Reviewerly/exHarmony huggingface dataset:
Transformer-based Image Generation from Scene Graphs

Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination of graph convolutional networks and adversarial methods for layout prediction and image generation, respectively. In this work, we show how employing multi-head attention to encode the graph information, as well as using a transformer-based model in the latent space for image generation can improve the quality of the sampled data, without the need to employ adversarial models with the subsequent advantage in terms of training stability. The proposed approach, specifically, is entirely based on transformer architectures both for encoding scene graphs into intermediate object layouts and for decoding these layouts into images, passing through a lower dimensional space learned by a vector-quantized variational autoencoder. Our approach shows an improved image quality with respect to state-of-the-art methods as well as a higher degree of diversity among multiple generations from the same scene graph. We evaluate our approach on three public datasets: Visual Genome, COCO, and CLEVR. We achieve an Inception Score of 13.7 and 12.8, and an FID of 52.3 and 60.3, on COCO and Visual Genome, respectively. We perform ablation studies on our contributions to assess the impact of each component. Code is available at https://github.com/perceivelab/trf-sg2im
CBF Based Geometric Visual Servoing Control for Quadrotors with FOV Constraint

The basic prerequisite of visual servoing control for a quadrotor unmanned aerial vehicle (UAV) is to ensure that the visual targets are always within the field of view (FOV) of the camera. In this paper, a new geometric control approach is proposed under FOV constraint for the quadrotor, in which the control barrier function (CBF) is used to constrain the states of the quadrotor to guarantee the visibility of visual targets. Specifically, the CBFs related to the quadrotor states are firstly constructed to satisfy the forward invariance of the safety set, and the states of the quadrotor are restricted within the safe area. Then, control inputs calculated from the geometric controller are refined by quadratic programming (QP) with minimal modification. Comparative simulation results show the remarkable performance of the proposed method in ensuring the visibility of visual targets of the quadrotor visual servoing.
Research on Online Flight Path Optimization Algorithm of Aircraft Based on Convex Optimization Method

Flight path optimization is to design an optimal flight path that minimizes an objective function under certain constraints. Because of the existence of many constraints, the objective function is non-convex function and most of the constraints are non-convex constraints, the flight path optimization problem of the aircraft is non-convex optimization problem with high constraints, and it is very difficult to solve the optimal control problem. In this paper, a convex optimization method is used to solve the flight path optimization problem of aircraft. The original non-convex optimization problem is transformed into a convex optimization problem after convex optimization treatment, which has the potential to solve complex optimization problems online and in real time. Simulation results show that the optimal solution can be obtained in a short time by using this method to solve the flight path optimization problem of aircraft. Compared with other optimization algorithms, the optimization time can be increased by more than 90%, which has good adaptability and high engineering application value.
The Influence of New Media Art on the Instrumentalist and on his Performance: New Performative Paths

The aim of this article is to identify and discuss the evolution fostered by the use of digital technologies in the instrumental music performance. We limit the focus to the musical performance of instruments identified as belonging to the orchestral family of Western tradition, in order to measure the repercussions on both the performance and the instrumentalist and considering three key aspects of new media art: interactivity, controlled randomness, and virtuality. On one hand, the junction of these two areas - new media art and instrumental music performance - enables the acquisition of digital characteristics to the traditional instruments and to their performance, providing new interactive and iterative paths. On the other hand, the same junction also leads to a unique level of performance due to the high level of instrumentalists’ expertise, allowing the work to be singular and unrepeatable. All this leads to an evolution of musical notation, the creation of new instrumental and performance techniques, new concepts, a multidisciplinary broadening of the instrumentalists’ role and an evolution in the relationship between new media art and instrumental music. Regarding the structure, the article firstly presents a definition of both areas, and then develops the three aspects mentioned with the addition of the generativity as an inherent aspect, identifying the different manners in which these artistic characteristics are related and evolved, as well as their repercussion in the instrumental music performance.
Risk Analysis of Major Factors Causing Drivers’ Excess Speed Choices Using Fuzzy Dematel Method

