Reviewerly/exHarmony huggingface dataset:
Mission assignment and 3D path planning for a team of UAVs

Significant topics for Unmanned Aerial Vehicle (UAV) flight are represented by path planning and task assignment in a 3D environment. In this paper we propose a mathematical formulation designed as a Mixed Integer Linear Programming (MILP) problem that couples the two mentioned topics. This optimization problem provides an optimal solution for a problem where multiple drones have to individually paint some regions of interest. Several constraints were defined based on energy and paint consumption, while the cost function is based on the time for completing the painting task. For the proposed solution, the impact of number of drones, number of regions of interest and the size of the graph modelling the environment, is asserted and visualized through numerical simulations.
A Dynamic and Fair Timeout Heartbeat Detection Technique for Server Clusters Using Nginx Reverse Proxy

In recent years, more and more SMEs use their existing equipment enterprises to form server clusters for the internal welfare platform of enterprises through Nginx reverse proxy. For this kind of platform, the health detection mechanism of nodes in the server cluster plays a very important role in ensuring the high availability of the system. Nginx’s native health detection mechanism is very weak. The widely used Tengine provides Nginx with a separate health detection module, but it still fails to consider the problem of different node performance caused by heterogeneous hardware and uneven load on software, and uses a unified heartbeat detection strategy, which leads to the problem that Nginx cannot detect the faulty node in time or mistakenly determines the “Fake dead” node as the faulted node. In this paper, node performance parameters such as CPU utilization and IO utilization are selected to construct a weight calculation model by entropy method to quantify node performance, and then a node fault misjudgment loss model is constructed to optimize the accuracy of node fault judgment. Finally, a dynamic fair timeout algorithm based on Nginx is proposed to make Nginx’s heartbeat timeout strategy more fair and real-time. Experiments show that compared with the heartbeat detection technology before improvement, the algorithm proposed in this paper improves the accuracy of node fault detection by 18%, and this algorithm is an effective technology to improve the high availability of cluster.
Combating the Cyber-Security Kill Chain: Moving to a Proactive Security Model

A former boss of mine (Peter Drissell (https://www.linkedin.com/in/peter-drissell-b917896/) (Commandant General RAF Regiment Air Officer Royal Air Force Police) once delivered a presentation at a University lecture, which I had been attending. Here he made the following statement: Many business leaders regard Security as being very expensive and virtually invisible. That is until it goes wrong, when it becomes very visible and considerably more expensive! Ever since hearing this statement, I have sought to change this view. Having a proactive, asset and risk focused approach that is aligned with the business mission statements/objectives has a significant impact on changing the business leaders’ perspectives. This chapter seeks to explain how you can start to reduce the opportunities for the cyber-attackers, through a more targeted and prioritized approach. Many organizations are feeling a sense of Cyber-security fatigue and often sensing that the cyber-criminals have got the upper hand and that this is a battle that they are losing, frequently believing that they are ‘Boiling the Ocean’. If a business fails to identify and categorize their assets, they will not be able to truly appreciate the value of their most important company assets, and their importance to the business. Consequently, when it comes to carrying out the risk assessments, it can often feel like this is based upon a premonition or a hunch. Additionally, when it comes to applying appropriate mitigation controls, this can be extremely difficult to show proportionality and a return on investment.
Feasibility of applying neural network in the mechanical designing

Parameter-calculation is an important part of the machine design process and this stage usually takes a lot of time. The workload of parameter-calculation is always proportional to the complexity and fineness of the design. As the demand for variety machines keeps increasing these years, a better method is necessary to increase the efficiency and quality of calculations during design a new machine. In this paper, neural network is thought of the solution. By learning from the researches in neural network and finite element analysis which is broadly used in the area of engineering presently. The neural network is found to have a better performance in efficiency, accuracy and cost performance and the results indicate neural network could be a better alternative of the finite element analysis.
Learning Independent Sparse Representation in Application to Symmetry Detection Problem

We continue to research biologically plausible learning methods and apply them to symmetry detection problem. We used the Hebbian-like learning rule, proposed in our previous work and upgraded it with a new algorithm of interaction of neurons of hidden layers. This algorithm relays only on pairwise interaction between neurons. Considering activation statistics, neurons suppress each other in case of statistically dependence. This allow them to learn statistically independent patterns. Together such neurons form what we call “sparse independent representation”.We use symmetry detection problem as a benchmark to test the proposed method. This problem provides an almost infinite number of training samples. Therefore, the method should be able to develop a generalizing ability to solve the problem.
Anomaly Detection and Diagnosis Using Pre-Processing and Time-Delay Autoencoder

