Reviewerly/exHarmony huggingface dataset:
Chaotic Binary Pelican Optimization Algorithm for Feature Selection

This research proposes a new wrapper model based on chaos theory and nature-inspired pelican optimization algorithm (POA) for feature selection. The base algorithm is converted into a binary one and a chaotic search to augment POA’s exploration and exploitation process, denoted as chaotic binary pelican optimization algorithm (CBPOA). The main focus of chaos theory is to resolve the slow convergence rate as well as entrapment in local optimal issues of classical POA. Therefore, ten dissimilar chaotic maps are entrenched in POA to tackle these issues and attain a more robust and effective search mechanism. CBPOA executes on continuous search; thus, the continuous search is reformed to a discrete one by adapting transfer functions. In CBPOA, eight transfer functions are used to find the best one and inspect CBPOA. Consequently, the performance of the CBPOA has been investigated by targeting several metrics under 18 UCI datasets. The best variant is nominated and explored the performance with classical wrapper-based and filter-based schemes. Furthermore, the proposed CBPOA is evaluated using 23 functions from CEC-2017, 2018 and 2020 benchmarks. As an outcome, CBPOA has accomplished better outcomes than existing schemes and is superior in handling feature selection problems.
Classification of Imbalanced Data Using SMOTE and AutoEncoder Based Deep Convolutional Neural Network

The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the imbalanced class distribution of the training data set. Recently, machine learning and deep learning techniques are used for imbalanced data classification. Data preprocessing approaches are also suitable for handling class imbalance problem. Data augmentation is one of the preprocessing techniques used to handle skewed class distribution. Synthetic Minority Oversampling Technique (SMOTE) is a promising class balancing approach and it generates noise during the process of creation of synthetic samples. In this paper, AutoEncoder is used as a noise reduction technique and it reduces the noise generated by SMOTE. Further, Deep one-dimensional Convolutional Neural Network is used for classification. The performance of the proposed method is evaluated and compared with existing approaches using different metrics such as Precision, Recall, Accuracy, Area Under the Curve and Geometric Mean. Ten data sets with imbalance ratio ranging from 1.17 to 577.87 and data set size ranging from 303 to 284807 instances are used in the experiments. The different imbalanced data sets used are Heart-Disease, Mammography, Pima Indian diabetes, Adult, Oil-Spill, Phoneme, Creditcard, BankNoteAuthentication, Balance scale weight &amp; distance database and Yeast data sets. The proposed method shows an accuracy of 96.1%, 96.5%, 87.7%, 87.3%, 95%, 92.4%, 98.4%, 86.1%, 94% and 95.9% respectively. The results suggest that this method outperforms other deep learning methods and machine learning methods with respect to G-mean and other performance metrics.
Lung Cancer Detection (LCD) from Histopathological Images Using Fine-Tuned Deep Neural Network

This work aims to detect lung cancer using histopathological images more accurately. Lung cancer is one of the major diseases because of which many deaths happened each year worldwide. The mortality rate can be reduced significantly in the early-stage diagnosis of cancer. Automatic histopathological image classification plays a key role in reducing death due to lung cancer. Nowadays, with the advancement in the medical field, many pathologies consider whole-slide images for their routine clinical procedure. With the advancement in medical imaging technology, whole-slide images are becoming a routine clinical procedure in pathology. Recently, machine learning and deep learning have shown the potential to analyze pathological images for early-stage cancer prediction such as lung cancer detection. However, training neural networks from scratch requires a large number of labeled images. This is not always feasible, especially with medical imaging data. A promising solution is a transfer learning application on a neural network. In this research paper, transfer learning is applied using fine-tuning the pre-trained EfficientNet-B0 model to detect three different classes of lung cancer. The designed model achieved an accuracy of 99.15%, 99.14%, and 98.67% on the train, validation, and test set, respectively.
Recent Trends in Deep Learning Based Textual Emotion Cause Extraction

Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a document and mainly includes Emotion Cause Extraction (ECE) and Emotion Cause Pair Extraction (ECPE). Traditional ECE aims to extract the cause based on a given emotion while ECPE aims to extract both the emotion and its corresponding cause. Recently, ECEF has attracted a lot of attention and most of the advances have benefited from significant developments in deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval. The large pre-trained language model of BERT has also shown effectiveness in this field. Following the proposal of ECPE, the development of ECEF has accelerated. However, a comprehensive review of existing approaches and recent trends in the field is lacking. To address this issue, this survey presents a thorough review to summarise existing methods and recent key advances, illustrate the general technical architecture of traditional ECE, introduce several important variants, in particular ECPE, and provide a detailed comparison of several public datasets. Finally, the limitations of existing work and the prospects for further technological advances in ECEF are discussed.
Detection of Parkinson’s disease based on spectrograms of voice recordings and Extreme Learning Machine random weight neural networks

