Reviewerly/exHarmony huggingface dataset:
Low Altitude Aerial Scene Synthesis Using Generative Adversarial Networks for Autonomous Natural Resource Management

Deep neural networks are currently the best solution for aerial scene interpretation for Unmanned Aerial Vehicle (UAV) based remote sensing. A problem faced by the deep neural networks is that the deep models require significantly large training datasets which should cover almost all of the scenarios. Gathering these datasets is usually very time consuming and expensive. In this paper, data augmentation and generative adversarial network are used for autonomous synthesis of low altitude aerial scenes for creating a training dataset for deep low altitude aerial video interpretation. The proposed system is evaluated using a real world scenario of road following under foliage in a jungle and the experimental results show that the proposed framework is capable of producing high accuracy training datasets for UAV vision system in natural resource management scenarios.
Adaptive two-layer light field compression scheme based on sparse reconstruction

As a new form of volumetric media, the technology of light field and its compression has gradually become the research hotspots in academia. The scheme of compressing using the sparsity of the light field is a very promising idea, which has the characteristics of high compression rate and is not affected by the occlusion of scene objects. However, the instability of the reconstruction algorithm's performance on different datasets limits the further application of this solution. Since the quality of the decompression outputs will be limited below the reconstruction result, the poor performance of the reconstruction algorithm on some light field images will result in a very low PSNR upper limit for the compression scheme. This paper finds that the main reason for this performance problem is the poor ability of the algorithm to process the high-frequency components of the light field. And in order to solve it, an adaptive two-layer light field compression scheme is presented. The proposed scheme separates the high-frequency components and the low-frequency components of the light field so that they can be independently compressed. Through the adaptive adjustment, the data of different frequency component can adopt different compression strategies, so that the performance of the proposed scheme can be optimal. Experiments with multiple datasets1 show that the proposed scheme can break the upper limit of PSNR caused by sparse reconstruction and is capable to provide decompression results above 40 dB. It also achieves significant improvement in compression efficiency under diverse requirements.
Mutation

We present Mutation: performing arts based approach that can help decrease the cognitive load associated with cyborg transitioning. Cyborgs are human-machine hybrids with organic and mechatronic body parts that can be implanted or worn. The transition into and out of experiencing additional body parts is not fully understood. Our goal is to draw from performing arts techniques in order to help decrease the cognitive load associated with becoming and unbecoming a cyborg. Actors constantly shift between states, whether from one character to another, or from pre- to post- performance. We contribute a straightforward adaptation of classic performing art practices to cyborg transitioning, and a study where actors used these protocols in order to enter a cyborg state, perform as a cyborg, and then exit the cyborg state. Our work on Mutation suggests that classic performing art practices can be useful in cyborg transitioning, as well as in other technology augmented experiences.
A Unified Framework for Frequent Sequence Mining with Subsequence Constraints

Frequent sequence mining methods often make use of constraints to control which subsequences should be mined. A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints. In this article, we show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. In more detail, we propose a set of simple and intuitive “pattern expressions” to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our algorithms translate pattern expressions to succinct finite-state transducers, which we use as computational model, and simulate these transducers in a way suitable for frequent sequence mining. Our experimental study on real-world datasets indicates that our algorithms—although more general—are efficient and, when used for sequence mining with prior constraints studied in literature, competitive to (and in some cases superior to) state-of-the-art specialized methods.
Adversarial Artificial Intelligence for Overhead Imagery Classification Models

In overhead object detection, computers are increasingly replacing humans at spotting and identifying specific items within images through the use of machine learning (ML). These ML programs must be both accurate and robust. Accuracy means the results must be trusted enough to substitute for the manual deduction process. Robustness is the magnitude to which the network can handle discrepancies within the images. One way to gauge the robustness is through the use of adversarial networks. Adversarial algorithms are trained using perturbations of the image to reduce the accuracy of an existing classification model. The greater degree of perturbations a model can withstand, the more robust it is. In this paper, comparisons of existing deep neural network models and the advancement of adversarial AI are explored. While there is some published research about AI and adversarial networks, very little discusses this particular utilization for overhead imagery. This paper focuses on overhead imagery, specifically that of ships. Using a public Kaggle dataset, we developed multiple models to detect ships in overhead imagery, specifically ResNet50, DenseNet201, and InceptionV3. The goal of the adversarial works is to manipulate an image so that its contents are misclassified. This paper focuses specifically on producing perturbations that can be recreated in the physical world. This serves to account for physical conditions, whether intentional or not, that could reduce accuracy within our network. While there are military applications for this specific research, the general findings can be applied to all AI overhead image classification topics. This work will explore both the vulnerabilities of existing classifier neural net models and the visualization of these vulnerabilities.
Complementary Recommendations: A Brief Survey

