Reviewerly/exHarmony huggingface dataset:
Optimal search algorithm in a big database using interpolation–extrapolation method

Fast data search is an important element of big data in the modern era of internet of things, cloud computing, and social networks. Search using traditional binary-search algorithm can be accelerated by employing an interpolation search technique when the data is regularly distributed. In this work, the interpolation search is investigated in which the search results provided unexpected sluggish progress during a search in a large database due to the irregular distribution of data. Irregular distribution of data does not allow the interpolation to make a good prediction about the location of the search item. To overcome this issue, an interpolation–extrapolation search (IES) method is proposed where the interpolation method is integrated with an extrapolation method that balances the lower and upper bounds of the search interval. The proposed method provides faster convergence property than the binary search and the interpolation method. Hence, the proposed IES method provides a faster search for items in a big database.
What Types Should Not Be†

ABSTRACT In a series of papers Ladyman and Presnell raise an interesting challenge of providing a pre-mathematical justification for homotopy type theory. In response, they propose what they claim to be an informal semantics for homotopy type theory where types and terms are regarded as mathematical concepts. The aim of this paper is to raise some issues which need to be resolved for the successful development of their types-as-concepts interpretation.
Visual Object Tracking via Graph Convolutional Representation

CNN-based trackers are easily interfered by insufficient feature learning, causing model drift. In recent years, graph convolutional networks (GCNs) have been widely used for the representation of graph data in the fields of machine learning and computer vision. In our work, we employ a GCN module to learn structural features for visual tracking. First, we utilize a dual path network to extract heterogeneous features. Then, we adopt a GCN module to construct features to have structured information. Finally, we connect all the features and use the attention mechanism to adaptively select features. Extensive experiments on two benchmark datasets validate the effectiveness of our approach.
Comparing Large-Scale Global Optimization Competition winners in a real-world problem

The optimization of thousands of variables, Large-Scale Global Optimization, is a research topic that is obtaining more and more attention by its applications in engineering and medical problems. In order to design evolutionary algorithms for these problems, several specific competitions have been organized, using benchmarks such as the ones proposed in CEC'2010 and CEC'2013, trying to simulate realistic features of real-world problems. Several algorithms have been proposed, some of them being very competitive on these benchmarks, especially during the last years. However, all of them were tested only on those artificial benchmarks, so there are no guarantees that they would obtain good performance in more realistic problems. In this paper, we select the best algorithms in these competitions to optimize a real-world problem, an electroencephalography (EEG) optimization problem. The new benchmark contains noisy problems and an increasing number of variables (up to 5000) compared to synthetic benchmarks (limited to 1000 variables). Results show that, although the fitness obtained by the majority of the algorithms is the same, the processing time strongly depends on the algorithm under consideration. The optimization time for a fixed number of fitness evaluations varies, in the most complex problems, from 3 hours to around 18 minutes, being MOS-2013 the fastest algorithm. However, if we focus our attention on the time needed to reach the best-known solution, SHADEILS becomes the fastest algorithm (with a maximum of three minutes). In our opinion, this should encourage researchers to continue working in more scalable and efficient algorithms for large-scale global optimization.
Traffic Sign Detection for Panoramic Images Using Convolution Neural Network Technique

This research presents a method for panoramic traffic sign images detection for regulatory signs and guide signs especially blue and green sign. A new approach for detecting the signs inside a large panoramic image was considered. A convolution neural network technique was used as tools for detecting. In addition, the steps required are the technique used in conjunction with the convolution neural network technique by using Tensorflow training to improve the accuracy of traffic sign detection. Moreover, to improve the accuracy of image detection, some image processing technique was added. For example, adding brightness to an image. From the experimental results, detection of traffic signs from panoramic images (360°) by using trained convolution neural network model to improve a traffic sign detection, the accuracy from panoramic images (360°) is better than the traditional model.
A Frequentist Inference Method Based on Finite Bivariate and Multivariate Beta Mixture Models

