Reviewerly/exHarmony huggingface dataset:
Low-cost clusters on big data - A systematic study

The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data such as volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze this massive volume of data. Moreover, advancements in the field of Big Data applications and data science poses additional challenges, where High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years.
Context-responsive attitudinal surveys about technology in language classrooms

Language learner attitudes about technology have traditionally been studied using survey data, but those surveys have primarily been based on disparate constructs and conceptualizations of learner attitudes. The lack of a common measure, or one that reflects other levels and contexts of learning, has limited the investigation about language learner attitudes about technology. One possible solution, suggested by mixed methods research methodologists, involves the inclusion of qualitative data in adapting extant surveys to new contexts. The purpose of this study is to illustrate this type of adaptation, and to highlight the challenges and insights that occur as a result. In this study, qualitative data were used to adapt extant instruments to the secondary world language instructional context in the United States. The initial survey was administered to high school students of Spanish in the United States (N = 268), eight students then participated in qualitative interviews, the survey was revised, and quantitative findings from the revised instrument (N = 1039) were analyzed for validity and reliability. This study shows that this method for adapting a survey to a new context is viable yet complex, offering researchers an important model of how to tailor surveys to new contexts while remaining in conversation with prior scholarship.
Swarm Intelligence for Deep Learning: Concepts, Challenges and Recent Trends

Machine learning and deep learning have undoubtedly contributed to tremendous achievements in Artificial Intelligence (AI) in recent years, and more are likely to follow. They have demonstrated extraordinary superiority in various real-world applications like computer vision, medical diagnostic systems, agriculture, robotics, and many more. It enables automating the computer-aided system and drastically reducing the human workload where correct prediction with accurate precision is needed. On the other side, as technology advances, a vast amount of data is generated, raising the problem complexity and computational challenges of real-world applications. Furthermore, machine learning, deep learning, and the majority of real-world applications have complex optimization problems within themselves that must be adequately addressed for better and more accurate analysis. Nonetheless, we believe that swarm intelligence-based approaches to deep learning have traditionally been understudied and may ultimately deliver similar advances in AI capabilities - either building on those provided by deep learning or offering whole new ones. Swarm intelligence approaches are frequently employed to solve a wide range of optimization issues. Nowadays, swarm intelligence-based methods are attracting a lot of attention from the research communities of different domains because previous research in complex optimization has shown that behavioral patterns and phenomena observed in nature have the ability to facilitate the foundation for many optimization algorithms and solve problems efficiently. Swarm intelligence, machine learning, and deep learning, on the other hand, each has its own set of advantages and disadvantages. Recently, research communities have discovered an interest in integrating these concepts in order to overcome the limitations of each domain and give rise to a new paradigm known as evolutionary machine learning or evolutionary deep learning. In the case of machine learning and deep learning, the “curse of dimensionality,” non-convex optimization, automatic parameter optimization, and optimal architecture are just a few of the issues that can be efficiently addressed with swarm intelligence, whereas in the case of swarm intelligence, slow convergence, local optima stagnation, and extensive computation cost can be addressed with the machine learning and deep learning community. Therefore, a robust and self-efficient model can be developed by integrating these concepts to solve the complex problem associated with real-world applications. This hybrid approach benefits the majority of research domains. Thus, this chapter will primarily present the ideas, challenges, and recent trends of an integrative approach of swarm intelligence with deep learning, which is currently in high demand for addressing industrial problems.
Graph Structure Optimization for Agent Control Problems Using ACO

Ant Colony Optimization (ACO) is one of the powerful swarm intelligence algorithms capable of solving various problems. In this research, ACO is used to optimize individuals with graph structure. This structure is exactly like the approach taken by genetic network programming (GNP) for individual representation for solving agent control problems. However, in some types of environments such as stochastic environments, calculated fitness of an individual is not the same in each evaluation. Therefore, to estimate the true fitness of an individual, several times of evaluation are needed leading to increased process time of the evolution. In this research, a method is proposed to avoid slowing down the progress speed of the algorithm using ACO. This method can be well adapted on graph structures and in each iteration, it enhances the fitness of individuals using a constructive mechanism. In constructive mechanism, an individual is produced according to the experience of previous generations. In this research, the experience is the achieved fitness and it is distributed on the corresponding paths in the graph structure. This new method was used to solve an agent control problem called Pursuit-Domain while the environment is deterministic or stochastic. The experimental results showed high capabilities of this algorithm in generation of efficient strategies for agents in an agent control problem.
A Brief Tutorial on Optimization Problems, Optimization Algorithms, Meta-Heuristics, and Swarm Intelligence

