Reviewerly/exHarmony huggingface dataset:
Vision-Based Interception of Tethered and Swaying Targets Using Aerial Manipulation

Target interception is an interesting application of unmanned aerial vehicles (UAVs). This requires dedicated system software and manipulation mechanism. We propose a vision based interception of swaying but tethered target in an environment with disturbance. The targets are detected, approached and intercepted. The process involves exploration, vision-based object detection and servoing to the target and terminal contact. This is followed by the confirmation of interception. The solution is formulated such that interception is guaranteed in an unknown environment. A manipulator is designed to achieve successful interception. The software framework is developed using ROS and simulated in Gazebo. It is also validated through field experiments. The proposed solution could be used for applications like drone-based fruit picking, counter-UAV systems, trimming or removing creepers from power lines, among many other similar applications.
Analysis of Canonical Heuristic Methods for the Optimization of an Investment Portfolio

An investment portfolio is a collection of financial assets in which an individual or a financial institution called an investor has decided to invest its capital based on criteria such as its risk aversion, the experience of a financial advisor or the formulation of an optimization problem. This last approach was the object of study of Harry Markowitz, an American economist who wrote the Theory of the optimal portfolio, in this, the amounts to be invested in the assets are determined in such a way that allows the maximum possible profit with the minimum possible risk. In this work it is proposed, as an optimization problem based on the Markowitz model, the obtaining of investment amounts of a six financial assets portfolio through the maximization of the profit ratio and the risk of loss. To determine the best possible solution, two genetic algorithms are compared with different percentage of cross, a clonal immunological algorithm and a differential evolution algorithm. The comparison criteria are the stability of the algorithm (obtainable with the coefficient of variability) and the hypothesis of normality and homoscedasticity obtainable from 60 executions of each of the proposed algorithms. It also determines the convergence of the algorithms used in this work. The results obtained allow to obtain a diversified portfolio, the first requirement of an investment portfolio according to Markowitz's theory.
Multi-stage Synthetic Image Generation for the Semantic Segmentation of Medical Images

Recently, deep learning methods have had a tremendous impact on computer vision applications, from image classification and semantic segmentation to object detection and face recognition. Nevertheless, the training of state-of-the-art neural network models is usually based on the availability of large sets of supervised data. Indeed, deep neural networks have a huge number of parameters which, to be properly trained, require a fairly large dataset of supervised examples. This problem is particularly relevant in the medical field due to privacy issues and the high cost of image tagging by medical experts. In this chapter, we present a new approach that allows to reduce this limitation by generating synthetic images with their corresponding supervision. In particular, this approach can be applied in semantic segmentation, where the generated images (and label-maps) can be used to augment real datasets during network training. The main characteristic of our method, differently from other existing techniques, lies in the generation procedure carried out in multiple steps, based on the intuition that, by splitting the procedure in multiple phases, the overall generation task is simplified. The effectiveness of the proposed multi-stage approach has been evaluated on two different domains, retinal fundus and chest X-ray images. In both domains, the multi-stage approach has been compared with the single-stage generation procedure. The results suggest that generating images in multiple steps is more effective and computationally cheaper, yet allowing high resolution, realistic images to be used for training deep networks.
Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images

For computer-aided diagnosis, automatic and accurate liver tumor detection in multi-phase CT images is essential. Nowadays, deep learning has been widely used in various medical applications. Deep learning-based AI systems require a large amount of training data for model learning. However, acquiring sufficient training data with high-quality annotation is a major challenge in Healthcare. As a result, deep learning-based models face a lack of annotated training data problem. While the generalization of a label-rich training domain (source) to a new test domain (target) causes a domain shift problem in deep learning-based models. To solve the lack of training data and domain shift problem, domain adaptation-based methods have recently been developed as a technique to bridge the domain gap across datasets with different feature characteristics and data distributions. In this chapter, we have proposed domain adaptation-based technique for liver tumor detection in multi-phase CT images. We discuss the domain-shift problem in different phases of multiphase liver CT images and introduce our domain adaptation technique for multi-phase CT images. We have used PV phase images to learn a model and applied the learnt model to ART and NC phase images (i.e. different domains) by adapting the domain knowledge. To address the domain gap between the different phases of CT images, we employ adversarial learning scheme using an anchor-free object detector. Further, we propose to use the maximum square loss for mid-level output feature maps to improve the performance. Our method does not require separate object-level annotations for each phase of Multiphase CT image while training. The results of the experiments show that models trained using our proposed domain adaptation technique perform much better than those trained in normal setting.
Identifying Pseudorutile and Kleberite Using Raman Spectroscopy

