Reviewerly/exHarmony huggingface dataset:
Optimizing Aggregation Frequency for Hierarchical Model Training in Heterogeneous Edge Computing

Federated Learning (FL) has been widely used for distributed machine learning in edge computing. In FL, the model parameters are iteratively aggregated from the clients to a central server, which is inclined to be the communication bottleneck and single point of failure. To solve these drawbacks, hierarchical model training frameworks like Hierarchical Federated Learning (HFL) and E-Tree learning have been proposed. One of the most challenging problems in the hierarchical model training framework is optimizing the aggregation frequencies of the edge devices at various levels. Because, in an edge computing environment, heterogeneity in the resource can introduce synchronization delays caused by waiting for slow workers and significantly impact the training performance. This paper tackles the problem with weak synchronization where edge devices on the same level have different frequencies on local updates and/or model aggregations. Existing works based on weak synchronization lack solutions to quantitatively determine the aggregation frequencies of each edge device. Thus, we propose a resource-based aggregation frequency controlling method, termed RAF, which determines the optimal aggregation frequencies of edge devices to minimize the loss function according to heterogeneous resources. Our proposed method can alleviate the waiting time and fully utilize the resources of the edge devices. Besides, RAF dynamically adjusts the aggregation frequencies at different phases during the model training to achieve fast convergence speed and high accuracy. We evaluated the performance of RAF via extensive experiments with real datasets on our self-developed edge computing testbed. Evaluation results demonstrate that RAF outperforms the benchmark approaches in terms of learning accuracy and convergence speed.
Virtual Environments Web 2.0 as a Tool for the Development of the Reading Comprehension in the Basic Education Area

This research was carried out in order to analyze the use of virtual environments in the development of comprehensive reading in students of the basic education career. It occurs because there is a limited information on the impact of these tools in different areas of the educational process. For which an experimental methodology with a mixed approach was used. The investigation was carried out in primary sources to know the level of reading comprehension in the study population. In order to collect information, a reading comprehension test was applied with 5 guiding questions. Also to a general survey of 14 questions on a Likert scale. It was validated by Cronbach’s Alpha statistic to highlight the reliability of the questions used in the research. Finally, a guide was developed for the development of author activities on the Educaplay platform. These activities were developing through the ADDIE methodology, which focuses on the correct use of technology focused on education. The main results were that students had a “poor” reading comprehension level, at the same time they did not have virtual environments that focus on comprehensive reading. This is because teachers only use known tools for the development of theoretical material, but not with interactive activities for the development of reading skill. In addition, the hypothesis was tested, which concludes that virtual environments contribute to the development of comprehensive reading in students.
Researching for GIS annotation

Using maps has become a daily activity for virtually everyone. However, in terms of interactive maps are limited. Rich interaction, such as issuing a complex query: "Which route has more greenery for my evening run?" is currently impossible to process for most Geographic Information Systems (GIS). To enable such rich interaction, data in GIS needs to be extended with high-level semantic annotations. Such annotations, due to their intrinsic semantic nature, can currently only be supplied by humans, who might moreover also need to have some expert knowledge about GIS in order to produce useful annotations. Given the cognitive resources required, the number of annotations to be made, and the tediousness of the task, there is a need to design systems that can support experts in completing such annotation tasks. We report on a study comparing two alternative systems for performing GIS annotations, a Conversational User Interface (CUI), designed for the purpose of this study, and a Web interface with NN GIS, which reflects the current state-of-the-art used by experts. We find that CUIs can indeed help to improve the accuracy and efficiency of the annotations. We conclude the paper with some tentative design principles for annotation systems coupled to complex user interfaces.
Lightweight Blockchain Secured Framework for Smart Precise Farming System