Modern technological improvement, revolutionized computers, progress in scientific methods, and other related factors led to generate a massive volume of structured and unstructured data. Such valuable data has potential to be mined for information retrieval and analyzed computationally to reveal patterns, trends, and associations that lead to better decisions and strategies. Thus, machine learning and specifically, unsupervised learning methods have become the topic of interest of much recent researches in data engineering. Finite mixture models as unsupervised learning methods, namely clustering, are considered as capable techniques for discovery, extraction, and analysis of knowledge from data. Traditionally Gaussian mixture model (GMM) has drawn lots of attention in previous literature and has been studied extensively. However, other distributions demonstrate more flexibility and convenience in modeling and describing data. The novel aspect of this work is to develop a framework to learn mixture models based on bivariate and multivariate Beta distributions. Moreover, we tackle simultaneously the problems of parameters estimation, cluster validation, or model selection which are principal challenges in deployment of mixture models. The effectiveness, utility, and advantages of the proposed method are illustrated through extensive empirical results using real datasets and challenging applications involving image segmentation, sentiment analysis, credit approval, and medical inference.
Transaction Log Analysis within a Bento Discovery System

Academic libraries have introduced bento-style discovery systems to address identified user issues with web-scale discovery systems. The bento systems partition search results into discrete areas on the screen grouped by content format type and/or local service results and address key user needs such as known-item search and streamlined full-text delivery. This poster reports on a transaction log study on the bento discovery system employed at the University of Illinois at Urbana-Champaign Library. The analysis looks at known-item vs exploratory searches, search argument characteristics, and user clickthrough behaviors.
All-in-One Underwater Image Enhancement Using Domain-Adversarial Learning

Raw underwater images are degraded due to wavelength dependent light attenuation and scattering, limiting their applicability in vision systems. Another factor that makes enhancing underwater images particularly challenging is the diversity of the water types in which they are captured. For example, images captured in deep oceanic waters have a different distribution from those captured in shallow coastal waters. Such diversity makes it hard to train a single model to enhance underwater images. In this work, we propose a novel model which nicely handles the diversity of water during the enhancement, by adversarially learning the content features of the images by disentangling the unwanted nuisances corresponding to water types (viewed as different domains). We use the learned domain agnostic features to generate enhanced underwater images. We train our model on a dataset consisting images of 10 Jerlov water types. Experimental results show that the proposed model not only outperforms the previous methods in SSIM and PSNR scores for almost all Jerlov water types but also generalizes well on real-world datasets. The performance of a high-level vision task (object detection) also shows improvement using enhanced images with our model.
Two Simple Tricks for Fast Cache-Aware Parallel Particle Swarm Optimization

Particle Swarm Optimization is an example of a trivially parallelizable algorithm where good performance gains can be achieved through the use of a few OpenMP pragmas. Writing an efficient parallel PSO algorithm, however, is much more challenging because although particle updates can occur independently, they rely on a shared global state (the globally best particle). The difficulty of maintaining this global state can be seen in the large body of work studying the parallelization of PSO - almost uniformly these algorithms rely on a global synchronization step, which can result in idle cores and reduced parallel efficiency. In this work, we explore two techniques for implementing a fast, cache-aware parallel PSO algorithm: batching the creation of the random weights and reducing critical section contention via a relaxed consistency guarantee. Our technique shows impressive performance improvements over prior work, seeing more than 60% speed-up over naive parallelization and more than 10% speed-up over the cache-aware algorithm. This speed comes at a cost; while our method quickly reaches an approximate solution, it struggles in environments requiring a high level of resolution. Despite these trade-offs, our method is both easy to understand and implement and is widely transferable to other swarm intelligence algorithms.
Vision-Based Target Objects Recognition and Segmentation for Unmanned Systems Task Allocation

This paper investigates the potential of deep learning methods to detect and segment objects from vision sensors mounted on autonomous robots to support task allocation in unmanned systems. An object instance segmentation framework, Mask R-CNN, is experimentally evaluated and compared with previous architecture, Faster R-CNN. The former model adds an object mask prediction branch in parallel with the existing branches for target objects location and class recognition, which represents a significant benefit for autonomous robots navigation. A comparison of performance between the two architectures is carried over scenes of varying complexity. While both networks perform well on recognition and bounding box estimation, experimental results show that Mask R-CNN generally outperforms Faster R-CNN, particularly because of the accurate mask prediction generated by this network. These results support well the requirements imposed by an automated task allocation mechanism for a group of unmanned vehicles.
Non-Uniformly Sound Certificates with Applications to Concurrent Zero-Knowledge

