Reviewerly/exHarmony huggingface dataset:
Analysis of Six Different GP-Tree Neighborhood Structures

Genetic programming is an automated artificial intelligence tool that can find an efficient solution to a problem in a program form. This program can be represented by a synthetic tree. In this paper, we study the possible moves that can be applied to a tree (program) in order to exploit its neighborhoods. We succeed to develop six different neighborhood structures. These structures are implemented and included in a recently proposed automatic programming metaheuristic called variable neighborhood programming algorithm. To analyze the behavior of the proposed neighborhood structure, we first devise them to six set. Then, we test them on benchmark functions drawn from the symbolic regression problem.
Integrating in-hand physical objects in mixed reality interactions

With the launch of commercial mixed reality headsets, it has become increasingly important to find new interaction modalities making use of their capabilities. Indeed, endowed with spatial understanding, those devices offer the possibility to interact with the physical environments around the users and move beyond the traditional WIMP(Windows, Icons, Menus, Pointer) paradigm. In this context, this research project aims at designing and implementing interactions that are seamless for users who are using physical tools during assembling tasks while wearing a mixed reality headset that provides them instructions. The goals of this project are to provide natural interactions despite having tools in hand: 1) by considering the tools in the hands while recognizing gestures or by using them to directly interact with the simulated virtual physics, 2) by associating different computational features such as mapped functionalities or allowed interactions depending on the tool being used or its shape. To meet those goals, we are iterating over the implementation of prototypes involving object and gesture recognitions and will evaluate the designed interactions in an assembly scenario.
Deep Learning for Medical Informatics and Public Health

The technology & healthcare intersect to constitute medical informatics (MI). MI enhances the outcomes of patients & healthcare through the skills of medical & computer sciences. The fusion of both these disciplines enables the related personnel to improve the patient care along with the research & clinical settings. Public health (PH) represents the science of safeguarding & improving community health. This outcome is attained by encouraging lifestyles that are healthy in nature, investigating new illnesses, preventing injuries and contagious illnesses. In general, PH focuses on safeguarding the health of entire communities. These communities can be small (local communities) or large (whole country/a global region). In this regard, Deep learning (DL) represents an interesting area of research. DL application has been observed in several domains due to the fast growth of both data & computational power. In recent years, the application of DL in the domain of MI has increased due to the probable advantages of DL applications in healthcare. DL can aid medical experts in diagnosing several illnesses, detecting sites of cancer, determining the impacts of medicines on each patient, comprehending the association among phenotypes & genotypes, discover novel phenotypes, & forecasting the outbreaks of contagious illnesses with higher precision. When compared to classical techniques, DL does not need data which is specific to a domain. DL is likely to transform the lives of humans. Regardless of these benefits, DL faces certain drawbacks related to data (higher number of features, dissimilar data, reliance on time, unsupervised data, etc.) & model (dependability, understandable, likelihood, scalable, safety) for real world applications. This chapter emphasizes on DL techniques applied in MI & PH, recent case studies related to the application of DL in MI & PH, and certain critical research questions.
Stylistic Change in Early Modern Spanish Poetry Through Network Analysis (with an Especial Focus on Fernando de Herrera’s Role)

Abstract Early Modern Spanish literature, also known as Golden Age Spanish literature, is a well-established period in the History of Spanish literary tradition, covering from the beginning of the sixteenth century to the end of the seventeenth century, from Renaissance to Baroque works. In the case of poetry, the stylistic change from one aesthetic to the other has been examined from a historical point of view, with some scholars considering a transitional group of poets between both stylistic movements, and many pointing to the writer Fernando de Herrera (1534–1597) as a bridge between the Renaissance style of Garcilaso de la Vega (1501–1536) and the Baroque of Luis de Góngora (1561–1627). This paper examines the stylistic change from Renaissance to Baroque in Spanish poetry and Herrera’s place on it from a quantitative, computational point of view, applying a methodology which combines Stylometry and Network Analysis to a big corpus of poems and authors writing in this period. The resulting stylometric network for Renaissance and Baroque poetry proves there is a computationally measurable chronological evolution in the texts of this period, as well as a change of style from Renaissance to Baroque, and supports the existence of a transitional group of poets between these two literary movements, one of which is Fernando de Herrera.
Helping or Hindering? How Clients’ Experiences Relate to Their Robo Advisory Use

