, exploiting their correlation to improve recovery. We derive the achievable reconstruction error and the corresponding sampling system for arbitrary sampling rates and spectral structures. The proposed system minimizes the error when sampling below the Nyquist rate by preserving only the most dominant spatial eigenmodes aliased to each frequency. Using this characterization, we obtain sufficient conditions for error free recovery. We also discuss a distributed sampling setting, where each signal is acquired separately, while reconstruction is performed jointly. We characterize conditions under which distributed sampling performs as well as joint sampling. Our numerical results illustrate that joint sampling can achieve negligible reconstruction error using low sampling rates when the signals exhibit notable spatial correlation, and demonstrate that properly exploiting this correlation can dramatically improve reconstruction accuracy.
Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm

Aiming at the problem of ease of falling into local optimum and low solution quality when solving optimization problems, this paper proposes an adaptive hybrid cuckoo search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms.
An intelligent ensemble learning approach for energy efficient and interference aware dynamic virtual machine consolidation

Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation algorithms can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing an energy and interference aware VM consolidation algorithm which uses predictive modelling to classify workloads using their resource usage features to make more informed consolidation decisions. The use of ensemble methods plays a pivotal role for improving predictive performance for many different problems. Using recent workload data from Microsoft Azure we present a comparative analysis of several ensemble methods using state-of-the-art prediction models and propose an ensemble based VM consolidation algorithm. Our empirical results demonstrate how our approach improves energy efficiency by 34% while also reducing service violations by 77%.
Automatic Initial Boundary Generation Methods Based on Edge Detectors for the Level Set Function of the Chan-Vese Segmentation Model and Applications in Biomedical Image Processing

Image segmentation is an important problem in image processing that has a wide range of applications in medicine, biomedicine and other fields of science and engineering. During the non-learning-based approaches, the techniques based on the partial differential equations and calculus of variation have attracted a lot of attention and acquired many achievements. Among the variational models, the Chan-Vese variational segmentation is a well-known model to solve the image segmentation problem. The level set methods are highly accurate methods to solve this model, and they do not depend on the edges. However, the performance of these methods depends on the level set function and its initial boundary too much. In this paper, we propose automatic initial boundary generation methods based on the edge detectors: Sobel, Prewitt, Roberts and Canny. In the experiments, we prove that among the four proposed initial boundary generation methods, the method based on the Canny edge detector brings the highest performance for the segmentation method. By combining the proposed initial boundary generation method based on the Canny edge detector, we implement the Chan-Vese model to segment biomedical images. Experimental results indicate we obtain improved segmentation results and compare different edge detectors in terms of performance.
Learning Convolutional Neural Networks in presence of Concept Drift

Designing adaptive machine learning systems able to operate in nonstationary conditions, also called concept drift, is a novel and promising research area. Convolutional Neural Networks (CNNs) have not been considered a viable solution for such adaptive systems due to the high computational load and the high number of images they require for the training. This paper introduces an adaptive mechanism for learning CNNs able to operate in presence of concept drift. Such an adaptive mechanism follows an "active approach", where the adaptation is triggered by the detection of a concept drift, and relies on the "transfer learning" paradigm to transfer (part of the) knowledge from the CNN operating before the concept drift to the one operating after. The effectiveness of the proposed solution has been evaluated on two types of CNNs and two real-world image benchmarks.
K-Random Forests: a K-means style algorithm for Random Forest clustering

In this paper we present a novel clustering approach based on Random Forests, a popular classification and regression technique whose usability in the clustering scenario has been investigated to a lesser extent. In the clustering context, the most used class of approaches is based on the exploitation of a single Random Forest to derive a proximity measure between points, to be used with any distance-based clustering technique. On the contrary, our scheme exploits a set of Random Forests, each one devoted to model one cluster, in a spirit similar to that of the mixture models approach. These Random Forests, which provide flexible cluster descriptors, are iteratively updated using a K-means-like clustering algorithm. The proposed scheme, which we call K-Random Forests (K-RF), has been evaluated on five datasets: the obtained results suggest that it represents a valid alternative to classic Random Forest clustering algorithms as well as to other established clustering approaches.
Formation of Energy Features of the Image based on Wavelet Transform

