Reviewerly/exHarmony huggingface dataset:
Multi-objective 3D Path Planning for UAVs in Large-Scale Urban Scenarios

In the context of real-world path planning applications for Unmanned Aerial Vehicles (UAVs), aspects such as handling of multiple objectives (e.g., minimizing risk, path length, travel time, energy consumption, or noise pollution), generation of smooth trajectories in 3D space, and the ability to deal with urban environments have to be taken into account jointly by an optimization algorithm to provide practically feasible solutions. Since the currently available methods do not allow for that, in this paper, we propose a holistic approach for solving a Multi-Objective Path Planning (MOPP) problem for UAVs in a three-dimensional, large-scale urban environment. For the tackled optimization problem, we propose an energy model and a noise model for a UAV, following a smooth 3D path. We utilize a path representation based on 3D Non-Uniform Rational B-Splines (NURBS). As optimizers, we use a conventional version of an Evolution Strategy (ES), two standard Multi-Objective Evolutionary Algorithms (MOEAs) - NSGA2 and MO-CMA-ES, and a gradient-based L-BFGS-B approach. To guide the optimization, we propose hybrid versions of the mentioned algorithms by applying an advanced initialization scheme that is based on the exact bidirectional Dijkstra algorithm. We compare the different algorithms with and without hybrid initialization in a statistical analysis, which considers the number of function evaluations and quality features of the obtained Pareto fronts indicating convergence and diversity of the solutions. We evaluate the methods on a realistic 3D urban path planning scenario in New York City, based on real-world data exported from OpenStreetMap. The examination's results indicate that hybrid initialization is the main factor for the efficient identification of near-optimal solutions.
UPANets: Learning from the Universal Pixel Attention Neworks

With the successful development in computer vision, building a deep convolutional neural network (CNNs) has been mainstream, considering the character of shared parameters in a convolutional layer. Stacking convolutional layers into a deep structure improves performance, but over-stacking also ramps up the needed resources for GPUs. Seeing another surge of Transformers in computer vision, the issue has aroused severely. A resource-hungry model is hardly implemented for limited hardware or single-customers-based GPU. Therefore, this work focuses on these concerns and proposes an efficient but robust backbone, which equips with channel and spatial direction attentions, so the attentions help to expand receptive fields in shallow convolutional layers and pass the information to every layer. An attention-boosted network based on already efficient CNNs, Universal Pixel Attention Networks (UPANets), is proposed. Through a series of experiments, UPANets fulfil the purposes of learning global information with less needed resources and outshine many existing SOTAs in CIFAR-{10, 100}.
A Trustworthy decentralized Change Propagation Mechanism for Declarative Choreographies

Blockchain technologies have emerged to serve as a trust basis for the monitoring and execution of business processes, particularly business process choreographies. However, dealing with changes in smart contract-enabled business processes remains an open issue. For any required modification to an existing smart contract (SC), a new version of the SC with a new address is deployed on the blockchain and stored in a contract registry. Moreover, in a choreography, a change in a partner process might affect the processes of other partners. Thus, the change effect must be propagated to partners of the choreography affected by the change. In this paper, we propose a new approach overcoming the limitations of SCs and allowing for the change management of blockchain-enabled declarative business process choreographies modeled as DCR graphs. Our approach allows a partner in a running blockchain-based DCR choreography instance to change its private DCR process. A change impacting other partners is propagated to their affected processes using a SC. The change propagation mechanism ensures the compatibility checks between public DCR processes of the partners. We demonstrate the approach's feasibility through an implemented prototype.
A Graph Matching Model for Designer Team Selection for Collaborative Design Crowdsourcing Tasks in Social Manufacturing

In order to find a suitable designer team for the collaborative design crowdsourcing task of a product, we consider the matching problem between collaborative design crowdsourcing task network graph and the designer network graph. Due to the difference in the nodes and edges of the two types of graphs, we propose a graph matching model based on a similar structure. The model first uses the Graph Convolutional Network to extract features of the graph structure to obtain the node-level embeddings. Secondly, an attention mechanism considering the differences in the importance of different nodes in the graph assigns different weights to different nodes to aggregate node-level embeddings into graph-level embeddings. Finally, the graph-level embeddings of the two graphs to be matched are input into a multi-layer fully connected neural network to obtain the similarity score of the graph pair after they are obtained from the concat operation. We compare our model with the basic model based on four evaluation metrics in two datasets. The experimental results show that our model can more accurately find graph pairs based on a similar structure. The crankshaft linkage mechanism produced by the enterprise is taken as an example to verify the practicality and applicability of our model and method.
Whether Cryptocurrency is a Tool of Investment?