Firms are increasingly introducing new business models based on digital technologies in knowledgeintensive, risky contexts that were long immune to them.In the financial industry, banks are opening themselves to robo advisory, an algorithm-based service supporting private clients' investment decisions.Based on our access to a recent data set of 11,302 clients from a leading German robo advisory provider, we have the unique opportunity to analyze how clients react to algorithm-based services in contexts with high uncertainty and risk.Guided by theorical foundations of knowledge and routines, we find clients' personal and business experiences with the focal bank to both, help and hinder their robo advisory use.
An Image-Based Information Hiding Approach with Chaotic Systems and Optimized Security

Background: Recently, information hiding has become an important approach to securely storing and transmitting important information in digital media. Objectives: This paper proposes a new approach to hide information in a color image with optimized security. Methods: The approach performs the hiding in two phases. Firstly, the information that needs to be hidden in an image is represented by a sequence of binary bits, and the sequence is shuffled and encrypted with a set of three-dimensional chaotic systems. Secondly, the binary bits in the shuffled and encrypted sequence are hidden into the R, G, and B components of the pixels in the given color image with a dynamic programming approach. Results: Experimental results show that the proposed approach can securely hide information in a color image without generating significant changes in the visual contents of a color image. The analysis also shows that the proposed approach is robust against potential attacks. Conclusion: The proposed approach is thus practically useful for applications aimed at image-based information hiding.
Predictor corrector algorithms considering multiple constraints for entry vehicles

Abstract This paper proposes an online entry trajectory planning based on predictor corrector algorithm that satisfies terminal constraints, path constraints and geographic constraints for lifting-body entry vehicles. The vehicle is considered as a 3DOF point mass. A piecewise linear bank profile in the altitude-versus-velocity (H-V) plane is used to project the longitudinal trajectory and a heading angle corridor-based bank angle reversal logic is designed to satisfy the geographic constraints simultaneously. The algorithm in longitudinal plane solves the problem of the mismatch between terminal range and height and the algorithm in lateral plane satisfies the no-fly zone constraint. Simulation results with the CAV-H model show that the proposed algorithm can generate entry trajectories satisfying multiple constraints and has certain robustness.
NOSE Algorithm for Offline English Speech Interaction Recognition System

In order to establish an online English speech interactive recognition system, this paper will focus on NOSE algorithm, mainly discuss the basic concepts of NOSE algorithm, and then carry out system design, and finally verify the system. Through the design of this paper, the English speech interaction recognition system based on NOSE algorithm is successfully implemented. The system has stable operation, high recognition accuracy, and can be put into practice.
SIREN! Detecting Burmese Hate Speech Comments on Social Media

Hate Speech on Social Media is definitely an evolving threat for every nation, especially for countries like Myanmar. Lack of media and digital literacy is playing a huge role in making people insult to each other or misallocating their stresses to others without physical encounter. Moreover, disingenuous politicians fuel online hate speech campaigns backstage of the elections by targeting different religions in the regard of heretics and using racialism. To emphasize this matter, we scraped over 16,000 social media comments from the most popular social media platform in Myanmar and performed hate-speech research using those samples. With the precise definition of a hate speech labelling guideline, annotation on the sample dataset was done systematically and efficiently. Experiments and evaluations were conducted using different linear and non-linear deep-learning classification models. Performances of the models are at the peak in Logistic Regression among linear models with 0.8974 AUC score and XLM-RoBERTa among deep learning models with 0.8958 AUC score on the test dataset. We observed that it is more advantageous to use linear models on our dataset since they achieved comparable results to the deep learning models and have much lower computational cost.
Robotic exploration algorithms in simulated environments with Python

Swarm Robotics is inspired by the biological swarms of social insects such as ants and bees, where individuals performing basic tasks give rise to complex behavior. It utilizes a team of cooperating robots to perform tasks more efficiently than possible by isolated robots. In this research, we study the exploration of unknown indoor areas using robots that coordinate with each other. In particular, we implement the Reverse Nearest Neighbor coordination algorithm with certain modifications to account for real-world constraints. The library developed as part of this work provides scripts to help with robotic tasks for exploration and robotic arm control that can be used to set up simulation tools like VREP, without much prior experience thereby lowering the barrier for entry and making the robotics projects more accessible.
Analogous Particle Swarm Optimization for Choosing Optimal Selection of Virtual Machine in Cloud

