Reviewerly/exHarmony huggingface dataset:
Optimization Through Cuckoo Search with a Brief Review

Optimization is a process in which the system is being modified so that the features can work more effectively or by finding any necessity execution. This process is conducted through maximizing and minimizing the parameters that are being involved in the problem. Many successful optimization approaches are available in the recent scenario; however, this research work focuses on cuckoo search technique. A new higher level procedure optimization algorithm called cuckoo search (CS) was developed by Yang and Deb in 2009. Cuckoo search is a swarm intelligence (SI) algorithm. Swarm intelligence techniques are those techniques that are being originated from the behavior of swarm. Cuckoo search algorithm is being inspired by the bird called cuckoo. It is the inspiration of brooding parasitism of cuckoo species and implemented successfully in many areas for optimization. This paper represents a brief review on CS algorithm along with the optimization process implementing some standard test functions. This research work highlights test function optimization for the non constraint function. This work may give an inspiration to apply the CS algorithm to solve the engineering design optimization problems.
Research on User Preference Film Recommendation Based on Attention Mechanism

Due to the influence of different factors such as environment, age, and interest, everyone has a different taste and appreciation of the movie. These factors can be used to make more personalized recommendation calculations for movie recommendations, but many traditional methods do not integrate these factors well. Therefore, how to effectively extract key information from multiple directions in a recommendation system is still a challenging problem. In this paper, we use user, film, and movie scoring data as input, and use the CNN-BLSTM deep learning model that incorporates the multi-head attention mechanism as a training, and finally combine the output features to calculate user preferences for recommendation. The convolutional neural network and LSTM are used to extract the user and movie feature information from the matrix, and the multi-head attention mechanism can also extract the key information from the data. The comparative experimental analysis of different models shows that our proposed user preference model based on attention mechanism can obtain better performance than traditional extraction methods.
De-speckling Convolutional Neural Network and Classification Method for SAR Images

In real-world applications, different problems can adopt different models. Most of the existing denoising methods use the framework of deep learning, and the most commonly used denoised algorithm evaluation indicators, such as PSNR, MSE, etc., all without exception, require pictures' ground truth which is needed as a reference. However, there are few real and noise-free pictures in the field of image denoising, only the noise reduction map can be compared with the noise map, which seems to be less persuasive. Therefore, this paper proposes a new criterion for judging the denoising model. The most important thing is that this method does not require noiseless images compared to PSNR when testing. Moreover, we improved the denoising model and verified the reliability of the criterion. At the same time, we conduct statistics on the recognition rate of different types of targets, and analyze the trend of misjudgment. In this paper, the synthetic aperture radar (SAR) image dataset is used as an experimental sample, and different noise parameters are used to obtain denoising data sets with different noise levels. Then we use different denoising models such as DN-CNN to process the data set. Finally, the CNN classification model is used for screening comparison. In this paper, the experimental results show that it is feasible to use classification to judge denoising, so based on this feasibility, this paper modified the denoising network and used classification to judge. The results show that the denoising effect is better and the classification accuracy is higher, that is, the denoising and classification are a complementary relationship.
A Detailed Observation on Association Rule Mining

Association rule mining is utilized to take care of various types of area issues. These days' affiliation rule mining has been stretched out to huge information. As a result of the gigantic measure of information, a solitary way the sequential procedure isn't adequate subsequently. The most basic piece of ARM is discovering a potential relationship between things in an enormous exchange based dataset. The well-known algorithms in particular apriori and FP Growth has been identified & implemented those algorithms using the WEKA (version 3.7.10) tool.
A Novel 3D Surface Reconstruction Method with Posterior Constraints of Edge Detection

This paper proposed a novel 3D surface reconstruction method with posterior constraints of edge detection applying to general digital camera. The intrinsic parameters are calibrated with Zhang calibration method. After matching the images taken at two different orientations, the fundamental matrix and corresponding motion parameters by two different orientations are estimated, selecting optical center coordinate system of left camera as world coordinate system, and the projection matrix corresponding the two orientations is obtained. At last, the 3D coordinates of object feature point is computed and object surface is displayed with VRML technology. This system is simple, in addition, the proposed method is suit for general digital camera.
Evaluation of a Permutation-Based Evolutionary Framework for Lyndon Factorizations