This chapter provides preliminaries and essential definitions in optimization, meta-heuristics, and swarm intelligence. It starts with different components of optimization problems, formulations, and categories. Conventional and recent optimization algorithms to optimize such problems are then discussed. The chapter is finished by focusing on meta-heuristics and swarm intelligence algorithms as the emerging and most widely used optimization algorithms lately in both science and industry.
A Novel Codebook Generation by Lévy Flight Based Firefly Algorithm

Nature inspired metaheuristic algorithms are become more powerful and useful in image processing algorithms especially by the developments in microprocessor technology. In the last several decades the Linde-Buzo-Gray algorithm is a powerful technique for local optimum codebook generation in image compression. Fuzzy C-Means and C-Means are the alternative ones for the same process. On the other hand nature-inspired metaheuristic algorithms have also become other alternate technics for designing the optimum codebook. In this paper the Firefly technique is enhanced by the Lévy flight function to achieve the global optimum codebook. The Firefly technique contains two sub search mechanisms to reach the global minimum solution. The first one is the attraction of any firefly by a brighter one. This process strongly guides to the firefly on the global minimum way especially if it is attracted by the brightest one. The second one is the random search of a firefly in a circle with a radius of $$\alpha $$ . On the other hand if $$\alpha $$ is determined so big, the firefly may lose its way and come to a location that is much far away from the firefly group and possibly there is no brighter firefly to be followed. On the contrary, if $$\alpha $$ is determined so small, this time the fireflies fall into a local minimum and can not escape. Therefore we need to have such an $$\alpha $$ that in most of the iterations its value changes in a small random value interval, but in rare iterations its value must be relatively big in order to escape local minimums. Therefore if a firefly is captured by a local minimum point by accidentally, Lévy Flight step provides an opportunity to escape from it easily. Numerical results suggest that the new introduced Lévy Flight based Firefly Algorithm is better than the classical techniques and provides the global optimum codebook for image compression.
Professional personas and capabilities of the future people practitioner: A thematic review

Orientation: The transformation of the technology-enabled workplace has a significant impact on the future professional roles, services and capabilities of both the human resource professional (HRP) and industrial/organisational psychologist (IOP).Research purpose: The present study aimed to identify the intersecting professional personas and capabilities that speak to both the HRP and IOP as people practitioners of the digital era.Motivation for the study: An integrated 2020s capabilities framework outlining IOP and HRP services-linked personas and capabilities in their conjoint roles as people practitioners seems to be lacking.Research approach/design and method: The study utilised a configurative thematic review analysis methodology to identify convergences among contemporary scholarly and empirical views on the professional personas and capabilities of the 2020s multidisciplinary people practitioner.Main findings: The thematic analysis revealed professional development in terms of four digitally-dexterous capabilities proficiency domains and six professional personas relevant to the technology-enabled working environment.Practical/managerial implications: The proposed integrative conceptual personas and capabilities model may inform the context-relevant professional development of the digital-era people practitioner.Contributions/value-add: The study adds to the research literature on the shifting roles and capabilities of IOPs and HRP as future people practitioners in technology-enabled hybrid and virtualised work environments.
Occupational Cynicism According to the Opinions of Teachers