The interrelation between drivers’ speed choices has not been clearly identified. Understanding the factors that influence drivers' choice of speed is a critical element of safety research, as it is an important factor in both the probability and severity of vehicle crashes. To identify key factors of excess speed in the traffic system, it is very important to ask for experts’ views and to analyze these views. In this study, fuzzy DEMATEL method, which is a combination of fuzzy and DEMATEL approaches, was preferred for analysis of critical factors which influence drivers’ speed choices. By taking into account independent and stochastic evaluations within a specific analytical dimension, fuzzy DEMATEL can be used to solve various challenges and produce a set of useful and practical choices. Five important factors affecting driver’s speed choose were used in this study. Criterion1, legal regulations and traffic inspection. Criterion2, the driver’s inability to percept hazard and presume accident risks. Criterion3, the driver’s will to test his driving talents and his car’s capabilities. Criterion4, the driver’s target to lessen driving time on the roads. Criterion5, the uncontrolled emotions of the driver’s (Getting angry fast and inability to suppress angriness, low frustration tolerance, lack of focus, feeling of being the best driver) (psychological illnesses). The driver’s thrill seeking and the idea of having fun. In this study, criterion3, criterion4 are considered to be as a causal variable, criterion1, criterion2, criterion5 are regarded as an effect. In terms of degree of importance, criterion2 is ranked in first place and criterion5, criterion4, criterion1 and criterion3, are ranked in the next places. According to D+R and D-R values; criterion 3 is the criterion that affects the model the most with the highest D-R value. Criterion 2 is the most important criterion in the model with the highest D+R value.
Exploring the Correlation between Motivation, Organizational Culture, and Employee Performance: Job Satisfaction as a Mediator in a Dynamic Environment

This study seeks to examine the correlation between motivation, organizational culture and employee performance and how job satisfaction can act as a mediator in higher education. By understanding the connection between these factors organizations can create a more productive and positive work environment. This research provide valuable insight into how to create a culture of motivation and satisfaction that lead to improved employee performance. 364 participants were selected from Yemeni and Omani universities including academics and staff randomly. Questionnaires were contributed online by using emails and social media applications and analyzed by (PLS). Results of the Structural Equation Model-Part Least Square (SEM-PLS 4) reveal that extrinsic motivation has negative impact on employee performance while organizational culture has positive significant impact in dynamic environments. Results also highlighted the positive role of job satisfaction represented by supervisors-employee engagement, incentives and promotion in enhancing performance in dynamic environment. Research recommend that when it comes to a dynamic environment, the best approach to follow is to adopt aggressive way with extrinsic motivation to increase job satisfaction and employee performance.
Modelling Geochemical Indices from Hyperspectral Drill Core Data from the Eucla Basin Basement

Whole rock geochemical analysis of drill core is crucial for rock classification and understanding the evolution of a geological province. This becomes even more important when access to fresh rock samples is hindered by considerable overburden, such as in the case of the basement to the Eucla Basin sediments in Australia. A partial least squares (PLS) regression method was trialled to model geochemical indices, commonly applied for classification of igneous rocks, from hyperspectral visible-near infrared (VNIR; 350 to 1200 nm), shortwave infrared (SWIR; 1200 to 2500 nm) and thermal infrared (TIR; 6000 to 14500 nm) reflectance spectra of three drill cores intersecting the basement of the Eucla Basin. Modelling of the Mg# (Mg/Mg+Fe) achieved a high correlation between the calculated and predicted values (i.e. R2 > 0.9) at an RMSE of about 10%. The 2200 to 2400 nm wavelength region of the SWIR was key in modelling the Mg#, with amphibole and chlorite-related absorption features overlapping with the most important input bands in the PLS model. Modelling of the silicon, calcium, iron, magnesium index (SCFM; SiO2/(SiO2+CaO+FeO+MgO)) required the TIR wavelength range, where diagnostic spectral signatures of silicates are located. PLS-based modelling of the SCFM achieved even a high correlation (i.e. R2 > 0.8) when the model was developed for one drill core but applied to another drill core that intersects the same lithologies. The third geochemical index trialled was the aluminium saturation index (ASI; Al2O3/(Na2O+K2O+CaO), producing high correlation between calculated and predicted ASI when using either, the VNIR-SWIR and the TIR wavelength ranges as input. Our study demonstrates how PLS regression can utilise hyperspectral data to determine geochemical indices, without the cost of full geochemical analysis of every sample in a wide array of rock types. The approach presented here can be used to I) advance stratigraphic correlation of basement and cover rocks based on objective drill core mineralogy, II) map intensity of weathering of basement, and III) map mineralogical and physicochemical gradients potentially related to hydrothermal systems.
Design and Development of Multithreaded Web Crawler for Efficient Extraction of Research Data