String factorization is an important tool for partitioning data for parallel processing and other algorithmic techniques often found in the context of big data applications such as bioinformatics or compression. Duval’s well-known algorithm uniquely factors a string over an ordered alphabet into Lyndon words, i.e., patterned strings which are strictly smaller than all of their cyclic rotations. While Duval’s algorithm produces a pre-determined factorization, modern applications motivate the demand for factorizations with specific properties, e.g., those that minimize the number of factors or consist of factors with similar lengths. In this paper, we consider the problem of finding an alphabet ordering that yields a Lyndon factorization with such properties. We introduce a flexible evolutionary framework and evaluate it on biological sequence data. For the minimization case, we also propose a new problem-specific heuristic, Flexi-Duval, and a problem-specific mutation operator for Lyndon factorization. Our results show that our framework is competitive with Flexi-Duval for minimization and yields high quality and robust solutions for balancing where no problem-specific algorithm is available.
Genetic learning of fuzzy rule bases for multi-label classification using an iterative approach

Multi-label classification problems exist in many real world applications where to each example in the dataset can be assigned a set of target labels. This paper presents a new two-step method for genetic learning of a fuzzy rule base for multi-label classification, called IRL-MLC. The first step uses a genetic algorithm based on an iterative approach to learn a preliminary rule base where the fitness of each rule depends on the degree of firing calculated for the set of labels of each example (positive or negatively) in the dataset. The second step uses a genetic algorithm to tune weights of each fuzzy rule in the preliminary rule base where the fitness of each set of weights is the precision of the multi-label classification. Experiments are conducted on five multi-label datasets, in biology, multimedia and text domains, and the proposed method has been compared with one state-of-art method. Results provide interesting insights into the quality of the discussed novel method.
Candidate Selection-based Deep Affinity Network for Multi-object Tracking

Deep Affinity Network (DAN) is a novel approach in multi-object tracking (MOT) designed to jointly modeling object appearances and affinities end to end. But tracking accuracy of DAN tracker is greatly limited since it neglects unreliable detection. Exploiting predictions of tracks has emerged as a popular approach to tackle the task of tracking-by-detection. However, it's observed that missing detection has not been solved well enough which would significantly influence tracking accuracy. Thus, obtaining more reliable tracking candidates is concerned to further address the problem of missing detection. In this paper, we propose Candidate Selection-based Deep Affinity Network (CSDAN) tracker for MOT. It collects candidates from detection, predictions of tracks and backward tracking simultaneously so that they can complement each other in different scenarios. Moreover, we propose a deep learned candidate selection model (DCSM) with a unified scoring function suitable for CSDAN, which can well handle candidates from three sources separately and select those for data association. Experiments conducted on MOT17 benchmark demonstrate that our extensions can significantly address the unreliable detection problem in DAN tracker, and our CSDAN tracker demonstrates competitive tracking performance.
Knowledge Graph Simple Question Answering for Unseen Domains

Knowledge graph simple question answering (KGSQA), in its standard form, does not take into account that human-curated question answering training data only cover a small subset of the relations that exist in a Knowledge Graph (KG), or even worse, that new domains covering unseen and rather different to existing domains relations are added to the KG. In this work, we study KGSQA in a previously unstudied setting where new, unseen domains are added during test time. In this setting, question-answer pairs of the new domain do not appear during training, thus making the task more challenging. We propose a data-centric domain adaptation framework that consists of a KGSQA system that is applicable to new domains, and a sequence to sequence question generation method that automatically generates question-answer pairs for the new domain. Since the effectiveness of question generation for KGSQA can be restricted by the limited lexical variety of the generated questions, we use distant supervision to extract a set of keywords that express each relation of the unseen domain and incorporate those in the question generation method. Experimental results demonstrate that our framework significantly improves over zero-shot baselines and is robust across domains.
Collaborative Filtering Techniques for Recency Frequency Monetary Analysis

Recommendation engines act as a key for the e-commerce industries today. The immense growth of information on the internet and the number of users accessing the internet everyday increases the intricacy for recommendation engines. Despite all this, they should deliver the best recommendations to ensure customer satisfaction. For recommending items many algorithms have being developed that can address large-scale problems easily. Collaborative filtering has proved to be one of the best among all algorithms used, providing great achievements to e-commerce retailers today in terms of their versatility, the recommendations it provides, and the revenue generated. In this paper, a collaborative user-item filtering approach has been discussed elaborating about user-user and item-item filtering algorithms in detail.
Revisiting Population Models in Differential Evolution on a Limited Budget of Evaluations

