Reviewerly/exHarmony huggingface dataset:
Chaotic Cuckoo Search Algorithm for Solving Unmanned Combat Aerial Vehicle Path Planning Problems

This paper applies an improved Cuckoo Search algorithm, named Chaotic CS algorithm, to solve Unmanned Combat Aerial Vehicle (UCAV) path planning problems. A circle-type chaotic map for generating chaotic sequences is used to specify the scaling factor (() of step size and fraction probability (pa) of abandonment for host nests formulated in the Original CS algorithm. The advantage of using Chaotic CS algorithm can dynamically change the parameters of ( and pa by using the chaotic sequences over the course of iterations, resulting in an improvement for searching performance to find out the global best solution. The enhanced CS algorithm shows flexible and robust capabilities to optimize complex and multimodal objective functions by evaluating standard benchmark functions. Furthermore, the Chaotic CS algorithm is applied to solve complex design problem. Two scenarios of UCAV path planning problems are carried out for the practical applications. The simulation results indicate that the Chaotic CS algorithm can efficiently be used for computing optimal flight path of UCAV.
An Improved Collaborative Filtering Recommendation Algorithm

Collaborative Filtering Recommendation (CFR) is the earliest proposed and widest used method in recommendation system. It can not only find out what people are interested in at present but also mine out the implicit interest information. As time goes by and data volumes grow, recommendations effect can be improved significantly. Therefore, the collaborative filtering recommendation is one of the most popular recommendation technologies in the electronic commerce recommendation system. But the algorithm also faces some problems such as cold start, sparse, real-time and scalability etc. problems affecting the quality of recommendation. An improved collaborative filtering algorithm based on properties is presented in this paper to improve the utilization of data resources and decrease the sparse degree of matrix. Meanwhile, in order to reduce the similarity calculation quantity of work and filter dirty data effectively, the collaborative filtering algorithm based on clustering and singular value decomposition is put forwards to improve the effect of collaborative filtering algorithm. At last the effectiveness of the improved collaborative algorithm in this paper is verified through experiments.
Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness

In this paper we propose a novel augmentation technique that improves not only the performance of deep neural networks on clean test data, but also significantly increases their robustness to random transformations, both affine and projective. Inspired by ManiFool, the augmentation is performed by a line-search manifold-exploration method that learns affine geometric transformations that lead to the misclassification on an image, while ensuring that it remains on the same manifold as the training data. This augmentation method populates any training dataset with images that lie on the border of the manifolds between two-classes and maximizes the variance the network is exposed to during training. Our method was thoroughly evaluated on the challenging tasks of fine-grained skin lesion classification from limited data, and breast tumor classification of mammograms. Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.
LORI: A Learning-to-Rank-Based Integration Method of Location Recommendation

Location recommendation method is an important application in a location-based social network. At present, it is a trend to integrate different recommendation methods since they have their own advantages in capturing different preferences of users and an integrated method can generally provide a better performance than every individual. However, the existing integration policies do not learn user preferences in their integration processes so that they cannot make full use of the advantage of each method. Therefore, we propose a novel integration method: learning-to-rank-based integration . In our method, a confidence coefficient is applied for each user in the integration process, and these coefficients can well optimize recommendation performance. A learning-to-rank-based algorithm is designed to train the confidence coefficients. A group of experiments are done on a real large-scale check-in data set, and the results demonstrate that our method outperforms the state-of-the-art ones.
Preservation of confidential information privacy and association rule hiding for data mining: a bibliometric review

In this era of technology, data of business organizations are growing with acceleration. Mining hidden patterns from this huge database would benefit many industries improving their decision-making processes. Along with the non-sensitive information, these databases also contain some sensitive information about customers. During the mining process, sensitive information about a person can get leaked, resulting in a misuse of the data and causing loss to an individual. The privacy preserving data mining can bring a solution to this problem, helping provide the benefits of mined data along with maintaining the privacy of the sensitive information. Hence, there is a growing interest in the scientific community for developing new approaches to hide the mined sensitive information. In this research, a bibliometric review is carried out during the period 2010 to 2018 to analyze the growth of studies regarding the confidential information privacy preservation through approaches addressed to the hiding of association rules of data.
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence

Conservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can help to reduce the number of false positive and false negative decisions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others.
Deep Learning for Database Mapping and Asking Clarification Questions in Dialogue Systems

A dialogue system will often ask followup clarification questions when interacting with a user if the agent is unsure how to respond. In this new study, we explore deep reinforcement learning RL for asking followup questions when a user records a meal description, and the system needs to narrow down the options for which foods the person has eaten. We build off of prior work in which we use novel convolutional neural network models to bypass the standard feature engineering used in dialogue systems to handle the text mismatch between natural language user queries and structured database entries, demonstrating that our model learns semantically meaningful embedding representations of natural language. In this new nutrition domain, the followup clarification questions consist of possible attributes for each food that was consumed; for example, if the user drinks a cup of milk, the system should ask about the percent milkfat. We investigate an RL agent to dynamically follow up with the user, which we compare to rule-based and entropy-based methods. On a held-out test set, assuming the followup questions are answered correctly, deep RL significantly boosts top five food recall from 54.9% without followup to 89.0%. We also demonstrate that a hybrid RL model achieves the best perceived naturalness ratings in a human evaluation.
Correlation filter tracker with siamese: A robust and real-time object tracking framework

Correlation filter (CF) based trackers have shown promising performance in object tracking. However, both the accuracy and efficiency of existing CF based trackers are limited. In this paper, we propose a robust and real-time object tracking framework, based on a canonical CF tracker. Specifically, we first propose an adaptive model update strategy for preventing the tracker from being contaminated when the target is occluded or disappears in sight. Then, we propose a multimodal validation method for reducing tracking failures, which is capable of generating potential candidates adaptively and evaluating them with a siamese network. In addition, we build a template library online to augment the discriminability of the employed siamese network. Experimental results over OTB-13 and OTB-15 benchmark datasets demonstrate that our method outperforms state-of-the-art ones. Especially, on OTB-15, our method not only achieves a relative gain of 12.3% in AUC score but also runs at a high tracking speed, i.e., 58.3 frames per second, in comparison with the baseline CF tracker.
Deep Feature Extraction for Pap-Smear Image Classification

Cervical cancer is one of the major public health problems in the world. Even if it is one of the most preventable cancers, the early screening with reliable Pap-smear test is the key to curing. Based on the morphology and the texture of the cervix cells, Cytopathologists rely on hand-crafted features to determine whether a cellule is normal or abnormal. In medical imaging, deep learning can generate features that are more sophisticated and difficult to elaborate in descriptive means. These relevant features allow improving the classification's accuracy. In this study, we use deep neural networks to extract features from Pap-smear images and provide these extracted features as inputs for optimized MLP classifier. We study the ability of the four different pre-trained models as feature extractors toward classifying Pap-smear images. Experiments performed on the DTU/HERLEV Database and comparison show that for the Pap-smear image classification, the ResNet50 exceeds the VGGs and the InceptionV3 by 15% of accuracy.
Convolution Accelerator Designs Using Fast Algorithms

Convolutional neural networks (CNNs) have achieved great success in image processing. However, the heavy computational burden it imposes makes it difficult for use in embedded applications that have limited power consumption and performance. Although there are many fast convolution algorithms that can reduce the computational complexity, they increase the difficulty of practical implementation. To overcome these difficulties, this paper proposes several convolution accelerator designs using fast algorithms. The designs are based on the field programmable gate array (FPGA) and display a better balance between the digital signal processor (DSP) and the logic resource, while also requiring lower power consumption. The implementation results show that the power consumption of the accelerator design based on the Strassen–Winograd algorithm is 21.3% less than that of conventional accelerators.
Automated Keyword Extraction using Support Vector Machine from Arabic News Documents

Keyword extraction is an indispensable step for many natural language processing and information retrieval applications such as; text summarization and search engine optimization. Keywords hold the most important information describing the content of a document. With the increasing volume and variety of unlabeled documents on the Internet, the need for automatic keyword extraction methods increases. Even though keyword extraction can be used in many applications, Arabic research in the field still lacking. In this paper, a supervised learning technique that uses statistical features and Support Vector Machine classifier was implemented and applied to extract the keywords from Arabic news documents. The proposed supervised learning approach achieved a precision of 0.77 and a recall of 0.58.
Augmented reality-based vision-aid indoor navigation system in GPS denied environment