Driven by the rapid development of global e-commerce, recommender systems have become a hot topic across both industry and academia because they offer a potential source of business revenue. A wide range of recommendation solutions have been developed and they can be classified as substitute and complementary: substitute recommenders offer similar items to the source item, complementary recommenders suggest items that are dissimilar to the source but are often sold as a companion item or service (such as a mobile phone (source) and case (complementary)). Both types of recommendations demonstrate unique values in specific application domains. However, the research and development of complementary recommendations still remain sparse when compared with substitute recommenders. This paper presents a brief survey on existing solutions for complementary recommendations. Our work summarizes the existing research activities and explores open questions in this field by discussing three aspects including the identification of the ground truth for complements; the recommendation models; and evaluation datasets and metrics. To the best of our knowledge, this work is one of the few surveys that provide particular insights on complementary recommendations in recent years.
Spiral Sine-Cosine Algorithm for Global Optimization

This paper presents a Hybrid Spiral and Sine-Cosine Algorithm (SSCA). Sine-Cosine algorithm (SCA) is a random-based optimization that utilizes an elitism approach and adaptive step size in its strategy. The step size is linearly varied and thus has caused the algorithm to produce steady convergence trend towards an optimal solution. It also has resulted the algorithm unable to achieve the true optimal solution. On the other hand, Spiral Dynamic Algorithm (SDA) is a deterministic-based algorithm that offers a nonlinear trend of agents step size in its operation. Therefore, an adoption of spiral equation from SDA into SCA is proposed as a solution to increase SCA convergence speed and its corresponding accuracy. The proposed algorithm is tested with a set of benchmark functions. Its accuracy and convergence trend performances are measured and recorded. A nonparametric Wilcoxon Sign Rank test is applied to statistically analyze the significance improvement of the SSCA accuracy in comparison to original SCA. Finding from the accuracy analysis indicates that the proposed SSCA algorithm significantly outperformed the original SCA. Moreover, from a graphical result, it shows that the SSCA has faster speed compared to another contestant algorithm.
Interpoint Distance Test of Homogeneity for Multivariate Mixture Models

Summary Finite mixtures offer a rich class of distributions for modelling of a variety of random phenomena in numerous fields of study. Using the sample interpoint distances (IPDs), we propose the IPD‐test statistic for testing the hypothesis of homogeneity of mixture of multivariate power series distribution or multivariate normal distribution. We derive the distribution of the IPDs that are drawn from a finite mixture of the multivariate power series distribution and multivariate normal distribution. Based on the empirical distribution of the IPDs, we construct a bootstrap test of homogeneity for other multivariate finite mixture models. The IPD test is applied to mixture models for matrix‐valued distributions and a test of homogeneity for Wishart mixture is presented. Numerical comparisons show that IPD test has accurate type I errors and is more powerful in most multivariate cases than the expectation–maximization (EM) test and modified likelihood ratio test.
A MapReduce-Based Association Rule Mining Using Hadoop Cluster—An Application of Disease Analysis

With the expansion and future potentialBhattacharya, Namrata in the field of healthcare industry, itMondal, Sudip is necessary to analyze a large amount of noisy dataKhatua, Sunirmal to obtain meaningful knowledge. Data mining techniques can be applied to remove inconsistent data and extract significant patterns. Association rule mining is a rule-based method which uncovers how items are associated with each other. Apriori algorithm is a broadly used algorithm for mining frequent itemsets for association rule mining. However, the performance of the apriori algorithm degrades with the large volume of data. So a parallel and distributed algorithm is required for efficient mining. In this paper, we provide an efficient implementation of the apriori algorithm in Hadoop MapReduce framework. We have considered medical data to produce rules which could be used to find the association between disease and their symptoms. These rules can be used for knowledge discovery to provide guidelines for the healthcare industry.
Research and Comparison of Ship Classification Algorithms Based on Variant CNNs