Parkinson's disease consists in the degeneration of the mesencephalic black substance, affecting the dopaminergic vias. Its causes are varied, including exposure to pesticides, genetic factors and, one of the most influential ones, age. Given the decrease in dopamine levels, the most common symptoms are the appearance of tremors and muscle rigidity. Due to the rigidity of the muscles, the patient has voice alterations which have great potential for non-invasive and early diagnosis of the disease. In addition, considering the low cost of the sound recorder respect to the clinical studies, this approach allows the diagnosis of Parkinson's disease in a large number of people. Recent works, which present the analysis of voice recordings through Convolutional Neural Networks, show high level of accuracy in the diagnosis of Parkinson's disease. Convolutional Neural Networks use a Multilayer Neural Network to classify convolutional feature vectors. In order to improve the training time of the classifier, in this paper the use Extreme Learning Machines are proposed. Experiments considering 4 types of spectrograms with AlexNet, VGG-16, SqueezeNet, Inception V3 and ResNet-50 Convolutional Neural Networks models. In the experiments, hit rate, training and testing time, sensitivity and the specificity indicators of all the neural architectures involved in the work are objectively compared. It is shown that the Extreme Learning Machine have a high level of accuracy in the diagnosis of Parkinson's disease but with reduced training time.
Few-shot object detection on aerial imagery via deep metric learning and knowledge inheritance

Object detection is crucial in aerial imagery analysis. Previous methods based on convolutional neural networks (CNNs) require large-scale labeled datasets for training to achieve significant success. However, the acquisition and manual annotation of such data is time-consuming and expensive. In this study, we present an original few-shot object detection (FSOD) method that focuses on detecting unseen objects in aerial imagery with limited labeled samples. Specifically, we revisited the multi-similarity network from deep metric learning and incorporated it into a faster region-CNN (R-CNN) architecture for FSOD, learning distinctive feature representations, and effectively improving the performance of unseen class samples. Furthermore, we preserved the knowledge learned from abundant base data by designing a knowledge inheritance module to ease the influence of catastrophic forgetting. We conducted experiments on two benchmark remote sensing image datasets, and the results demonstrated that the proposed methods could achieve a satisfactory performance for FSOD in aerial imagery.
User-centric immersive virtual reality development framework for data visualization and decision-making in infrastructure remote inspections

Data visualization and decision-making are critical aspects of infrastructure inspections. Numerous research efforts have been made to develop practical remote inspection systems that overcome the limitations of conventional inspection methods. Although the integration of Immersive Virtual Reality (IVR) into remote inspection is currently being addressed, most IVR frameworks have focused on data accuracy and there has been a lack of research on developing human-centered IVR frameworks through usability evaluation. This paper addresses this limitation by proposing a new IVR development framework utilizing agile User Experience (UX) design techniques, which can be implemented in remote infrastructure inspections for data visualization and decision-making. Usability tests were conducted through a case study with 22 participants, using an IVR prototype developed in accordance with this framework. Following its implementation, the framework demonstrated its ability to identify damage via remote bridge inspection and achieved acceptable usability. Future research can build on the framework for semantic enrichment tasks.
Corruption, formal institutions, and foreign direct investment: The case of <scp>OECD</scp> countries in Africa

Abstract Purpose Corruption has shown a mixed impact on foreign direct investment (FDI). This study proposed moderating the role of two—foreign aid (international institution) from the home country and democracy (national institution) in the host country—between corruption‐FDI nexus. Methodology The framework is analyzed using panel data analysis (2001–2018) of bilateral foreign aid and FDI from 18 European members of the Organization for Economic Cooperation and Development (OECD) to 34 African countries. Findings The presents several key findings. First, Africa's level of government corruption harms bilateral FDI from Europe. Second, the OECD's bilateral foreign aid moderates the negative effects of Africa's host‐country corruption on FDI. Third, the level of democracy in the host country also moderates the negative impact of corruption on FDI. Finally, foreign aid strongly moderates the negative effect of corruption on FDI in democratic countries compared to non‐democratic host countries. Research Implications This study provides the institutional analysis that bilateral foreign aid and democracy as formal institutions affect the European multinational enterprises's decision to invest in Africa. Practical Implications It presents the policy and managerial implications. First, European MNEs managers avoid investing in Africa, and governments must take strict actions to attract FDI. Second, foreign aid and democracy motivate MNEs to invest in Africa. Finally, the OECD policymaker could formulate rigorous and relevant conditions for foreign aid to Africa to reduce corruption. Originality/Value Through the lens of institutional theory and selectorate theory, the novel institutional role of foreign aid and democracy is proposed and tested between the nexus of corruption‐FDI.
The improved grasshopper optimization algorithm with Cauchy mutation strategy and random weight operator for solving optimization problems