High accuracy localization and user positioning tracking is critical in improving the quality of augmented reality environments. The biggest challenge facing developers is localizing the user based on visible surroundings. Current solutions rely on the Global Positioning System (GPS) for tracking and orientation. However, GPS receivers have an accuracy of about 10 to 30 meters, which is not accurate enough for augmented reality, which needs precision measured in millimeters or smaller. This paper describes the development and demonstration of a head-worn augmented reality (AR) based vision-aid indoor navigation system, which localizes the user without relying on a GPS signal. Commercially available augmented reality head-set allows individuals to capture the field of vision using the front-facing camera in a real-time manner. Utilizing captured image features as navigation-related landmarks allow localizing the user in the absence of a GPS signal. The proposed method involves three steps: a detailed front-scene camera data is collected and generated for landmark recognition; detecting and locating an individual’s current position using feature matching, and display arrows to indicate areas that require more data collects if needed. Computer simulations indicate that the proposed augmented reality-based vision-aid indoor navigation system can provide precise simultaneous localization and mapping in a GPS-denied environment.
Parametric NCP-Based Recurrent Neural Network Model: A New Strategy to Solve Fuzzy Nonconvex Optimization Problems

The present scientific attempt is devoted to investigating the fuzzy nonconvex optimization problems (NCOPs) utilizing the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first study on finding a solution for fuzzy NCOP using RNN models. For this purpose, the original problem is reformulated into an mth power form, the interval, and then the weighting problem. Then, the Karush-Kuhn-Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Besides, the Lyapunov stability and the global convergence of the RNN model are proved. Finally, several illustrative examples are given to demonstrate the performance of this approach. The obtained results are compared with previous approaches for solving fuzzy NCOP.
Status detection from spatial-temporal data in pipeline network using data transformation convolutional neural network

With the scale expansion and structural upgrading of pipeline network, the detection methods based on both ends of the pipeline pressure have appeared the limitations of judging pipeline status in the multi-mode and complex network system. To overcome the limitation of early methods, a pipeline network status detection method based on data transformation convolutional neural network (DT-CNN) is proposed in this paper. Firstly, the difference among the eigenvalue distribution of data covariance matrices is calculated to detect the pipeline status by Kullback-Leibler divergence (KLD). If the eigenvalue distribution deviates from the normal status, the KLD will exceed the given threshold. Furthermore, an improved CNN model is proposed to judge pipeline status by converting the largest eigenvectors of data covariance matrices to extract features. The effectiveness of the proposed detection method is demonstrated through the simulation results of a practical pipeline network.
Shared Linear Encoder-Based Multikernel Gaussian Process Latent Variable Model for Visual Classification

Multiview learning has been widely studied in various fields and achieved outstanding performances in comparison to many single-view-based approaches. In this paper, a novel multiview learning method based on the Gaussian process latent variable model (GPLVM) is proposed. In contrast to existing GPLVM methods which only assume that there are transformations from the latent variable to the multiple observed inputs, our proposed method simultaneously takes a back constraint into account, encoding multiple observations to the latent variable by enjoying the Gaussian process (GP) prior. Particularly, to overcome the difficulty of the covariance matrix calculation in the encoder, a linear projection is designed to map different observations to a consistent subspace first. The obtained variable in this subspace is then projected to the latent variable in the manifold space with the GP prior. Furthermore, different from most GPLVM methods which strongly assume that the covariance matrices follow a certain kernel function, for example, radial basis function (RBF), we introduce a multikernel strategy to design the covariance matrix, being more reasonable and adaptive for the data representation. In order to apply the presented approach to the classification, a discriminative prior is also embedded to the learned latent variables to encourage samples belonging to the same category to be close and those belonging to different categories to be far. Experimental results on three real-world databases substantiate the effectiveness and superiority of the proposed method compared with state-of-the-art approaches.
PathoGenius VR