In recent years, cryptocurrency is developing at a fast pace because of the big fluctuation in the prices of bitcoins.An increasing number of people decide to invest their assets into bitcoins or other types of cryptocurrencies.As a result, the idea of whether bitcoin is a good tool of investment has come out.This study describes, analyzes, and compares the different areas in bitcoins and finally draws a conclusion of bitcoins is tools of investment or not.Statistical measurements such as standard deviation, expected return, and skewness are adopted.The results are that bitcoins can bring a large amount of expected return, but also as well as a larger standard deviation, i.e., higher risks.Also, celebrities, such as Elon Musk, can have a huge impact on the market behavior of bitcoins, and it is easy for them to manipulate the market.The dramatic increase in trades of bitcoins encourages the development of other cryptocurrencies as well; for instance, dogecoins and SHIBA INU coins.This scenario leads to a mass of the market at a particular time so many governments attempted different policies to regulate the market.For example, China and the US are the two countries that held completely different views on cryptocurrency.
New Attacks on the GPRS Encryption Algorithms GEA-1 and GEA-2

GEA-1 and its successor GEA-2 are two stream ciphers, designed to protect against eavesdropping General Packet Radio Service (GPRS) between the phone and the base station. They were widely used for GPRS encryption in the late 1990s and during the 2000s, and are surprisingly still supported in a range of current mobile phones. In this paper, new key recovery attacks on GEA-1 and GEA-2 are proposed by combining with the time-memory trade-off technique. The new attacks significantly reduce the time and memory costs of the previous attacks on GEA-1, and show that GEA-1 only offers 32-bit (out of 64) security. Furthermore, the slide properties of GEA-1 and GEA-2 are first found and used to explore practical related key attacks. The results show that GEA-1 and GEA-2 can be broken on a common PC within about 0.81 and 6.2 seconds in the multiple related key setting, respectively. Finally, an improved variant of GEA-2 called GEA-2a is proposed. The reasons for making the changes from GEA-2 are explained in detail, which indicates that GEA-2a has significantly better resistance than GEA-2 against all known attacks and can offer 64-bit security.
CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework

In this paper, we propose a framework for evaluation of co-salient object detection algorithms along with two novel CoSOD methods. The processing pipeline of this framework is based on the CoEGNet algorithm, where the saliency detection part can be easily replaced by any saliency detector to be evaluated. By leveraging the proposed framework, we developed two new algorithms: one based on U2Net and the other based on the label decoupling framework (LDF). They are called in this paper CoU2Net and CoLDF, respectively. The proposed solutions were tested on three datasets: CoCA, CoSal2015, and CoSOD3k, and compared with some of the best algorithms in co-silent object detection: GICD and CoEGNet. The advantages and disadvantages of the proposed methods are highlighted and discussed. As a generalization of the aforementioned methods, a framework called Sal.Co has been also proposed. It is a modification of the CoEGNet method and it works on a saliency mask obtained from a saliency detector and attempts to indicate salient objects coexisting in a group of images. Both CoLDF and CoU2Net achieved better results than CoEGNet on the CoCA dataset. On the CoSal2015 and CoSOD3k datasets, they performed similarly to state-of-the-art methods, while maintaining a highly customizable structure. The source code can be found at: https://github.com/jakubkorczakowski/cosal_sal_testing.
Implicit Semantic Data Augmentation for Hand Pose Estimation