No previous study has reported that differential evolution (DE) is competitive with state-of-the-art black-box optimizers on a limited budget of evaluations (i.e., the expensive optimization scenario). This is true even for surrogate-assisted DEs. The basic framework of DE should be reconsidered to improve its performance substantially. In this context, this paper revisits population models in DE on a limited budget of evaluations. This paper analyzes the performance of DE with five population models on the BBOB function set. Results demonstrate that the traditional synchronous model is unsuitable for DE in most cases. In contrast, the performance of DE can be significantly improved by using the plus-selection model and the worst improvement model. Results also demonstrate that DE with a suitable population model is competitive with covariance matrix adaptation evolution strategy depending on the number of evaluations and the dimensionality of a problem.
Stochastic Gaussian Function For RBF Network

In Artificial Neural Network applications, new solutions are searched for high speed and low circuit cost for high density inputs. In this study, a new Gaussian Function calculation method is presented for Radial Basis Function Network using stochastic calculation. The Gaussian Function of the Radial Basis Function Network was obtained using a Linear Finite State Machine approach. Stochastic representations of input values and centers were applied to XOR, OR and AND gates to realize simple arithmetic operations. The accuracy of the presented method depends on the bit length of the stochastic sequences. Using this method, considerable flexibility has been provided to the designer in terms of speed and hardware cost for applications with high input data. From the FPGA application results, the recommended stochastic calculation hardware resource requirement for the Gauss Function is much less than the hardware requirement of the corresponding deterministic calculation. The proposed stochastic network can be expanded to the large scale networks for complex tasks using simple hardware architectures. Simulation results and resource usage of FPGA are demonstrated in this paper.
Medical Data Classification Using Jaya Optimized ELM

The implication of machine learning approaches in healthcare issues makes the diagnosis and prognosis of cancer like deadly diseases easier and affordable. The sole aim of machine learning researchers is to design a robust classification model that can classify the medical data with high accuracy and less computational complexity. Here, Jaya optimized extreme learning machine (Jaya-ELM) is proposed for classifying medical data. To estimate the efficacy of the proposed model, two benchmark medical datasets, Lung Cancer and Breast Cancer, are collected from the UCI repository. The classification accuracy percentage is taken as a performance evaluation measure in this model. The result of this model is compared with the existing Neural Network (NN), Jaya optimized NN (Jaya-NN) and ELM models. The experimental result shows that the Jaya-ELM is significantly better than other models.
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions

Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating paths from these processes is usually an intractable problem, and typically involves time-discretization approximations. We propose an exact Markov chain Monte Carlo sampling algorithm that involves no such time-discretization error. Our sampler is applicable to the problem of prior simulation from an SDE, posterior simulation conditioned on noisy observations, as well as parameter inference given noisy observations. Our work recasts an existing rejection sampling algorithm for a class of diffusions as a latent variable model, and then derives an auxiliary variable Gibbs sampling algorithm that targets the associated joint distribution. At a high level, the resulting algorithm involves two steps: simulating a random grid of times from an inhomogeneous Poisson process, and updating the SDE trajectory conditioned on this grid. Our work allows the vast literature of Monte Carlo sampling algorithms from the Gaussian process literature to be brought to bear to applications involving diffusions. We study our method on synthetic and real datasets, where we demonstrate superior performance over competing methods. Supplementary materials for this article are available online.
ScholarFinder: Knowledge Embedding Based Recommendations using a Deep Generative Model

Bold scientific research tasks today need multi-disciplinary knowledge and interdisciplinary collaborations that require finding scholars from a particular domain with relevant knowledge. Given the variety of scholars and diversity of research tasks, finding the appropriate scholar is a critically important and challenging problem for scientific communities. Prior approaches to identify scholars use supervised learning with fixed or high-level research interest tags. Such approaches make it hard to recognize scholars with specific interests, or track their changes in research interests. Hence, there is a need to investigate suitable methods to quantify scholars' expertise knowledge for matching research tasks. In this paper, we propose a novel model viz., "ScholarFinder" that uses contextual information (abstracts or publications) for embedding a scholar's knowledge in an unsupervised learning manner. Subsequently, with pre-trained knowledge embeddings, we can perform machine learning tasks such as classification, visualization or checking whether a scholar is suitable for performing particular research tasks or not. Based on our pre-trained techniques, we also provide a novel negative sampling method to overcome the issues of missing negative samples. Using a "follow-the-money" strategy, we apply our model to a large collection of NSF (National Science Foundation) grant awards dataset collected over the last twenty years that contains more than 20,000 award records (with project abstracts and names), corresponding to 15,074 scholars who received grants. We evaluate different deep learning models to see how to use pre-trained knowledge embedding for achieving optimal performance, and how our negative sampling method improves model performance. We also compare our model with state-of-the-art baseline models (e.g., XGBoost, DNN), and our results show that the ScholarFinder model outperforms those models in terms of precision, recall, F1-score, and accuracy.
GraphLib: A Parallel Graph Mining Library for Joint Cloud Computing