At present, the image classification algorithm based on deep learning in the field of computer vision has come to maturity. However, the papers related to the main classification are aimed at, for example, life scenes and traffic scenes. This paper specially produced image datasets for ships, and carried out various image preprocessing experiments and classification algorithms for specific ship scenes. Various models based on convolutional neural networks were used for training, and a number of performances were performed for comparison, including the accuracy of the training from scratch, the accuracy after the transfer learning, the class accuracy of each model. The experimental results show that the model using transfer learning is not only faster in training, but also has a significant improvement in accuracy. And the recognition difficulty of the model is different for different classes, which provides useful data for subsequent research on this particular scenario.
CALMS: A Context-Aware Learning Mobile System Based on Ontologies

The growing use of mobile devices to support the teaching - learning process and their use in storing academic contents on virtual platforms, requires that systems oriented to the mobile learning take into consideration the educational context of students. One of the biggest challenges during the construction of these systems is the modeling of the context and the possibility of including ontology-based approaches in order to obtain a better structure of the information. Thus, this paper presents a mobile learning system that uses a network of ontologies, which allow the representation of the contextual dimensions of a teaching - learning environment (e.g., location, time, user profiles and knowledge areas). In addition, it is possible to take advantage of the use of semantic search (keywords)and route algorithms applied to ontological models due to their expressiveness and extensible architecture. This study looks for providing students and teachers with personalized and relevant academic information in their current context of study. Finally, as a first step towards an empirical evaluation, the proposed system has been tried into a real academic context by means of a quasi-experiment with six students. Here, the results show favorable perceptions from the participants about its usefulness. These results were obtained using the statistical tool ANOVA, and show a positive influence on the academic performance of the students.
Comparing state-of-the-art methods of detection and tracking people on security cameras video

The use of security cameras allows to perform video surveillance. Nevertheless, little has been done to get other type of information from the videos. This project develops a people counting system for video from detection and tracking algorithms developed on Python 3. The use of trackers and detectors makes the system more robust to avoid overcounting problems. Comparisons were made to evaluate three different detection algorithms: YOLO, SSD and OpenPose; and six tracking algorithms developed on OpenCV: Boosting, MIL, KCF, TLD, Median Flow, MOSSE y CSRT, in order to implement the best algorithms. The system manages to obtain a relative error of maximum counting of 40%, having trouble with sub counting of people in the video.
Automated reconstruction of smoothly joining 3D printed restorations to fix broken objects

In this work, we provide an approach to automatically reconstruct a 3D printed restoration piece for a broken object from 3D scanned meshes of the broken object and an original counterpart. Our approach provides two contributions to reconstruct a restoration with a smooth join to the broken object, necessary for object functionality such as liquid containment, injury prevention, and visual aesthetic. As our first contribution, we leverage the original counterpart mesh to grow an exterior surface for the restoration piece that approaches the broken object within a small tolerance. As our second contribution, we project the exterior surface boundary onto the broken object to create a fracture surface boundary whose vertices satisfy the constraints of proximity, normal alignment, and tangency to vertices on the exterior surface boundary. Our approach prevents artifacts of volumetric Boolean subtraction, such as floating components and thin long slivers at the join, and avoids ruts at the join region introduced by Euclidean distance thresholding. We show 3D printed restoration results for 14 objects and 3D printable results for 8 objects.
Automatic Character Extraction from Handwritten Scanned Documents to Build Large Scale Database

Text extraction is an important phase in document recognition systems. In order to differentiate text from non-text objects, it is necessary to detect all possible text regions in the document. In this article, an efficient algorithm is proposed to detect all handwritten text characters from a page, written specifically, to prepare a dataset of handwritten alphabets at large scale, to be used in training phase of Machine Learning algorithms. The text line extraction algorithm uses a series of different steps to obtain the text region. Following, a sequence of histogram projection and recovery is proposed to obtain the line segmented region of the text. Text lines positions are detected using a horizontal histogram projection. Vertical histogram projection is used in each individual text line to find out the positions of alphabets in the respective text line. In post-processing, noise which is mostly small black spots, are removed using a moving median filter. Histogram projections are used once again, to detect all alphabets again after removal of noise. Alphabet detection rate on documents prepared for alphabet data preparation is 98.9% and alphabet detection rate for the normal handwritten document is 98%. Text line detection rate on 100 images of IAM database is 99.47%.
A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