Cynicism, which is defined as the critical and insulting attitudes towards life, people and institutions depending on the negative experiences and feelings of the people, can be exhibited by the employees in the organizations.Cynicism consists of individual and organizational dimensions.Occupational cynicism is a type of organizational cynicism and can be expressed as emotional fatigue, disregard for the profession, thinking that the profession is overwhelming, not rewarding, not worth for the effort, having a negative perspective, attitude and feelings towards one's profession.It is the reflection of these attitudes and feelings critically.Emotions, attitudes and behaviors of teachers towards their profession can positively or negatively affect the climate of the educational organizations they are in, and therefore the educational outcomes.In the literature, there are studies on organizational cynicism for teachers.However, studies examining teachers' perceptions of occupational cynicism seem to be limited.Therefore, it can be said that examining teachers' opinions towards occupational cynicism is important.For this reason, the purpose of this study is to examine teachers' opinions of cynicism towards their profession.The study was designed with phenomenology, one of the qualitative research methods, and maximum variation sampling method was used to determine the study group.The study group consists of teachers working in public schools.The data were obtained by collecting with a semi-structured interview form.Descriptive analysis of the obtained data was made.The study was evaluated around four main themes.These are teachers' perceptions of occupational cynicism, causes of occupational cynicism, results of occupational cynicism and precautions to reduce occupational cynicism.According to the results of the study, the attitudes and behaviors towards occupational cynicism are not worth the effort, the thought that the teaching profession is worthless, the idea that professional rights are limited, and negative attitudes and behaviors such as reluctance.The labor which is not valued enough, unnecessary workload other than education and training, financial and family problems, lack of sufficient legal basis to protect the rights of teachers, problems with students and parents, injustice and management styles within the institution are some of the main causes for occupational cynicism according to the participants.Decline the quality of the education-teaching process, internal conflicts, decrease in school commitment, psychosomatic problems, communication problems, decreased professional commitment and resignation are some of the institutional and individual consequences of occupational cynicism for participants.According to the participants, establishing justice-based education policies, encouraging professional development, strengthening school culture, participating in personal and professional development activities, changing institutions, staying away from the work environment for a short time are some of the precautions listed to reduce occupational cynicism.
THE EXTRA-LEGAL PROPERTY RIGHTS DESIGN OF BITCOIN AND ITS PHILOSOPHICAL ISSUES

This article provides a philosophical examination of the Bitcoin concept of property rights protection. To that end, several fundamental questions must be addressed on the subject, including what money is, what purpose it seeks to serve, and how the system that supports it is related to the concept of property rights. Finally, it is important to identity what, if anything, Bitcoin has to offer in these matters. This article concludes that the primary function of money as a social institution is to store one’s labour as part of one’s property right. In comparison to fiat currency, Bitcoin is the superior medium of exchange. However, the ideological foundation of Bitcoin has philosophical issues: it is based on the false premises of absolute individual property rights derived from the concept of natural rights, which is incompatible with Indonesia’s economic commitment and goals of establishing a welfare state, as reflected in the constitution.
Orthogonal Vector-Decomposed Disentanglement Network of Interactive Image Retrieval for Fashion Outfit Recommendation

Interactive image retrieval for fashion outfit recommendation is a challenging task, which aims to search for the target desired image according to a multi-modal query (a reference image and a modification text). Previous studies focus on exploring effective feature composing methods to achieve similarity matching between different modalities. However, the existence of feature redundancy and the semantic inconsistency between modalities introduces many task-irrelevant information. It is intractable to correctly identify the particular information to be modified and will inevitably introduce noise disturbances which lead to suboptimal performance. To this end, we present a novel Orthogonal Vector-Decomposed Disentanglement Network (OVDDN) for image retrieval. It proposes to leverage the disentangled parts to learn a controllable denoising embedding space. First, we design an orthogonal disentanglement module. It is applied to both image and text features to decouple them into two independent components (invariant and specific) through orthogonal constraints. A similarity metric loss ensures semantic consistency of paired images. Then, an attention network generates composition of the reference image invariant part and text task-related part to match the target one. Finally, a differential feature alignment module maintain the cross-modal semantic consistency. Extensive experiments conducted on three benchmark datasets denote the OVDDN achieving the consistently superior performance. Ablation analyses further verify the effectiveness of our proposed model.
On Leveraging the Metapath and Entity Aware Subgraphs for Recommendation

In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbours while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture critical insights for downstream tasks. Concretely, in recommender systems (RSs), disregarding a larger neighbourhood and focusing only on the immediate neighbours leads to inadequate distillation of the collaborative signals. In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures. We propose metaPath and Entity-Aware Graph Neural Network (PEAGNN), which trains GNN to perform metapath-aware information aggregation on such subgraphs. The information from different metapaths is then fused using attention mechanism. To leverage the local structure of CSGs, we introduce entity-awareness that acts as a contrastive regularizer on node embedding. Moreover, PEAGNN can be combined with prominent layers such as GAT, GCN and GraphSage. Our empirical evaluation shows that our proposed approach outperforms competitive baselines on three public datasets. Further analysis demonstrates that PEAGNN also learns meaningful metapath combinations from a given set of metapaths.
Human motion prediction based on bidirectional feature sequence learning