This paper proposes an anomaly detection algorithm for a factory automation system, which jointly performs data pre-processing and time-delay autoencoder (TDAE) with a hybrid loss function. The source data are pre-processed by digital filters before feeding into a TDAE for anomaly detection. The digital filters extract analog signals from a variety of frequency bands to facilitate identifying anomalies. The pre-processed data then takes time-delay reform to explore temporal relationship of data signals. In addition, two anomaly diagnosis algorithms, a statistical based method and an autoencoder based method, are presented. Numerical results show that time-delay reform can improve the anomaly detection accuracy compared to the conventional autoencoder. Data pre-processing can further improve the anomaly detection accuracy. Moreover, we confirm that our anomaly diagnosis algorithms outperform traditional method that does not perform data pre-processing and time-delay reform.
PREDICTING THE SPEED OF ACHIEVING RESULTS IN THE SELF-DESIGN OF EDUCATIONAL AND PROFESSIONAL ACTIVITIES

The article presents the results of multivariate regression analysis of the relationship between the rate of achievement of learning outcomes with variables that correspond to the individual characteristics of students. The constructed mathematical model includes three variables (purposefulness, internal locus of control, self-management), the coefficients of which are used to construct the regression equation. The presented mathematical model has practical value and can be used to predict learning outcomes in self-correction of educational and professional activities.
WBTP-VTON: Whole Body and Texture Preservation based Virtual Try-On Network

Image-based virtual clothes try-on systems are becoming more and more popular. However, many challenges are waiting to be solved. Therefore, we propose a new fully learnable method, called the whole body and texture preservation based virtual try-on network (WBTP-VTON) to guide the virtual attempt to deal with all practical challenges in this area. First, the WBTP-VTON template conversion is used to transform the target clothing and pants (or skirts) according to the body shape of the target person using a method called Geometric Matching Module (GMM). The second part is to synthesize the final image and make the generated results more realistic. Finally, we use try-on modules and synthetic masks to combine the deformed clothes and the final image to ensure image smoothness. After experimenting on a large data set, it is proved that our WBTP-VTON method has advanced virtual try-on performance.
Gold price simulation under quantum mechanical perspective

Gold price is an impactful variable in the gold mine project evaluation. The gold price is one of the most discussed variables in the literatures due to its uncertainty. In the gold mine project evaluation, gold price modeling is one activity commonly done to anticipate the project's risk. In this study, the gold prices within certain periods of time were modeled using quantum mechanics (QM) as a particle in a one-dimensional infinite well. Using this analogy, we demonstrate that the associated wave function leads to a Born probability density which could explain the gold price uncertainty. In addition, QM views the gold price distribution having multiple peaks, which actually happened in some periods of time, as a confined particle in an excited state. Essentially, this research proposed a gold mine project risk analysis where the probability distribution of the gold prices follows the QM perspective. The approach can also be used for different mining project and different uncertain variables by adjusting the QM parameters.
On Cognitive Management Architectures for Enhancing Septic Shock Predictions in Intensive Care Units

Although today’s advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel (nurses, doctors, etc). These problems are most severe in critical care environments such as Intensive Care Units (ICUs) where many events are life-threatening and thus require immediate attention and the implementation of definitive corrective actions. Septic syndrome is one of the most common pathogens of ICUs. Early diagnosis of this condition is complicated by nonspecific clinical signs and symptoms and the fact that not all infections lead to sepsis and to progression into septic shock. On this basis, the use of Cognitive Clinical Management Systems (CCMSs) is receiving increasing interest in recent years, enabling increased precision of diagnostics and novel clinical decision making solutions. Such systems operate on the basis of collecting information from various sources, intelligently processing it, integrating knowledge and experience, and finally, taking the most appropriate clinical decisions. Following the above, the ambition of the present study is to create a CCMS architecture for direct patient use with real-time monitoring, namely I-CSSP, being able to support intelligent septic shock predictions in ICUs.
Structured face feature extraction algorithm based on elastic embedding