Abstract An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed CMRWGOA, which combines both Random Weight (shorted RWGOA) and Cauchy mutation (termed CMGOA) mechanism into the GOA. The GOA received inspiration from the foraging and swarming habits of grasshoppers. The performance of the CMRWGOA was validated by 23 benchmark functions in comparison with four well-known meta-heuristic algorithms (AHA, DA, GOA, and MVO), CMGOA, RWGOA, and the GOA. The non-parametric Wilcoxon, Friedman, and Nemenyi statistical tests are conducted on the CMRWGOA. Furthermore, the CMRWGOA has been evaluated in three real-life challenging optimization problems as a complementary study. Various strictly extensive experimental results reveal that the CMRWGOA exhibit better performance.
Vision-aided UAV Navigation and Dynamic Obstacle Avoidance using Gradient-based B-spline Trajectory Optimization

Navigating dynamic environments requires the robot to generate collision-free trajectories and actively avoid moving obstacles. Most previous works designed path planning algorithms based on one single map representation, such as the geometric, occupancy, or ESDF map. Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably handle static and dynamic obstacles simultaneously. To address the problem, this paper proposes a gradient-based B-spline trajectory optimization algorithm utilizing the robot's onboard vision. The depth vision enables the robot to track and represent dynamic objects geometrically based on the voxel map. The proposed optimization first adopts the circle-based guide-point algorithm to approximate the costs and gradients for avoiding static obstacles. Then, with the vision-detected moving objects, our receding-horizon distance field is simultaneously used to prevent dynamic collisions. Finally, the iterative re-guide strategy is applied to generate the collision-free trajectory. The simulation and physical experiments prove that our method can run in real-time to navigate dynamic environments safely.
LATTE: LAnguage Trajectory TransformEr

Natural language is one of the most intuitive ways to express human intent. However, translating instructions and commands towards robotic motion generation and deployment in the real world is far from being an easy task. The challenge of combining a robot's inherent low-level geometric and kinodynamic constraints with a human's high-level semantic instructions traditionally is solved using task-specific solutions with little generalizability between hardware platforms, often with the use of static sets of target actions and commands. This work instead proposes a flexible language-based framework that allows a user to modify generic robotic trajectories. Our method leverages pre-trained language models (BERT and CLIP) to encode the user's intent and target objects directly from a free-form text input and scene images, fuses geometrical features generated by a transformer encoder network, and finally outputs trajectories using a transformer decoder, without the need of priors related to the task or robot information. We significantly extend our own previous work presented in [1] by expanding the trajectory parametrization space to 3D and velocity as opposed to just XY movements. In addition, we now train the model to use actual images of the objects in the scene for context (as opposed to textual descriptions), and we evaluate the system in a diverse set of scenarios beyond manipulation, such as aerial and legged robots. Our simulated and real-life experiments demonstrate that our transformer model can successfully follow human intent, modifying the shape and speed of trajectories within multiple environments. Codebase avail-able at: https://github.com/arthurfenderbucker/LaTTe-Language-Trajectory-TransformEr.git.
Continuity-Aware Latent Interframe Information Mining for Reliable UAV Tracking

Unmanned aerial vehicle (UAV) tracking is crucial for autonomous navigation and has broad applications in robotic automation fields. However, reliable UAV tracking remains a challenging task due to various difficulties like frequent occlusion and aspect ratio change. Additionally, most of the existing work mainly focuses on explicit information to improve tracking performance, ignoring potential interframe connections. To address the above issues, this work proposes a novel framework with continuity-aware latent interframe information mining for reliable UAV tracking, i.e., ClimRT. Specifically, a new efficient continuity-aware latent interframe information mining network (ClimNet) is proposed for UAV tracking, which can generate highly-effective latent frame between two adjacent frames. Besides, a novel location-continuity Transformer (LCT) is designed to fully explore continuity-aware spatial-temporal information, thereby markedly enhancing UAV tracking. Extensive qualitative and quantitative experiments on three authoritative aerial benchmarks strongly validate the robustness and reliability of ClimRT in UAV tracking performance. Furthermore, real-world tests on the aerial platform validate its practicability and effectiveness. The code and demo materials are released at https://github.com/vision4robotics/ClimRT.
Generating Formal Safety Assurances for High-Dimensional Reachability