The semantic Web renders the search, machine friendly rather than human friendly. Semantic Web comprises of a set of principles, various tools, and techniques which enables people to share their content on the Web and participate in a collaborative work for quickly sharing the valuable information on Web. The current research focuses on the combined qualitative and quantitative research methodology to find out the specific requirements of data with respect to research area. The authors have designed and implemented semantic framework for querying the research data in machine readable format employing RDF, FOAF, and ontology semantic Web technologies. The current paper presents a multithreaded Web crawler module for extracting the attributes of interest pertaining to different attributes of research paper such as indexing, journal impact factor, processing charges, volume no, issue no, e-ISSN, and p-ISSN. The model is generic and can be applied to any domain for retrieving the attributes of interest. The research objective is to minimize the time involved in literally going through each journal listed by the search engine and manually finding out the requisite information.
Securing Data in Internet of Things (IoT) Using Elliptic Curve Cryptography

The data everywhere was being transferred for each second using domain of Internet of Things (IoT). To secure data using the Internet of Things (IoT) is a tedious task. When the data is being transferred using Internet of Things (IoT), more security can be provided using elliptic curve cryptography. Asymmetric cryptography is used by most of the applications for providing secure communication between two parties (Weber in Comput Law Secur Rev 26:23–30, 2010). The purpose of this type of cryptography is the requirement of huge amount of computation and storage. This is where the use of elliptic curve cryptography comes into picture, as it needs less storage and can be used in small computational devices. ECC needs smaller key sizes and provides stronger encryption compared to various asymmetric cryptographic algorithms like RSA. The usage of power required is low but the performance of the devices using ECC is high for different types of devices like IoT, sensors, etc. This paper shows how ECC must be implemented strongly for providing communication securely to encode the data on an elliptic curve, in IoT devices. The encryption of the data must be done securely while mapping the data on to the elliptic curve. This paper shows how the data is encrypted and mapped on to the curve securely. The work shows how the data can be encrypted with ECC and how it can be visible to only authorized users. In the field of cryptography, ECC is a method for asymmetric cryptography which is dependent on the algebraic structure of an elliptic curve on the finite field.
A Fast and Accurate Compound Collision Detector for Rrt Motion

A recent research wave has led to a wide application of artificial intelligence technology in robot motion planning. Benefited from machine learning, novel collision detectors have excellent computational efficiency compared with previous tools. However, these new approaches come with the problem in accuracy. To obtain a good trade-off between efficiency and accuracy, we propose a novel compound collision detector (CCD) for collision queries from planners of rapidly-exploring random tree (RRT). The collision detector is composed of an exact collision detector (ECD) and an inference collision detector (ICD), and chooses one of them for the current collision query strategically. Different from label-based compound detectors, such as probabilistic collision detector (PCD), the proposed compound detector implement queries via a continuous variable, which is called as sphere-ellipsoidal pseudo distance (SEPD). The exact detector calculates the exact SEPD values to obtain query results. The inference detector uses the local distribution of SEPD to infer query results. The SEPD based compound detector can relieve the problem of PCD, i.e., highly-frequent outputs of false-positive in narrow passages. In addition, node based bounding method (NBB) is used to provide local data for ICD. The method can simplify the process of data storage and loading. The experiments use Kinova Jaco assistive robotics arm as an example to evaluate the performance of the proposed detector. The experimental results indicate that the proposed method is promising in demanding tasks.
Intrusive video oculographic device: An eye-gaze-based device for communication

With the improvement in image processing and using the fact that eye movement can persist even with severe paralysis condition, we have developed a device that enables a severely paralysed patient to communicate. The device developed makes it possible for the patient to communicate in “yes” or “no” with the movement of the eyeball alone. In the proposed work, we have used Raspberry Pi as the main processor and Python as programming tool. A 5-megapixel camera is used to read eye movement, a speaker is used to speak “yes” or “no” for the patient and a normal power bank is used to supply the proposed setup. The designed device is efficient, cost-effective, lightweight, and can easily fit on the head.
UAV 3D Environmental Track Planning Based on Improved Ant Colony Algorithm