Currently, the smartphone contains lots of sensitive information. The increasing number of smartphone usage makes it more interesting for phishers. Existing phishing detection techniques are performed on their specific features with selected classifiers to get their best accuracy. An effective phishing detection approach is required to adapt the concept drift of mobile phishing and prevent degradation in accuracy. In this work, an adaptive phishing detection approach based on case-based reasoning technique is proposed to handle the concept drift challenge in phishing apps. Several experiments are conducted in order to demonstrate the design decision of our proposed model. The proposed model is evaluated with a large feature set containing 1,065 features from 10 different categories. These features are extracted from more than 10,000 android applications. Five combinations of features are created in order to mimic new real-world Android apps to evaluate our experiments. Moreover, a reduced feature set is also studied in this work in order to improve the efficiency of the proposed model. Both accuracy and efficiency of the proposed model are evaluated. The experimental results show that our proposed model achieves acceptable accuracy and efficiency for the phishing detection.
Lasers structured light with phase-shifting for dense depth perception

In this paper, a novel depth acquisition algorithm aiming to obtain high resolution of dense depth maps based on lasers is proposed. Which expands the projection range and improves the robustness. In addition, dithering projection and defocusing techniques are used by lasers with the improvement of the Diffraction Optical Elements (DOE) technology. Four patterns are projected onto the projects by the lasers and captured by camera, where three for three-step phase-shifting (TS-PS) patterns and the other one is for space stripe pattern. More important, the calibrated by the traditional methods are unavailable in the proposed devices. We propose an algorithm for obtaining intrinsic and extrinsic parameters of the system. A substantially different spatial stripe coding scheme is designed, which is utilized to assist in time coding (TS-PS) solving depth information. We proposed a novel phase unwrapping method to obtain accurate absolute phase map. In addition, to further boost the performance, we utilized Weighted Least Squares (WLS) strategy to optimize the detail of the depth map based on color image. Thereby high resolution, detail and dense depth map obtained. Compared with the current methods, the robustness of our method is improved which will be extended to more scenes. The quantitative and qualitative evaluation experiments demonstrate that the proposed algorithm consistently produces better quality dense and higher resolution depth maps, superior to the current depth sensor.
TAMF

Confronting with the sheer amount of Over-the-Top (OTT) videos, personalized recommendation is especially important for users to locate videos of interest. However, previous approaches seldom considered the influence of watching time when designing video recommendation algorithms. In this paper, we first conduct a detailed measurement study on a leading OTT video service provider in China and our results show that user view preferences are substantially influenced by watching time. Based on the above results, we further propose a personalized time-aware video recommendation algorithm called TAMF for OTT videos. The basic idea of our proposed TAMF algorithm is to utilize matrix factorization to unveil how watching time affects user view interests and cluster time slots with similar influence. In this way, we can collaboratively learn users' personal interests if their views belong to the same cluster, and precisely capture user view preferences with watching time. Finally, we also conduct extensive experiments using real traces to evaluate the performance of our algorithm, and the experimental results show that our proposed algorithm can improve video recommendation performance by 4.83% and 4.42% in terms of WMRR and WMAP respectively and significantly boost user engagement.
Multi-drone Control with Autonomous Mission Support

Recent advancements on miniaturization and cost of sensors and instruments have promoted a growth in the usage of drones in an increasingly wide range of scenarios such as search and rescue, agriculture and environmental monitoring. However, most mechanisms for drone control still require an active pilot, limiting the ability to execute complex missions, especially when multiple drones are involved. Leveraging recent advances in the autonomous capabilities of commercially-available drone equipment, we introduce a complete and modular solution for controlling multiple drones, implementing the functionality necessary for inexperienced users to plan, execute and monitor complex missions that require drone cooperation.
Recurrent Prediction with Spatio-temporal Attention for Crowd Attribute Recognition

Crowd attribute recognition is a challenging task for crowd video understanding because a crowd video often contains multiple attributes from various types. Traditional deep learning-based methods directly treat this recognition problem as a multiple binary classification problem and represent the video by vectorizing and fusing the separately learned spatial and temporal features in the fully connected layers. Therefore, the correlations between these attributes may not be well captured. In this paper, a bidirectional recurrent prediction model with a semantic-aware attention mechanism is proposed to explore the spatio-temporal and semantic relations between the attributes for more accurate recognition. The ConvLSTM is introduced for feature representation to capture the spatio-temporal structure of the crowd videos and facilitate the visual attention. The bidirectional recurrent attention module is proposed for sequential attribute prediction by associating each subcategory attributes to corresponding semantic-related regions iteratively. The experiments and evaluations on the challenging WWW crowd video dataset not only show that our approach significantly outperforms the state-of-the-art methods but also verify that our approach can effectively capture the spatio-temporal and semantic relations of the crowd attributes.