Providing formal safety and performance guarantees for autonomous systems is becoming increasingly important. Hamilton-Jacobi (HJ) reachability analysis is a popular formal verification tool for providing these guarantees, since it can handle general nonlinear system dynamics, bounded adversarial system disturbances, and state and input constraints. However, it involves solving a PDE, whose computational and memory complexity scales exponentially with respect to the state dimensionality, making its direct use on large-scale systems intractable. A recently proposed method called DeepReach overcomes this challenge by leveraging a sinusoidal neural PDE solver for high-dimensional reachability problems, whose computational requirements scale with the complexity of the underlying reachable tube rather than the state space dimension. Unfortunately, neural networks can make errors and thus the computed solution may not be safe, which falls short of achieving our overarching goal to provide formal safety assurances. In this work, we propose a method to compute an error bound for the DeepReach solution. This error bound can then be used for reachable tube correction, resulting in a safe approximation of the true reachable tube. We also propose a scenario-based optimization approach to compute a probabilistic bound on this error correction for general nonlinear dynamical systems. We demonstrate the efficacy of the proposed approach in obtaining probabilistically safe reachable tubes for high-dimensional rocket-landing and multi-vehicle collision-avoidance problems.
Extensible Modeling Framework for Reliable Machine Learning System Analysis

Machine learning system analysis requires different approaches for each different task and domain. Selecting a proper set of analytic models can be challenging for a specific problem. This paper discusses the extensibility of the Multi-View Modeling Framework for ML Systems approach using process mapping and extensible metamodel. We conducted a case study to evaluate the feasibility of such extensibility by extending the approach to facilitate an activity-driven analysis for an optical character recognition system. Based on the result of the case study, we found that Multi-View Modeling Framework for ML Systems is likely to be extensible.
TVA-GAN: attention guided generative adversarial network for thermal to visible image transformations

In the recent improvement in deep learning approaches for realistic image generation and translation, Generative Adversarial Networks (GANs) delivered favorable results. GAN generates novel samples that look indistinguishable from authentic images. This paper proposes a novel generative network for thermal-to-visible image translation. Thermal to Visible synthesis is challenging due to the non-availability of accurate semantic and textural information in thermal images. The thermal sensors acquire the thermal face images by capturing the object’s luminance with fewer details about the actual facial information. However, it is advantageous for low-light and night-time vision, where image information cannot be captured in a complex environment by an RGB camera. We design a new Attention-guided Cyclic Generative Adversarial Network for Thermal to Visible Face transformation (TVA-GAN) by integrating a new attention network. We utilize attention guidance with a recurrent block with an Inception module to simplify the learning space toward the optimum solution. The proposed TVA-GAN is trained and evaluated for thermal to visible face synthesis over three benchmark datasets, including the WHU-IIP, Tufts Face Thermal2RGB, and CVBL-CHILD datasets. The proposed TVA-GAN results show promising improvement in face synthesis compared to the state-of-the-art GAN methods. For the proposed TVA-GAN, code is available at: https://github.com/GANGREEK/TVA-GAN .
Blockchain technology as an enabler for cross‐sectoral systems integration for developing smart sustainable cities

Abstract Smart cities driven by modern technologies are the need of the day to alleviate the urbanisation challenges and improve the overall experience of the citizens. As the role of data‐sharing to facilitate systems integration across city sectors for developing smart cities has grown ever so profoundly, there is a need for decentralisation, transparency, and openness in terms of integration of city sectors to have efficient data diffusion among them. This is extremely important as the requirements of smart sustainable cities are open data sharing to allow service providers to better serve the citizens. Blockchain technology offers these characteristics with the extremely important added advantage of maintaining data security via an immutable record. However, the notion of the use of Blockchain for smart sustainable cities is still in the early years and requires extensive efforts to research and test it. In this research, a state‐of‐the‐art review is conducted to explore the usefulness of Blockchain technology in smart sustainable city development with a specific focus on cross‐sectoral systems integration, highlighting the gaps in the existing body of knowledge. This leads to the proposal of a novel framework for the use of Blockchain for smart sustainable cities, linking together service providers and citizens.
Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network

In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes, this paper proposes an operational risk prediction model based on an automatic encoder and convolutional neural networks. First, we use an automatic encoder to extract features of motion risk factors and obtain feature components that can highly represent risk. Secondly, based on the causal relationship between sports risk and risk characteristics, a convolutional neural network with a dual convolution layer and dual pooling layer topology is constructed. Finally, the sports risk prediction model is established by combining the auto-coded feature components with the topology of the convolutional neural network. Compared with other algorithms, the proposed method can effectively analyze and extract risk characteristics and has a high prediction accuracy. At the same time, it promotes the integration of sports science and computer science and provides a basis for the application of machine learning in the field of sports risk prediction.