This paper proposes an improved algorithm for the UAV trajectory planning problem. The algorithm improves the performance of Ant Colony Algorithm (ACO) by solving the existing problems. First, the improved algorithm adjusts the initial pheromone size according to the distance between the front and rear nodes to avoid the randomness of the ants at the beginning of the algorithm. Then, a heuristic function is added to improve the convergence speed of the algorithm. Simultaneously, in order to reduce the influence of the worst path on the subsequent iterative process, the pheromone update rule is changed in the improved algorithm. Finally, through the comparison of the simulation experiments of the two algorithms in the same environment, the simulation results show that the improved algorithm has faster convergence speed, and the optimal fitness value and algorithm time-consuming are improved.
Characterizing Memory Access Patterns of Various Convolutional Neural Networks for Utilizing Processing-in-Memory

Convolutional neural network (CNN) models require deeper networks and more training data for better performance, which in turn results in greater computational and memory requirements. In this paper, we analyze the memory access patterns that occur in main memory during the training processes of various CNN models. CNN training is a linear procedure consisting of a forward pass (FP) and a backward pass (BP). As a result of the analysis, we found that BP accounted for 83.4% of the total main memory accesses on average. Therefore, CNN training including FP and BP is much more memory-intensive than CNN inference using only FP. This demonstrates that CNN training is a suitable application for near-data processing to reduce memory bottlenecks and conserve computational resources.
Quantum conditional entropies and steerability of states with maximally mixed marginals

Quantum steering is an asymmetric correlation which occupies a place between entanglement and Bell nonlocality. In the paradigmatic scenario involving the protagonists Alice and Bob, the entangled state shared between them is said to be steerable from Alice to Bob if the steering assemblage on Bob's side does not admit a local hidden state (LHS) description. Quantum conditional entropies, on the other hand, provide for another characterization of quantum correlations. Contrary to our common intuition, conditional entropies for some entangled states can be negative, marking a significant departure from the classical realm. Quantum steering and quantum nonlocality, in general, share an intricate relation with quantum conditional entropies. In the present contribution, we investigate this relationship. For a significant class, namely the two-qubit Weyl states, we show that negativity of conditional R\'enyi 2-entropy and conditional Tsallis 2-entropy is a necessary and sufficient condition for the violation of a suitably chosen three settings steering inequality. With respect to the same inequality we find an upper bound for the conditional R\'enyi 2-entropy, such that the general two-qubit state is steerable. Moving from a particular steering inequality to local hidden-state descriptions, we show that some two-qubit Weyl states which admit a LHS model possess nonnegative conditional R\'enyi 2-entropy. However, the same does not hold true for some non-Weyl states. Our study further investigates the relation between nonnegativity of conditional entropy and LHS models in two-qudits for the isotropic and Werner states. There we find that, whenever these states admit a LHS model, they possess a nonnegative conditional R\'enyi 2-entropy. We then observe that the same holds true for a noisy variant of the two-qudit Werner state.
The Revival of Pamphletary Speech in the Twenty-First Century: The Rhetoric of Reversal

ABSTRACT Through the study of three pamphleteer authors, Renaud Camus in France, Oriana Fallaci in Italy, and Fernando Vallejo in Colombia, this article explores the impact of recent legislations against hate speech on the literary field. It determines how literature, especially pamphlets, allows the reactionary ideological offensive to attempt to circumvent the law and studies the characterizing trait of this new twenty-first century pamphlet: the rhetoric of reversal.
IMPLEMENTATION OF E-TRAINING FOR EARLY CHILD EDUCATORS WITH A BLENDED APPROACH

The use of ICT in education has evolved into distance education management in the field of training, it aims to provide the widest possible access to education and training for trainees who do not have the opportunity to attend face-to-face education due to various problems. Based on Dapodik data, most playschool educators or teachers in Indonesia do not have an educational background that can support their competence. Thus, education and training services are one of the efforts that can be made to encourage the improvement of the competence of playschool educators. Through e-training with a blended approach, it is expected to be a solution because it has advantages including 1) affordable and efficient costs; 2) unlimited quota; 3) can be accessed via the internet; 4) flexible time. The approach taken is a qualitative research approach with a descriptive method using purposive sampling technique which representing training activities in Bogor Regency, Karang Regency, and Bekasi Regency, West Java, Indonesia. The results of the research on the application of e-training with a blended approach for playschool educators show a new model in the implementation of learning, namely combining face-to-face learning with online. Adaptation to e-training with a blended approach must be carried out following the changes and developments of an increasingly rapid era without compromising the objectives and learning outcomes in the training program.