We introduce the notion of non-uniformly sound certificates: succinct single-message (unidirectional) argument systems that satisfy a "best-possible security" against non-uniform polynomial-time attackers. In particular, no polynomial-time attacker with s bits of non-uniform advice can find significantly more than s accepting proofs for false statements. Our first result is a construction of non-uniformly sound certificates for all $$\mathbf{NP }$$ in the random oracle model, where the attacker's advice can depend arbitrarily on the random oracle. We next show that the existence of non-uniformly sound certificates for $$\mathbf{P }$$ (and collision resistant hash functions) yields a public-coin constant-round fully concurrent zero-knowledge argument for $$\mathbf{NP } $$ .
Studying the Effect of Optimizing Weights in Neural Networks with Meta-Heuristic Techniques

Meta-heuristic algorithms provide derivative-free solutions to optimize complex problems. Back-propagation Neural Network (BP) algorithm is one of the most commonly used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. This paper proposed a new meta-heuristic search algorithm, called cuckoo search (CS), based on cuckoo bird’s behavior to train back propagation (BP), Elman Recurrent Neural Network (RNN), and Levenberg Marquardt (LM) algorithms to achieving fast convergence rate and to avoid local minima problem. The performances of the proposed hybrid Cuckoo Search algorithms are compared with artificial bee colony using BP algorithm, and other hybrid variant. Specifically on Iris and 7-Bit parity datasets are used. The simulation results show that the hybrid Cuckoo Search show better performances than the other hybrid technique.
Evolutionary Planning for Multi-User Multi-Task Missions

The problem of mission planning is to enable robots solve complex missions with Boolean and temporal operators in the mission specification. Typically, missions are specified using a Linear Temporal Logic Formulation and solved by using a model verification approach which has an exponential complexity. Given a language which is polynomial verifiable, evolutionary paradigm of mission planning can enable probabilistic optimality and probabilistic completeness, thus enabling the use of solvers for a very high number of variables, which is impossible to do using the model verification techniques. This paper motivates the heuristic of a mission consisting of a number of tasks, such that each task is a complex instruction given by a user, while many such users share a robot. The heuristic is used to generate a near-optimal solution of tasks that can then be fused optimally by a Dynamic Programming approach to make the solution of the mission. However, the problem is not decomposable and optimal solutions of tasks do not result in an optimal solution to mission. Hence a 2step algorithm is used. The first step computes a near-optimal solution of the tasks. The second step does a full Genetic Algorithm search to generate task solutions that eventually fused by a Dynamic Programming approach produce an optimal mission solution. Comparative analysis is done with numerous baselines and the proposed approach is experimentally shown to perform better than all baselines. The experiments are also done on the Pioneer LX robot using the Robot Operating System framework.
Enhancing k-Nearest Neighbors through Learning Transformation Functions by Genetic Programming

The k-nearest neighbors algorithm (kNN) is renowned for solving classification tasks. The notion of kNN is to seek similar data instances in the dataset as prediction reference, for which the similarity between instances is ordinarily measured by Euclidean distance. Recently, some studies propose problem-tailored distance metrics to improve the classification performance of kNN. In this paper, we utilize genetic programming to learn the transformation function, which interprets the relationship of two data instances into a scalar differential. The differential of data pairs indicates the dissimilarity between two instances. This study considers two forms of transformation functions. Experimental results show the transform functions learned by GP can effectively enhance the performance of kNN.
Drift Detection Using SVM in Structured Object Tracking

This paper extends structured learning to detect object tracking failures or inaccuracies and reinitialize itself when needed using object segmentation. First, we use binary support vector machines to develop a technique to detect tracking failures (drifts or inaccuracies) by monitoring internal variables of our baseline tracker. We leverage learned examples from our baseline tracker to train the employed binary support vector machines. Second, we propose an automated method to re-initialize the tracker to recover from tracking failures by integrating an active contour based object segmentation and using particle filter to sample bounding boxes for segmentation. We subjectively and objectively show that our extended method outperform classical structured learning approach under various tracking challenges.
Deep Reinforcement Learning with Adaptive Update Target Combination

Abstract Simple and efficient exploration remains a core challenge in deep reinforcement learning. While many exploration methods can be applied to high-dimensional tasks, these methods manually adjust exploration parameters according to domain knowledge. This paper proposes a novel method that can automatically balance exploration and exploitation, as well as combine on-policy and off-policy update targets through a dynamic weighted way based on value difference. The proposed method does not directly affect the probability of a selected action but utilizes the value difference produced during the learning process to adjust update target for guiding the direction of agent’s learning. We demonstrate the performance of the proposed method on CartPole-v1, MountainCar-v0, and LunarLander-v2 classic control tasks from the OpenAI Gym. Empirical evaluation results show that by integrating on-policy and off-policy update targets dynamically, this method exhibits superior performance and stability than does the exclusive use of the update target.
Strategies for Improving Single-Head Continual Learning Performance