Cloud computing is one of the chief elements in Medical Care Administrations. It tends to be utilized to recover data about patients, their records identified with diagnosing, therapy taken and other clinical data in less measure of time. In this field picking of an ideal VM is a difficult and more intriguing in medical care administrations. The way to deal with pick the best ideal VM helps in expanding the exhibition of medical care benefits consequently decreasing the running opportunity to execute the solicitation. This paper proposes a novel and upgraded rendition of PSO called Similar to Molecule Multitude Enhancement (APSO). The presentation is contrasted and Hereditary Calculation and PSO. The assessment is finished by considering three boundaries like holding up time, use of computer chip and turnaround time. The outcomes unmistakably express that the APSO approach performs well when contrasted with different models. The general execution time for every molecule is registered as 1s and effectiveness apparently is improved by 5.6&#x0025;.
Contigo: Monitoring People’s Activity App for Anomalies Detection

The pandemic situation produced by COVID-19 has made access to medical services and personal treatment difficult. For the older adults, this has been a major problem because they often need assistance to facilitate their daily lives. Different types of solutions have been put into place very quickly to alleviate this problem, such as telemedicine or telecare. However, most of these solutions require people to perform some actions manually. This paper presents a solution to monitor people’s activity in a way that is transparent to them. This solution is based on a mobile application that records their movements and estimates a certain amount of activity, and a web platform that allows authorized personnel to request this information by using different type of microservices. This application offers a simple and easy-to-access solution that allows the monitoring of the activity of the older adults to know their status and to be able to act accordingly in case of unusual situations.
The Impact of Performance Appraisal on Employees’ Job Satisfaction in INGOs in Yemen

This study aims to gain a better comprehension of how employees in INGOs in Yemen perceive performance appraisals and their role in job satisfaction through exploring the impact of performance appraisal and its five constructs namely: direction and objectives to staff, performance appraisal process, communication between managers and subordinates, transparency of performance appraisal outcomes, and fairness of performance appraisal process on employees' job satisfaction in INGOs in Yemen. A quantitative method was used, and relevant questionnaires were distributed to these employees. The findings of the study show that there is a positive significant relationship between job satisfaction and performance appraisal and its above-stated five constructs. They also reveal that performance appraisal and four of the aforementioned constructs have significant positive impacts on employees’ job satisfaction. It is also found that the fifth construct, transparency of performance appraisal outcomes, has no significant impact on job satisfaction due to the limited transparency practiced in Yemen compared to better practices of such transparency in other countries. Moreover, the high R2 of 82% is an indication of the high explanatory power of the test. The above findings are consistent with similar studies referred to hereunder. This study suggests that decision-makers in Yemen's INGOs should improve the clarity of directions and objectives, the transparency and fairness of performance appraisals, and ways of communication for employees to express their concerns. Such improvements are critical since they may result in increasing the employees’ job satisfaction and consequently lead to a better humanitarian service delivered by the respective INGOs through accomplishing more and better work in less time and with fewer resources. The proposed framework is developed based on previous literature on performance appraisal emphasizing that no previous study used all the above constructs together to measure their impacts on job satisfaction of employees working in INGOs in Yemen.
Dehazing and deblurring of underwater images with heavy-tailed priors

The common problems of underwater images include color cast, haze effect, and the motion blur effect caused by turbulence and camera shake. To address these problems, research on color cast and haze and blur effects is carried out in this paper. Because red light has significant attenuation underwater, which could cause color cast of images, this paper proposes a red channel compensation method to solve this problem. This approach adaptively compensates for the red channel according to the pixel value of the red channel, successfully preventing excessive compensation. To address the haze effect of underwater images, combined with the physical model of underwater images, a variational method is introduced in the paper. This method can not only recover clear underwater images, but also refine the transmission map at the same time. Furthermore, the blind deconvolution method is adopted to deblur underwater images. First, the blur kernel of an underwater image is estimated, and then a clear underwater image is recovered based on the obtained blur kernel. Finally, qualitative and quantitative comparisons of the underwater images recovered by different methods are also carried out. From the qualitative perspective, the images recovered by our method have higher image sharpness and more outstanding details. The quantitative comparison results show that the images recovered using our method have higher scores according to various criteria. Therefore, on the whole, our method presents great advantages in comparison with others.
LDAAD: An effective label de-noising approach for anomaly detection

Classification algorithms are widely applied to predict failures and detect anomalies in various application areas. It is common to assume that the data and labels are correct when training, but this is challenging to guarantee in the real world. If there are erroneous labels in the training data, a model can easily overfit to these, resulting in poor performance. How to handle label noise has been previously researched, however, few works focus on label noise in anomaly detection. In this work, we propose LDAAD, a novel algorithm framework for label de-noising for anomaly detection that combines unsupervised learning and semi-supervised learning methods. Specifically, we apply anomaly detection to partition the training data into low-risk and high-risk sets. We subsequently build upon ideas from cross-validation and train multiple classification models on segments of the low-risk data. The models are used both to relabel the samples in the high-risk set and to filter the low-risk samples. Finally, we merge the two sets to obtain a final sample set with more confident labels. We evaluate LDAAD on multiple real-world datasets and show that LDAAD achieves robust results that outperform the benchmark methods. Specifically, LDAAD achieves a 5% accuracy improvement over the second-best method for symmetric noise while having a minimal detrimental impact when no label noise is present.
ExtractMetOnto: A Strategic domain Ontology modeling approach in the field of Extractive Metallurgy