Pseudorutile and kleberite are intermediate minerals formed during alteration of ilmenite to rutile. They are difficult to identify, as both have a range of chemical composition and occur as small crystals commonly mixed with other minerals. Reference samples of large crystals of pseudorutile and kleberite, with published X-ray diffraction and chemical analyses, were analysed to establish characteristic Raman spectra. Pseudorutile produced a goethite-like Raman spectrum but with a shift to increased wavenumber. It has characteristic Raman bands with peak positions at 234, 302, 402, 546, 617, 713 and 816 cm−1 and OH stretching over the interval of 3390–3350 cm−1. The 402 and 806 cm−1 bands are the most intense. Kleberite produced a similar spectrum, but with a 10–30 cm−1 greater Raman shift in the goethite-like bands. Its Raman bands have peak positions at 432, 573, 740, and 820 cm−1 and OH stretching at 3390–3350 cm−1. These results were applied to identify pseudorutile formed by diagenetic alteration of detrital ilmenite in Cretaceous sandstones of the Mesozoic Scotian Basin, eastern Canada. These samples showed pseudorutile Raman bands, but some samples are intermixed with residual ilmenite. Raman microspectroscopy thus allows rapid identification of small grains of pseudorutile and kleberite.
Finding Treasure in the Wild: A Field Study of a Collaborative Design Software

Software evaluation has several forms: it can take place in controlled or naturalistic settings with low or high researcher control. Compared to single-user software evaluation, collaborative software evaluation investigates not only the usability of the software, but also its social context of use. To understand the specific needs of users, their daily tasks should be examined in real life working and organizational context. Therefore, group behavior, the process of collaboration and the organizational culture need to be investigated. To achieve this goal ethnography or field study should be applied as a research strategy. Both are performed in a naturalistic setting, apply a mix of data collection techniques and qualitative data analysis. Nevertheless, the main value of ethnography and field study is that they provide an understanding of software usage in the social context of work. This chapter aims to make a contribution by introducing a field study of a collaborative design software. First, the difference between ethnography and field study will be demonstrated. Then, related literature about ethnographic research and field study will be discussed. Finally, our field study of a collaborative software evaluation will be presented.
Toward Sequential Recommendation Model for Long-Term Interest Memory and Nearest Neighbor Influence

Sequential recommendation can make predictions by fitting users’ changing interests based on the users’ continuous historical behavior sequences. Currently, many existing sequential recommendation methods put more emphasis upon users’ recent preference (i.e., short-term interests), but simplify or even ignore the influence of users’ long-term interests, resulting in important interest features of users not being effectively mined. Moreover, users’ real intentions may not be fully captured by only focusing on their behavior histories, because users’ interests are diverse and dynamic. To solve the above problems, we propose a novel sequential recommendation model for long-term interest memory and nearest neighbor influence. Firstly, item embeddings based on item similarity and dependency are constructed to alleviate the problem of data sparsity in users’ recent interest history. Secondly, in order to effectively capture long-term interests, the long sequence is divided into multiple nonoverlapping subsequences. For these subsequences, the graph attention network with node importance factor is designed to fully extract the main interests of subsequences, and LSTM is introduced to learn the dynamic changes of interest among subsequences. Long-term interests of users are modeled through complex structure within subsequences and sequential dependencies among subsequences. Finally, the user’s neighbor representation is introduced, and a gating module is designed to integrate the user’s neighbor information and self-interests. The influence of users’ short-term and long-term interests on prediction is dynamically controlled by considering nearby features in the gating network. The experimental results on two public datasets show that the proposed sequential recommendation model can outperform the baseline methods in hit rate (HR@K) and normalized discounted cumulative gain (NDCG@K).
Developing progress monitoring measures: Parallel test construction from the item-up