Catastrophic forgetting has long been seen as the main obstacle to building continual learning models. We argue in this paper that an equally challenging characteristic of the continual learning framework is that data are never completely available at the same time, making it difficult to learn joint conditional distributions over them. This is most evident in the usually large gap between single-head and multi-head performance of continual learning models. We propose in this paper two strategies to improve performance of continual learning models, particularly in the single-head framework and for image classification tasks. First, we argue that learning multiple binary classifiers, rather than a single multi-class classifier, for each presentation of data is more consistent with the single-head framework. Moreover, we argue that auxiliary, unlabelled data can be used in tandem with this approach to slow the decay in performance of these binary classifiers over time.
Static Hand Gesture Recognition for Human Robot Interaction

Human-robot interaction is making a robot understanding human action, working and sharing the same space with the human. To achieve this, the communication between human and robot must be effective. The most used ways in this communication are the vocal and the body gestures such as full body actions, hand and arm gestures or head and facial gestures. Hand gestures are used as a natural and effective way of communicating between human and robot. The difference in hand size and posture, light variation and background complexity makes the hand gesture recognition to be a challenging issue. Different algorithms have been proposed and they gave better results. Yet, some problems such as poor computational scalability, trivial human intervenes and slow speed learning are still appearing in this field. In this paper, these issues are solved using a combination of three features extraction methods: Haralick texture, Hu moments, and color histogram and extreme learning machine (ELM) method for classification. The ELM results were compared to that of K-Nearest Neighbors, Random Forest Classifier, Linear Discriminant Analysis, Convolution Neural Networks and these experiment was evaluated on National University of Singapore (NUS) dataset II. ELM performed better than any of the above algorithms with an accuracy of $$98.7 \%$$ and time used of 109.7 s which is the proof of the satisfactory of the model.
Exploring Usage Patterns of a Large-Scale Digital Library

Log file (LF) analysis has been widely used in Interactive Information Retrieval (IIR) as a means to uncover information about users and their behaviour when searching for information. Various studies targeted small-scale IIR systems; Online Public Access Catalogue (OPAC) is an example. Instead this research considers a large-scale digital library that services heterogeneous population and provides divers digital contents. The aim of this study is to explore the hidden usage patterns (UP) of a large-scale digital library (DL) by analysing the LFs. A dataset of 28 million records obtained from the RERO Doc DL was adopted for our study. We identify and discuss three main distinct UPs: item seekers, navigators, and searchers. The UPs were characterised by session duration, session starting points, discovering content actions, functions used, and determination session points. We conclude that LF reveals valuable information that might assist DL developers and designers to improve user experiences.
Total Optimization of Energy Networks in Smart City by Cooperative Coevolution using Global-best Brain Storm Optimization

This paper proposes total optimization of energy networks in a smart city (SC) by cooperative coevolution using global-best brain storm optimization (CCGBSO). The smart city problem is one of mixed integer nonlinear programming (MINLP) problems. Therefore, various evolutionary computation methods such as differential evolutionary particle swarm optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (GBSO) have been applied to the problem. However, quality of solution is still required to be improved. Cooperative Cooperation has a possibility to improve solution quality of large scale optimization problems such as the SC problem and this paper proposes a new cooperative coevolution algorithm, CCGBSO. The results of the proposed CCGBSO based method are verified to be the most improved comparing with those of the conventional DEEPSO, BSO, MBSO, and GBSO based methods.
Some Polynomial Sequence Relations

We give some polynomial sequence relations that are generalizations of the Sury-type identities. We provide two proofs, one based on an elementary identity and the other using the method of generating functions.
Fusing creative operations into evolutionary computation for composition: From a composer’s perspective

This paper presents a study on fusing creative operations into evolutionary computation for music composition, as an attempt to provide a collaborative creation environment between human and machine and to investigate into the mind process during music creation. A framework based on the paradigm of evolutionary computation is proposed to incorporate key mechanisms observed in the creation process and implemented to work on evolving abstract musical ideas and thoughts. The developed software framework is released as open source. The collaborative creation process, involving the framework, the composer, and the performer, is presented and discussed, and the results of this study also include a composed music work for unaccompanied cello, played by a renowned cellist.