Dynamic analysis of human behavior is an important research branch of computer vision and pattern recognition field in recent years. In order to enable the computer to predict the intention of future action posture in advance by monitoring human body's early behavior, a behavior prediction model Bi-Seq2Seq is proposed in this paper, which is based on the integration of Bi-GRU unit and Seq2Seq model. Bidirectional Gated Recurrent Unit (GRU)is introduced into the encoder of traditional Seq2Seq structure to integrate forward and reverse features. It breaks away from the limitation of temporal order and effectively combines context information. The decoder part still adopts one-way GRU unit. The experimental results show that the human behavior prediction model based on bidirectional feature sequence reduces the prediction error of Smoking, Posing, Greeting and Sitting movements, with the maximum reduction up to 0.52. The short-term prediction accuracy of attitude prediction algorithm within 400ms is improved.
Ensembles of fractal descriptors with multiple deep learned features for classification of histological images

In this paper, we propose an approach to study the ensemble of handcrafted and deep learned features, as well as possible templates for associating them for the classification of histological images. The handcrafted features were calculated with fractal techniques and the deep learned features were extracted from multiple convolutional neural network architectures. The most relevant features from each ensemble, selected with a ranking algorithm, were analyzed by a heterogeneous ensemble of classifiers to avoid overfitting scenarios. The proposed method was applied in the context of histological images of breast cancer, colorectal cancer and liver tissue. The highest accuracies were values from 93.10% to 99.25%. These results allowed defining some standard templates for techniques on different kinds of histological images, for instance, the fractal descriptors when ensembled with deep features via transfer learning can provide the best results. The insights presented here are a relevant contribution to specialists interested in the field of histological images and developing techniques to support the detection and diagnostics of scientifically relevant diseases.
Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes

Some environments remain largely unexplored, like unvisited planetary surfaces or regions of the ocean. Many of the destinations we are interested in have never been explored and thus, there is no a priori knowledge to leverage. Instead, a vehicle must sample upon arrival, process this data, and either send this information back to a teleoperator or autonomously decide where to go next. Teleoperation is suboptimal in that human intuition can be imprecise and cannot be mathematically guaranteed to yield an optimal result. Given a surface environment, a mobile agent will map the distribution of a scalar variable without any prior information, with a degree of confidence of model convergence, and while minimizing distance traveled. Science-blind approaches to covering a surface area include a predefined path with waypoints for a vehicle to locomote in an &#x201C;open-loop&#x201D; policy, like the Boustrephedon or Spiral patterns. Information theoretic approaches iteratively gather information to feed back into a model or policy, which then updates with a waypoint to visit next. Gaussian processes have been popularly incorporated into active learning exploration strategies due to their ability to incorporate information into a parameter-free model and quantify a confidence metric with every model prediction. We evaluate the performance of this informative path planning algorithm in mapping an environment on three different surfaces: parabola, Townsend, and hydration value across a lunar crater from LAMP data. The Gaussian process model was tasked to learn these relationships across these specific surfaces to investigate the efficacy of the learner and policy on a noiseless vs noisy surface, a convex vs a nonconvex surface, and a smooth well-behaved function vs an unknown real function. We quantify model variance, model root-mean-squared error, distance, and surface global minimum identification of the various methods in exploring a range of surfaces with no a priori knowledge. The results show that the information-driven methods significantly outperform naive exploration methods in minimizing model error and distance with potential of convergence.
Mobile Eye Tracking Research in Inclusive Classrooms: Children’s Experiences

The increasing pervasiveness of inclusive educational environments poses an urgent need to implement research methodologies and practices that could shed light on how children's social interactions unfold in such contexts. Our work explores the use of mobile eye tracking technology in naturalistic, inclusive K12 education settings towards a richer understanding of the children’s interactive behaviours. This paper presents the children’s responses to, experiences with and impressions about the naturalness of using mobile eye tracking glasses during a collaborative group task. Results highlight the importance of understanding the children’s experiences to foster naturalistic research environments that closely reflect real-life complexity. Our work contributes towards the deployment of research designs in naturalistic contexts, providing important clues towards the collection of ecologically valid real-world data.