Traditional linear feature extraction methods, such as PCA and LDA, are based on global structure, but human faces may exist approximately on a nonlinear low-dimensional submanifold in high-dimensional data space. Aiming at the problems of high spatial complexity and poor real-time performance of traditional elastic graph matching algorithm for face recognition, an improved elastic graph matching algorithm is proposed. Firstly, Gabor is used to detect and locate face features (such as eyes, nose, mouth, etc.), and a set of Gabor wavelet coefficient vectors are extracted; Then, under the constraint of cost function, elastic graph matching is performed for each feature. The improved method proposed in this paper overcomes the problems of inaccurate face subregion, missing feature information in the process of face roughness extraction and poor real-time performance of the algorithm. The improved algorithm obviously improves the efficiency and accuracy of gender recognition.
A MAP-Based Hybrid Cryptography Algorithm for Low-Cost Secret Communication

In the era where the security, privacy and bandwidth of communication matter the most, this paper focuses on a map-based algorithm integrated with a hybrid cryptography algorithm in order to reduce the congestion in networks and also increases the security and privacy in open networks like the Internet. Compression is the way to compact long cipher texts, which can cause consumption of large bandwidth in networks and delays in communication due to its large size. Encryption is the way to ensure privacy in open network data transfers. This paper aims to amalgamate both compression and encryption of data toward low-cost secured communication.
COVID-19 Contact Tracing Using Geolocation

COVID-19 is an infectious disease caused by the newly discovered coronavirus. With the number of detected cases increasing exponentially each day, it is very challenging to track the movements of an infected person for the duration of the incubation period of the virus. As there is no active vaccination that has been developed yet, it has become a huge concern globally to mitigate the effects of this virus. Countries across the world have been looking for ways to prevent the spread of this disease. There are various precautionary measures implemented worldwide, but this might not be very effective in the future for containing the transmission of the virus. This necessitates the development of a technique which can help track the whereabouts of the infected person and trace the spread of the virus. The aim of this paper is to discuss our progressive Web application (PWA) which compares and matches the user's data with the data of a patient who has tested positive for COVID-19. This PWA uses geolocation to find the real-world geographic location of a person without Bluetooth and GPS turned on at all times. It alerts a user if they have come in contact with a COVID-positive patient in any particular place at any specific instance of time to take the necessary precautions.
Small Data and Process in Data Visualization: The Radical Translations Case Study

This paper uses the collaborative project Radical Translations [1] as case study to examine some of the theoretical perspectives informing the adoption and critique of data visualization in the digital humanities with applied examples in context. It showcases how data visualization is used within a King’s Digital Lab project lifecycle to facilitate collaborative data exploration within the project interdisciplinary team – to support data curation and cleaning and/or to guide the design process – as well as data analysis by users external to the team. Theoretical issues around bridging the gap between approaches adopted for small and/or large-scale datasets are addressed from functional perspectives with reference to evolving data modelling and software development lifecycle approaches and workflows. While anchored to the specific context of the project under examination, some of the identified trade-offs have epistemological value beyond the specific case study iterations and its design solutions.
Modeling Intrinsic Motivation in ACT-R

To date, many studies concerned with intrinsic motivation in humans and artificial agents based on a reinforcement learning framework have been conducted. However, these studies have rarely explained the correspondence between intrinsic motivation and other essential cognitive functions. This study aims to build a method to express curiosity in new environments via the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. To validate the effectiveness of this proposal, we implement several models of varying complexity using the method, and we confirm that the model’s behavior is consistent with human learning. This method focuses on the“ production compilation” and ”utility” modules, which are generic functions of ACT-R. It regards pattern matching with the environment as a source of intellectual curiosity. We prepared three cognitive models of path planning representing different levels of thinking. We made them learn in multiple-breadth maze environments while manipulating the strength of intellectual curiosity, which is a type of intrinsic motivation. The results showed that intellectual curiosity in learning an environment negatively affected the model with a shallow level of thinking but was influential on the model with a deliberative level of thinking. We consider the results to be consistent with the psychological theories of intrinsic motivation. Furthermore, we implemented the model using a conventional reinforcement learning agent and compared it with the proposed method.
Ways and Tools to Ensure the Safety of the Educational Environment at School and University