In this paper the new approach of image analysis using energy characteristics of wavelet transform is proposed. We consider a model based on the wavelet transform, which makes it possible to form features characterizing the energy estimates of the image points and reflecting their significance. To form a model of energy characteristics, a procedure is performed based on taking into account the dependencies between the wavelet coefficients of various levels. This approach based on image representation using of energy features models can be used for contours detection, salient points detection and texture features extraction. Energy features models can be used as the basis to detect and recognize the object in various systems of information processing and control, such as systems of unmanned aerial vehicle control, distant exploration of the Earth systems, access control systems for protected objects etc.
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation

Semantic Image Interpretation is the task of extracting a structured semantic description from images. This requires the detection of visual relationships: triples 〈subject, relation, object〉 describing a semantic relation between a subject and an object. A pure supervised approach to visual relationship detection requires a complete and balanced training set for all the possible combinations of 〈subject, relation, object〉. However, such training sets are not available and would require a prohibitive human effort. This implies the ability of predicting triples which do not appear in the training set. This problem is called zero-shot learning. State-of-the-art approaches to zero-shot learning exploit similarities among relationships in the training set or external linguistic knowledge. In this paper, we perform zero-shot learning by using Logic Tensor Networks, a novel Statistical Relational Learning framework that exploits both the similarities with other seen relationships and background knowledge, expressed with logical constraints between subjects, relations and objects. The experiments on the Visual Relationship Dataset show that the use of logical constraints outperforms the current methods. This implies that background knowledge can be used to alleviate the incompleteness of training sets.
A Secure and Efficient Renewable Energy Trading Scheme Based on Blockchain in Smart Grid

Nowadays, with the diversification and decentralization of energy systems, the energy Internet makes it possible to interconnect distributed energy sources and consumers. In the energy trading market, the traditional centralized model relies entirely on trusted third parties. However, as the number of entities involved in the transactions grows and the forms of transactions diversify, the centralized model gradually exposes problems such as insufficient scalability, High energy consumption, and low processing efficiency. To address these challenges, we propose a secure and efficient energy renewable trading scheme based on blockchain. In our scheme, the electricity market trading model is divided into two levels, which can not only protect the privacy, but also achieve a green computing. In addition, in order to adapt to the relatively weak computing power of the underlying equipment in smart grid, we design a credibility-based equity proof mechanism to greatly improve the system availability. Compared with other similar distributed energy trading schemes, we prove the advantages of our scheme in terms of high operational efficiency and low computational overhead through experimental evaluations. Additionally, we conduct a detailed security analysis to demonstrate that our solution meets the security requirements.
Object Tracking Based on Meanshift and Particle-Kalman Filter Algorithm with Multi Features

Object tracking is considered to be a key and important task in intelligent video surveillance system. Numerous algorithms were developed for the purpose of tracking, e.g. Kalman Filter, particle-filter, and Meanshift. However, utilizing only one of these algorithms is considered inefficient because all single algorithms have their limitations. We proposed an improved algorithm which combines these three traditional algorithms to cover each algorithms drawbacks. Moreover we also utilized a combination of two features which are color histogram and texture to increase the accuracy. Results show that the method proposed in this paper is robust to cope with numerous issues, e.g. illumination variation, object deformation, non linear movement, similar color interference, and occlusion. Furthermore, our proposed algorithm show better results compare to other comparator algorithms.
Fast and Precise Handling of Positive Weight Cycles for Field-Sensitive Pointer Analysis

By distinguishing the fields of an object, Andersen's field-sensitive pointer analysis yields better precision than its field-insensitive counterpart. A typical field-sensitive solution to inclusion-based pointer analysis for C/C++ is to add positive weights to the edges in Andersen's constraint graph to model field access. However, the precise modeling is at the cost of introducing a new type of constraint cycles, called positive weight cycles (PWCs). A PWC, which contains at least one positive weight constraint, can cause infinite and redundant field derivations of an object unless the number of its fields is bounded by a pre-defined value. PWCs significantly affect analysis performance when analyzing large C/C++ programs with heavy use of structs and classes.para This paper presents Dea, a fast and precise approach to handling of PWCs that significantly accelerates existing field-sensitive pointer analyses by using a new field collapsing technique that captures the derivation equivalence of fields derived from the same object when resolving a PWC.para Two fields are derivation equivalent in a PWC if they are always pointed to by the same variables (nodes) in this PWC. A stride-based field representation is proposed to identify and collapse derivation equivalent fields into one, avoiding redundant field derivations with significantly fewer field objects during points-to propagation. We have conducted experiments using 11 open-source C/C++ programs. The evaluation shows that Dea is on average 7.1X faster than Pearce et al.'s field-sensitive analysis (Pkh), obtaining the best speedup of 11.0X while maintaining the same precision.