Progress monitoring is a process of collecting ongoing samples of student work and tracking performance of individual students over time. Progress monitoring involves administering parallel sets of items to the same student on a regular basis (at least monthly) that are sensitive to changes in the student’s understanding based on instruction. The sets of items administered over time should be parallel in difficulty so that differences in performance can be attributed to differences in the student’s understanding as opposed to variability in item difficulty across sets. In this manuscript, we describe an approach to designing items that controls item-level variability by constraining the item features that may elicit different cognitive processing. This approach adapts the principles of Automated Item Generation (AIG) and includes carefully designing test specifications, isolating specific components of the content that will be assessed, creating item models to serve as templates, duplicating the templates to create parallel item clones, and verifying that the duplicated item clones align with the original item model. An example from an operational progress monitoring system for mathematics in Kindergarten through Grade 6 is used to illustrate the process. We also propose future studies to empirically evaluate the assertion of parallel form difficulty.
Thoughts on the usage of audible smiling in speech synthesis applications

In this perspective paper we explore the question how audible smiling can be integrated in speech synthesis applications. In human-human communication, smiling can serve various functions, such as signaling politeness or as a marker of trustworthiness and other aspects that raise and maintain the social likeability of a speaker. However, in human-machine communication, audible smiling is nearly unexplored, but could be an advantage in different applications such as dialog systems. The rather limited knowledge of the details of audible smiling and their exploitation for speech synthesis applications is a great challenge. This is also true for modeling smiling in spoken dialogs and testing it with users. Thus, this paper argues to fill the research gaps in identifying factors that constitute and affect audible smiling in order to incorporate it in speech synthesis applications. The major claim is to focus on the dynamics of audible smiling on various levels.
Decidability Preservation and Complexity Bounds for Combined Logics

Transfer theorems for combined logics provide essential tools and insight for reasoning about complex logical systems. In this paper, we present the first sufficient criterion (contextual extensibility) for decidability to be preserved through combination of propositional logics, and we study the complexity upper bounds induced by the method. In order to assess the scope and usability of our criterion, we illustrate its use in re-obtaining two standard important (though partial) results of the area: the preservation of decidability for disjoint combinations of logics, and the preservation of decidability for fusions of modal logics. Due to the very abstract nature and generality of the idea underlying contextual extensibility, we further explore its applicability beyond propositional logics. Namely, we explore the particular case of 2-deductive systems, and as a byproduct, we obtain the preservation of decidability for disjoint combinations of equational logics and discuss the relationship of this result and of our criterion with several related results with meaningful applications in satisfiability modulo theories.
MF-Storm: a maximum flow-based job scheduler for stream processing engines on computational clusters to increase throughput

A scheduling algorithm tries to schedule multiple computational tasks on a cluster of multiple computing nodes to maximize throughput with optimal utilization of computational and communicational resources. A Stream Processing Engine (SPE) is deployed to run streaming applications (computational tasks) on a computational cluster which helps execution and coordination of these applications. It is observed that there is a gap in the optimal mapping of a computational and communicational load of a streaming application on the underlying computational and communication power of the resources (cluster). Frequently communicated tasks are scheduled at different processing nodes with relatively slow communicating links. This increases network latency with a decrease in resource utilization. Hence, reduces the achieved throughput of the cluster significantly.MF-Storm, a max-flow min-cut based job scheduler is presented to achieve a near-optimum schedule to maximize throughput. It schedules a streaming application by considering the processing, communication demands, available computational and communicational resources in a heterogeneous cluster, dynamically with minimized scheduling cost. To keep the scheduling cost minimum, the scheduler is built in a pipeline with two major stages: in the first stage, the application's tasks graph is partitioned using the max-flow min-cut algorithm to minimize inter-partition traffic, and in the second stage, these partitions are assigned to computing nodes according to the computational power of the cluster's nodes.Extensive experiments were done to evaluate the performance of MF-Storm using different topologies with multiple scenarios on a physical cluster implementation. Results showed on average 148% improvement in throughput with 30% less computational resources as compared to different state-of-the-art schedulers.
Estimation of Soil Macronutrients Using Voting Classifiers