The goal of Ontologies is to share and reuse knowledge while also describing semantically equivalent things. Finding matching resources specific to this metallurgical subject from the massive data available on the internet is a bit difficult. The metallurgy domain needs Ontologies to better grasp the subject and aid in the faster discovery of helpful information via the Semantic Web. The main aim of this study is to advance Ontology in Extractive Metallurgy. The concepts in Extractive Metallurgy are gathered from various internet sources and given a semantic description. The Ontology is modeled using software, and it is retrieved using machine learning tools and algorithms. ExtractMetOnto relies on achieving a formal Ontology model in Extractive Metallurgy. It has been implemented with a reuse ratio of 8.33 and a zero-reference ratio, making it the first independent Ontology of its kind.
Similarity Relations

In this chapter, we discuss a special type of fuzzy relations, called similarity relations. They play an important role in interpreting fuzzy controllers and, more generally, can be used to characterize the inherent indistinguishability or vagueness of a fuzzy system. In this context, fuzzy sets are induced concepts based on a similarity relation. The membership degrees are interpreted as grades of similarity.
Hybrid parallelization of the black hole algorithm for systems on chip

Black Hole (BH) is a bioinspired metaheuristic algorithm based on the theory of relativity in which a sufficiently compact mass can deform the space-time to form a black hole, where no particles or electromagnetic radiation can escape from it. Thus, such an approach is based on the concept of a population of individuals (stars) representing solutions for a given computational problem to be optimized. In the literature, such an approach has been used to solve clustering problems, among others, since it is parameter-free and simple to implement. In this article, due to such characteristics, a hybrid solution, in software/hardware, of parallelization of the BH algorithm is proposed, aiming at accelerating its processing in hardware through a methodology that allows any user, even non-expert, implement hardware accelerators, for optimization problems, among others, through a high level tool. A System on Chip (SoC) platform was used for this implementation, containing a Zynq chip from Xilinx, which has two ARM cores and an FPGA. The BH Algorithm was implemented in software first and then in hardware for runtime comparison purposes to validate this approach. Also, in this paper, simpler and more popular optimization algorithms, such as Particle Swarm Optimization (PSO), Gravitational Search (GSA), and Big Bang – Big Crunch (BB-BC), along with simpler datasets, were used for comparison purposes, due to its ease of implementation and to keep a fairer comparison with BH as realized in other works in the literature. Therefore, the results obtained were satisfactory in terms of execution time and quality, with an average speedup of 25 times compared to the same implementation in software. In the future, it is intended to use this procedure to implement more recent clustering and optimization algorithms with larger datasets as well.
Emotion Detection with Deep Neural Network and Contrastive Learning

Researchers expended much effort identifying and classifying emotions in sentences, as they were critical in Natural Language Processing (NLP). Typically, existing algorithms approach this problem as a multi-label classification challenge, and some of them are sufficiently mature and perform reasonably well. However, upon investigation, it became clear that all of these methods had a common limitation: the model can hardly depict the relationship among sample data in the space. In this research, I propose to integrate the advantages of contrastive learning in data representation with these models in order to improve performance. The studies are divided into two stages: first, they transform the typical Cross Entropy loss function employed during the training stage to a contrastive loss function; and second, they employ a compound loss function to emphasize advantages and minimize bad impacts. The improved experiment results as compared to the previous methods demonstrate the practicality of my proposed improvement.
Multimedia streaming analytics

In today's complex OTT multimedia streaming ecosystem, the task of ensuring the best streaming experience to end-users requires extensive monitoring, and such monitoring information is relevant to various stakeholders including content providers, CDN providers, network operators, device vendors, developers, and researchers. Streaming analytics solutions address this need by aggregating performance information across streaming sessions, to be presented in ways that help improve the end-to-end delivery. In this paper, we provide an analysis of the state of the art in commercial streaming analytics solutions. We consider five products as representatives, and identify potential improvements with respect to terminology, QoE representation, standardization and interoperability, and collaboration with academia and the developer community.