Data augmentation is a well-known technique used for improving the generalization performance of modern neural networks.After the success of several traditional random data augmentation for images (including flipping, translation, or rotation), a recent surge of interest in implicit data augmentation techniques occurs to complement random data augmentation techniques.Implicit data augmentation augments training samples in feature space, rather than in pixel space, resulting in the generation of semantically meaningful data.Several techniques on implicit data augmentation have been introduced for classification tasks.However, few approaches have been introduced for regression tasks with continuous/structured labels, such as object pose estimation.Hence, we are motivated to propose a method for implicit semantic data augmentation for hand pose estimation.By considering semantic distances of hand poses, the proposed method implicitly generates extra training samples in feature space.We propose two additional techniques to improve the performance of this augmentation: metric learning and hand-dependent augmentation.Metric learning aims to learn feature representations to reflect the semantic distance of data.For hand pose estimation, the distribution of augmented hand poses can be regulated by managing the distribution of feature representations.Meanwhile, hand-dependent augmentation is specifically designed for hand pose estimation to prevent semantically meaningless hand poses from being generated (e.g., hands generated by simple interpolation between both hands).Further, we demonstrate the effectiveness of the proposed technique using two well-known hand pose datasets: STB and RHD.
The Quantum Internet: A Hardware Review

In the century following its discovery, applications for quantum physics are opening a new world of technological possibilities. With the current decade witnessing quantum supremacy, quantum technologies are already starting to change the ways information is generated, transmitted, stored and processed. The next major milestone in quantum technology is already rapidly emerging—the quantum internet. Since light is the most logical candidate for quantum communication, quantum photonics is a critical enabling technology. This paper reviews the hardware aspects of the quantum internet, mainly from a photonics perspective. Though a plethora of quantum technologies and devices have emerged in recent years, we are more focused on devices or components that may enable the quantum internet. Our approach is primarily qualitative, providing a broad overview of the necessary technologies for a large-scale quantum internet.
Integration of Augmented Reality and Voice Recognition in Learning English for Children

Application development by combining two technologies, namely Augmented Reality and Voice Recognition, can make learning media regarding object recognition at home interact directly with 3D virtual objects. The technology can also help the pronunciation or pronunciation of sentences in English. Natural Language Processing (NLP) is used to understand human language so that machines can understand and process it. This ability supports Voice Recognition to have intelligence and interact like humans. wit.ai is an open-source NLP platform that can support speech-to-text application development. The merging of the two technologies in this development using the wit.ai platform. With the wit.ai platform that is used to understand voice commands and perform tasks as needed for applications regarding object recognition at home, users will be able to interact with objects at home through the given voice commands. In the Black Box testing, each functionality got the results that all the features had functioned properly. User Acceptance Test was also carried out and the average test results were 95.77% and 93.26% on a Likert scale with test results on 13 respondents aged 6-9 years old who have tried the application and 14 respondents as observers when 13 respondents aged 6-9 years tried the application. These results show that the application can be accepted and used as a tool in learning media.
The interplay among higher education, technology, innovation and labour market efficiency: Middle East case

The study builds a model that examines the effect of the quality of higher education and training on both innovation and the labour market efficiency. In addition the proposed model reflects the effect of the quality of technology readiness on the same factors using a sample of 12 Middle East countries from the period (2012 to 2018). The study uses the partial least squares method to estimate the model that shows the relationship among multiple dependent and independent variables, constructs unobserved factors, and finally tests the pre-defined assumptions against empirical data. The model suggests that the increase in the quality of higher education will increase the quality of both innovation and labour market efficiency. There is a significant positive effect of technology on higher education and training, innovation, and labour market efficiency. The study offers new perspectives on the relationships among the factors included in the proposed model.
Deep Learning based Ship Variants Classification Using Different Scale Images

Nowadays, trade based on logistics and sea transportation has gained importance. Considering the traffic related to this, the classification and discrimination of ship types are important in terms of transportation and storage costs, and safety. The classification of ships performing different tasks on the sea is handled in this study, and a ship image dataset has been created in order to make a high accuracy ship classification thanks to deep learning methods. It is preferred in our deep learning study in comparison to the classical machine learning method, as the features are semantically richer as the higher level, while expressing content from the dataset. It was created by the acquisition of various ship images thanks to the web scraping method. YOLOv5 and Xception deep learning models were trained to obtain the most appropriate classification performance. As a result of the experiments, an accuracy rate of approximately between 96% and 99% was achieved with both models. Scientific findings and discussion are included in our study as well.
Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model

Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, the prediction of students’ final course grades with continuous assessment marks is a useful toolkit for degree program educators. Predictive models are practical tools used to evaluate the effectiveness of teaching as well as assessing the students’ progression and implementing interventions for the best learning outcomes. This study develops a novel multivariate adaptive regression spline (MARS) model to predict the weighted score WS (i.e., the course grade). To construct the proposed MARS model, Introductory Engineering Mathematics performance data over five years from the University of Southern Queensland, Australia, were used to design predictive models using input predictors of online quizzes, written assignments, and examination scores. About 60% of randomised predictor grade data were applied to train the model (with 25% of the training set used for validation) and 40% to test the model. Based on the cross-correlation of inputs vs. the WS, 12 distinct combinations with single (i.e., M1–M5) and multiple (M6–M12) features were created to assess the influence of each on the WS with results bench-marked via a decision tree regression (DTR), kernel ridge regression (KRR), and a k-nearest neighbour (KNN) model. The influence of each predictor on WS clearly showed that online quizzes provide the least contribution. However, the MARS model improved dramatically by including written assignments and examination scores. The research demonstrates the merits of the proposed MARS model in uncovering relationships among continuous learning variables, which also provides a distinct advantage to educators in developing early intervention and moderating their teaching by predicting the performance of students ahead of final outcome for a course. The findings and future application have significant practical implications in teaching and learning interventions or planning aimed to improve graduate outcomes in undergraduate engineering program cohorts.
What Do the Biggest US Banks Disclosure About Green Intellectual Capital in the Period of COVID-19 Crisis?

The purpose of this study is to explore the corporate news related to the topic of green intellectual capital in 6 biggest US banks in the period of Covid-19 pandemic crisis.In order to answer the research question, computerized lexical qualitative content analyses were done of 50 corporate separate news (texts) from March 2020 until February 2022, with 40,126 words.The research study concentrates on 4 main clusters of news that focus on (1) green human capital; (2) green structural capital; (3) green relational capital, and (4) financial performance as a company's potential to gain competitive advantage.The research findings prove that biggest US banks disclosure intensively in public about green intellectual capital together with its components.These green intellectual resources are sufficiently present in communication practices.The article contributes to the current literature on green intellectual capital narratives.It tends to provide explanation how biggest US banks companies interpret green intellectual capital in its corporate news and which components companies focus most in order to gain competitive advantage and better financial results.
A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built.
From virtual reality to augmented reality: A neuromarketing perspective

OPINION article Front. Psychol., 09 September 2022Sec. Organizational Psychology https://doi.org/10.3389/fpsyg.2022.965499
AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal Generation

AI illustrator aims to automatically design visually appealing images for books to provoke rich thoughts and emotions. To achieve this goal, we propose a framework for translating raw descriptions with complex semantics into semantically corresponding images. The main challenge lies in the complexity of the semantics of raw descriptions, which may be hard to be visualized e.g., "gloomy" or "Asian"). It usually poses challenges for existing methods to handle such descriptions. To address this issue, we propose a Prompt-based Cross-Modal Generation Framework (PCM-Frame) to leverage two powerful pre-trained models, including CLIP and StyleGAN. Our framework consists of two components: a projection module from Text Embeddings to Image Embeddings based on prompts, and an adapted image generation module built on StyleGAN which takes Image Embeddings as inputs and is trained by combined semantic consistency losses. To bridge the gap between realistic images and illustration designs, we further adopt a stylization model as post-processing in our framework for better visual effects. Benefiting from the pre-trained models, our method can handle complex descriptions and does not require external paired data for training. Furthermore, we have built a benchmark that consists of 200 descriptions from literature books or online resources. We conduct a user study to demonstrate our superiority over the competing methods of text-to-image translation with complicated semantics.