To adopt a proper fertilizer management strategy, estimation of the nutrient contents of agricultural soil is a primary task. Several models based on machine learning techniques have been suggested for this purpose. However, these models are suffering from some major limitations. This paper suggests a better alternative model where a classier assembly has been hybridized with voting classifiers to achieve the accuracy better than any other existing model. Both hard and soft voting techniques were used in designing the model. The model has been trained and tested with benchmarked datasets. The performance of the model has been evaluated in terms of five standard popular classification metrics. A comparative analysis of performance reveals that to date, our proposed model is the best performing one for the estimation of macronutrients in agricultural soil.
Performance Efficacy of Cost-Sensitive Artificial Neural Network: Augmenting the Results of Imbalanced Datasets in Supervised and Unsupervised Learning

In any type of classification model, successful implementation of the balanced dataset is required so as to obtain the optimal result in machine learning. Imbalance dataset enforces many challenges for achieving and implementing the analytical data in the real world. Such situation occurs when there is high variation in one sample class as compared to other sample class. Performance of any classification model is totally dependent on the dataset quality as well as its quantity. Imbalanced training data has a significant detrimental influence on overall performance. The most advanced machine learning approach always fights an unbalanced dataset by focusing on two major components as avoiding the minority class and decreasing inaccuracy for the majority. In this paper, an attempt is made to compare the results of SMOTE-NC, SMOTE-ENC, and cost-sensitive ANN (Artificial Neural Network) to alleviate the imbalance data and to generate the better efficiency than existing methods and models. Furthermore, it is clearly demonstrated that ‘cost-sensitive ANN’ technique generates better results using deep learning model for prediction with versatile datasets. Finally, the results of the experiment conducted in the study verified that ‘cost-sensitive ANN’ technique performs more effectively when there is a presence of extensive data and fundamentally the case of high ratio exists.
Image Detection and Recognition Using Convolutional Neural Networks

The method of collecting useful information after converting an image into digital form is called image processing. This is achieved by performing some operations on the digitized data. Object detection and recognition are the tasks that are important and challenging and has various applications in computer vision. Object detection is verifying the presence of an object in an image and then recognizing the object. Object recognition aims at classifying images and helps in understanding images. Classifying objects and tracking their motion are an important aspect of any intelligent system. This paper proposes a VGG based-convolutional neural network with 12 layers that can be used for image recognition. The proposed architecture is compared with other standard CNNs and is found to be efficient and computationally less complex. Competitive datasets CIFAR-10 and Caltest 101 have been used to validate the model and test the accuracy of the model under different variations. The edges of the images have been enhanced using a Sobel operator before image recognition to improve the efficiency of the architecture.
Automatic Conversion of Table Contents from PDF Technical Specification Documents into Database Using AI Optical Character Recognition (OCR)

This study aimed to develop a technology to recognize and warehouse text data from a table containing technical documents relating to construction and plant projects. To this end, a table optical character recognition (OCR) technology was proposed to tag text data by recognizing the structure of the table and the context of the content in the table. For analysis, the table format was first classified into two patterns: T1 and T2. T1 refers to a table with only one step of the header, and T2 refers to a table with two phases of the header. The table OCR model extracts text in cell units of the table using the OpenCV engine after extracting data from the headers. A training model improves text recognition rate through a long short-term memory (LSTM)-based Tesseract OCR engine. Extracted data from the table were stored in the DB and output in CSV format. The confusion matrix was applied to verify the recognition accuracy of the extracted data, and as a result of the verification, the F-measure value of T1 was 96%, and T2 was 87%. Therefore, from the outcome of this study, it is expected that the automated management of tasks that hitherto relied solely on the engineer’s experience will subsequently contribute to reducing the workload and improving the productivity of the engineer in charge.
A classification model of power operation inspection defect texts based on graph convolutional network

Aiming at the problems of diversification, complexity and islanding of power operation and inspection data and the high dependence of operation and inspection operations on expert experience and normative information, the key technology research of intelligent judgment of defect types of power operation inspection equipment is carried out. For the field of power operation and inspection, the defect text classification algorithm based on graph convolutional neural network is proposed. And the practical tests in a large defect text network diagram built by main transformer defect reports are performed. And the proposed model achieves better classification results than 7 benchmark models in the defect text classification task. Specifically, the Accuracy, Weighed-Precision, and Weighed-F1 indicators reach 73.39, 72.42, and 72.21 respectively, which improves the model’s ability to identify defect types to a greater extent and plays an important role in improving the intelligence and digitalization of power operation and inspection work.