Aim. To clarify the contents of such phenomena as ways and means to ensure the safety of the educational environment and to justify their use in relation to the educational environment of the university and school. Procedure and methods. The article econsiders the content of ways and means to ensure the safety of the educational environment, from the point of view of a philosophical approach to understanding this phenomenon, and therationale for the rationale for the options and means to ensure the safety of the educational environment for the university and the school as a system. In the study methods are used: analogies, comparative, generalization, system, interpretation of the results, thought experiment. The results. The results of the study contribute to the palette of research into the ways and means to ensure the safety of the educational environment in school and university, to the methodology of organizing the safety of the educational environment, as well as to the practice of educational activities of schools and universities. Theoretical and/or practical significance. The results of the study contribute to the palette of research of the educational environment in school and university, to the methodology of organizing the safety of the educational environment, as well as to the practice of educational activities of schools and universities.
Are Developers Equally Concerned About Making Their APIs RESTful and the Linguistic Quality? A Study on Google APIs

REST (REpresentational State Transfer) is an architectural style for distributed, hypermedia systems that allows communication between clients and servers using the HTTP methods and URIs (Uniform Resource Identifiers). In the literature, researchers and practitioners defined best design practices, i.e., REST patterns, violation of which are known as REST antipatterns. Also, clients need to understand the use and purpose of APIs while consuming them. A set of best practices is defined in the literature for APIs to have a better linguistic design, i.e., linguistic patterns, violation of which are known as linguistic antipatterns. For API developers, it is challenging to ensure that their APIs are RESTful and manifest linguistic design quality. This paper investigates whether developers are equally concerned about making their APIs RESTful while also focus on designing APIs with better linguistic quality that may facilitate their comprehension and consumption. Thus, we examine the relation between RESTful and linguistic design quality in RESTful APIs. We analyzed eight Google APIs and performed the detection of 21 patterns and antipatterns on those APIs. Using the quantitative data, we performed a series of statistical tests. Results suggest a negligible relationship between RESTful and linguistic design quality. Thus, developers are unaware of whether they conjointly lack RESTful and linguistic design quality.
Information Visualization in Mental Health Research and Practice

Understanding the complex relationships between a range of disparate types of data including (but not limited to) clinical signs and symptoms, socio-economic statuses, and environmental exposures is an ongoing struggle for researchers, administrators, clinicians, public health experts, and patients who struggle to use data to understand mental health. Information visualization techniques combining rich displays of data with highly responsive user interactions allow for dynamic exploration and interpretation of data to gain otherwise unavailable insights into these challenging datasets. To encourage broader adoption of visualization techniques in mental health, we draw upon research conducted over the past thirty years to introduce the reader to the field of interactive visualizations. We introduce theoretical models underlying information visualization and key considerations in the design of visualizations, including understanding user needs, managing data, effectively displaying information, and selecting appropriate approaches for interacting with the data. We introduce various types of mental health data, including survey data, administrative data, environmental data, and mobile health data, with a focus on focus on data integration and the use of predictive models. We introduce currently available open-source and commercial tools for visualization. Finally, we discuss two outstanding challenges in the field: uncertainty visualization and evaluation of visualization.
BioGamal Privacy Preserving Public Auditing for Cloud Computing

The advent of internet technologies has created a wonder that results in several streams using computing technologies termed as cloud computing such as in factories, medical stream, manufacturing industries, fields of science and researches, schools, colleges, universities, public sector units, for the sake of depot of data. As per the requirement of information, the data can be easily obtained from such cloud depot. Also, the hindrances like insecurity of data like loss of data, unexpected issues are in front during storing data in cloud. As always, along with barriers, there are various ways to avoid or minimize the issues relating to such cloud computing. One of the well-known technologies that takes care of security of data getting stored in cloud is cryptography. Conventional method of securing data involved cryptography algorithms was not that much fruitful and suffice for the top-level preservation of data, processed information, in cloud. The invent of new approach of applying DNA succession results in the discovering latest preserving technology. Hybrid encryption or decryption methods are also prevailing technologies nowadays that enable security of processed information. AES algorithm is meant for preserving secrecy of processed information, whereas hybrid algorithm uses DNA cryptography. Fact, figures righteousness can be verified applying the approach of TPA along with SHA. Applying the concept of digital signature that enables user to validate the proposed document information involves usage of latest approaches EDSA. Advanced concept manages the trine involves integrity, authenticity and confidentiality. Modernized and advanced preservation technology with low time consumption in between the process of data file encryption and decryption become possible only due to proposed algorithm.