Extensive training is mandatory for medical students during their academic studies. However, the shortage of training personnel, patients' safety, scarcity of equipment are the key limitations that hinder the training process. To address these challenges, we built PathogeniusVR, a virtual reality application for training physicians to interact and diagnose patients. We also conducted a preliminary study on 8 participants to collect their initial feedback about our prototype. Our results suggest that PathogeniusVR is immersive and more entertaining than case study diagnosing.
A Debris Clearance Robot for Extreme Environments

The need for nuclear decommissioning is increasing globally, as power stations and other facilities utilising nuclear reaches the end of their operational life. Currently the majority of decommissioning tasks are carried out by workers in protective air fed suits, which is slow, expensive and dangerous. The work presented here is the early stages in the development of a flexible mobile manipulator platform, combining a Clearpath Husky, a Universal UR5 manipulator and various sensors. The system will be used for research specifically in the area of exploration of contaminated environments, map building to aid in task planning, and also to investigate manipulation for waste sorting. The aim is to develop a system that can, in the short term, be used in real world tasks but longer term function as a research platform to allow continued research and development. As well as developing a hardware platform, a detailed simulation model is also being developed to allow testing of algorithms in simulation before being deployed on hardware. The use of the simulation model for operator training is also an area that will be investigated in the future. This article focuses on the planned work for developing the system, as well as discussing the progress on the simulation model.
Confined teaching-learning-based optimization with variable search strategies for continuous optimization

The well-known optimization approach teaching-learning-based optimization (TLBO) is modified by using a confined TLBO (CTLBO) to eliminate the teaching factor. Different settings are suggested for various types of search factors, as they are used for different purposes. In addition, crossover frequencies are introduced into TLBO to prevent premature convergence. Furthermore, eight new mutation strategies are introduced to the teacher phase, and four new mutation strategies to the student phase to enhance the algorithm's exploitation and exploration capabilities. The experimental results show that the proposed versions, especially those that either adopted low crossover frequencies or implemented various mutation strategies, performed particularly well in achieving fast convergence speeds in the early stages, reaching convergence precision at lower cost, arriving at convergence plateaus at either lower cost or higher precision, handling tests of composition functions well, and achieving competitive performance on CEC2015 test problems.
Understanding the order of agile practice introduction: Comparing agile maturity models and practitioners’ experience

Context: Agile maturity models (AMMs) suggest that agile practices are introduced in a certain order. However, whether the order of agile practice introduction as suggested in the AMMs is relevant in industry has not been evaluated in an empirical study. Objectives: In this study, we want to investigate: (1) order of agile practice introduction mentioned in AMMs, (2) order of introducing agile practices in industry, and (3) similarities and differences between (1) and (2). Methods: We conducted a literature survey to identify strategies proposed by the AMMs. We then compared the AMMs' suggestions to the strategies used by practitioners, which we elicited from a survey and a series of interviews from an earlier study. Results: The literature survey revealed 12 AMMs which provide explicit mappings of agile practices to maturity levels. These mappings showed little agreement on when practices should be introduced. Comparison of the AMMs' suggestions and the empirical study revealed that the guidance suggested by AMMs are not aligned with industry practice. Conclusion: Currently, AMMs do not provide sufficient information to guide agile adoption in industry. Our results suggest that there might be no universal strategy for agile adoption that works better than others.
Expert2Vec: Experts Representation in Community Question Answering for Question Routing

Communities of Question Answering (CQAs) are rapidly growing communities for exchanging information in the form of questions and answers. They rely on the contributions of users (i.e., members of the community) who have appropriate domain knowledge and can provide helpful answers. In order to deliver the most appropriate and valuable answers, identification of such users (experts) is critically important. However, a common problem faced in CQAs is that of poor expertise matching, i.e., routing of questions to inappropriate users. In this paper, we focus on Stack Overflow (a programming CQA) and address this problem by proposing an embedding based approach that integrates users' textual content obtained from the community (e.g., answers) and community feedback in a unified framework. Our embedding-based approach is used to find the best relevant users for a given question by computing the similarity between questions and our user expertise representation. Then, our framework exploits feedback from the community to rank the relevant users according to their expertise. We experimentally evaluate the performance of the proposed approach using Stack Overflow's dataset, compare it with state-of-the-art models and demonstrate that it can produce better results than the alternative models.