Graph algorithms are widely applied in social networks, computational biology, Internet security and a broad range of complexity science. Although there are many state-of-the-art graph frameworks, few frameworks support parallel graph mining in joint cloud computing environment. In this paper, we propose GraphLib, a parallel graph mining library, based on a BSP (Bulk Synchronous Parallel) service over joint cloud computing which was proposed in our prior work. We first summarize the features of commonly-used graph mining algorithms, and present our approaches for parallelizing typical graph mining algorithms. GraphLib includes 17 parallel graph mining algorithms that can be used in 3 scenarios. We evaluate the performance of 4 typical parallel graph algorithms in GraphLib on three real-world datasets. Our parallelized algorithms can achieve sub-linear scalability.
In-Service Performance Evaluation of Longitudinal Barrier to Study Occupant Risk

Roadside safety features generally demonstrate successful performance through crash testing, which provides an assessment of occupant risk, post-impact trajectory, and structural adequacy. It has been long recognized that the performance of safety features cannot be assessed by crash testing alone and should be followed by in-service performance evaluations (ISPEs). The primary objective of any ISPE is to evaluate occupant risk under real-world field conditions. A secondary objective may be to determine which factors influence performance (e.g., maintenance, installation, hardware design, etc.). This paper assembled available data from both the literature and the states of Ohio, Pennsylvania, and Tennessee to demonstrate the various data sources readily available to answer the primary ISPE question of occupant risk. While much more can be done with the data available from each of these states, this paper is limited to a review of occupant risk. This paper has shown that there is a very large amount of data very easily accessible for an ISPE. While some states maintain an inventory of safety features, some do not, and additional information was needed to identify the feature and proceed with the evaluation.
Characterising Futuring Strategies for Biodiverse Speculative Design and Systems Design

In this paper, it is proposed that new tools and techniques for speculative design practice are needed to guarantee the biodiversity of global systems, and that they can be based in the non-normative strategies of futuring that exist within culturally diverse and ecologically-engaged communities. Furthermore, these tools and techniques can be used to build an ecology-centred practice of speculative design for community action. Taking a research through design approach that draws on critical theories of ecosophy, queer ecology and queer futures, the presented research, as a part of an ongoing doctoral work, aims to characterise the futuring strategies of such communities through participant and desk-based qualitative research. Preliminary reflections are that the communitarian and ecological future-making strategies described in theory are present in material and imaginative practices. Such strategies are multi-faceted and richly contextual and resonate across theoretical, practical and speculative domains. Moreover, these approaches possess common and recurring aspects of care-taking, non-linear temporality, relationality, storytelling, and playfulness.
A Noise Rejection Mechanism for pLSA-induced Fuzzy Co-clustering

Noise fuzzy clustering is a useful scheme for analyzing intrinsic data structures through robust estimation of fuzzy c-partition. In this paper, a novel noise rejection scheme for improving fuzzy co-clustering is proposed, which is useful in such cooccurrence information analysis as document classification, where multi-topic co-cluster structure extraction by probabilistic latent semantic analysis (pLSA) is achieved through rejection of the influences of noise objects. Supported by the uniform noise distribution concept in noise fuzzy clustering, a noise cluster having uniform item occurrence probabilities is newly introduced into the pLSA-induced fuzzy co-clustering model. Several numerical experiments demonstrate the advantage of tuning the noise sensitivity of the pLSA-induced objective function.
Application of Deep Association for Real Time Pedestrian Tracking

Multiple object tracking plays an important role in computer vision and video analysis. There are many problems with object tracking, such as appearance changes, distance from the camera, occlusion, moving too fast, and so on. In this paper, we combine the pre-trained pedestrian association model with a pedestrian's appearance and moving model to achieve better tracking performance. We trained a neural network base on a large dataset of pedestrian classification, together with the moving model of an object's position, velocity, and acceleration, to help us predict the trajectory more accurately. To demonstrate the performance of the proposed method, the Multiple Object Tracking (MOT) benchmark was used. Experimental results showed the proposed method achieves reasonable tracking results.