Smart farming is a technology framework that links the many components of agriculture to generate new possibilities. This link is made possible by the Internet of Things (IoT), which gives physical items a digital identity. Some research have suggested combining Blockchain technology with Internet of Thing in various case studies such as orchestration, access, and replicated storage layer. Owing to its redundancies and conventional consensus that requires a lot of processing. Blockchain is insufficient for the majority of linked items due to capacity limitations. This article tackles these issues by providing a new lightweight Blockchain architecture with a lightweight consensus and structure. According to their processing and storage capabilities, the proposed architecture of framework displays a linked object role hierarchy. We presented a security framework that blends the blockchain technology with IoT devices to provide a secure communication platform in Smart Precise Farming System and to take use of the capabilities of high-capacity objects like Edge and Fog computing nodes.
Corporate Social Responsibility (CSR) and Development in Africa

The literature on the relationship between Corporate Social Responsibility (CSR) and sustainable development in Africa is only just emerging, and thus characterized by a wide range of diverse perspectives. While the analyses of the CSR-sustainable development nexus in Africa have been particularly insightful, it often lacks sufficient grounding in systematically accumulated empirical evidence. However, central to the CSR-sustainable development nexus debate in Africa is the disagreement over the reimagining of the role of business from being the cause to becoming a part of the solution to the problem of underdevelopment in the region. This chapter critically examines the CSR-sustainable development nexus literature in Africa and lays bare the controversies that have so far emerged. The chapter engages with the drivers of CSR, its dynamics and nature within Africa. It then examines the debate of whether or not contextual factors matters for CSR and its relationship with development. Crucially, it identifies differences between proponents of CSR is good for development and those that share opposing views at the conceptual, practical, and discourse levels. The chapter concludes by considering the emerging issues and its implications for future research agenda.
Cat-Inspired Deep Convolutional Neural Network for Bone Marrow Cancer Cells Detection

Bone marrow cancer is considered to be the most complex and dangerous disease which results due to an uncontrolled growth of white blood cells called leukocytes.Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) are considered to be important categories of bone cancers, which induces a larger number of cancer cells in the bone marrow, results in preventing the production of healthy blood cells.The advent of Artificial Intelligence, especially machine and deep learning, has expanded humanity's capacity to analyze and detect these increasingly complex diseases.But, accurate detection of cancer cells and reducing the probability of false alarm rates remain on the darker side of the research.This paper proposes novel deep convolutional neural networks in which the hyper parameters are optimized by adaptive Multi-objective CAT algorithms.The proposed model is trained on the preprocessed cell images followed by training with Optimized Convolutional Neural Network (OCNN) and finally detecting the category of cancer cells present in the bone marrow.The extensive experimentations have been carried out using SN-AM datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are calculated and analyzed.The overall accuracy was found to be 99.45% in predicting different categories of cancer and it outperforms the pre-trained deep learning models such as Alexnets, VGG-16 nets, and U-nets.To establish the superiority of the proposed model, we have compared the performance of the proposed model with other machine learning models such as Support Vector Machines, Random Forest, Naïve Bayes, and Artificial Neural Networks.From the above intensive studies, it is clear that the proposed model was able to produce brighter performances than the other learning models in detecting the category of cancer cells.
Page, Scan, Send, Reshelve: A Mobile Scanning Station (Cart) for Interlibrary Loan Electronic Document Delivery Service

The Access and Resource Sharing Department (ARS) project team, in a library at a large research–intensive university, was awarded a libraries’ strategic opportunities grant in August 2019 to create and test the efficiency of a mobile scanning cart, and expedite a new scanning workflow and procedure for electronic document delivery and lending. The mobile station includes an overhead portable book scanner, a height adjustable cart, a rechargeable battery pack, a laptop computer and other accessories. This scan-beside-the-stacks initiative eliminates duplicate workflow and procedures, reduces human error of re-shelving, improves operational efficiency, improves electronic document delivery and electronic lending turnaround time, and saves staff time and library money. This initiative minimizes the duration that print collections are taken off the shelf, thus assuring patron access to library collections. This mobile station may be the only such scan-beside-the-stacks methodology in use in U.S. academic libraries. This technology also allows a single employee to handle all items to complete the digital conversion of print materials and reduces the risk of coronavirus transfer between library staff members during the COVID-19 pandemic.Supplemental data for this article is available online at https://doi.org/10.1080/1072303X.2022.2030842 .
DEVELOPMENT OF CONCEPTUAL BASES OF THE EMPLOYEE LIFE CYCLE WITHIN AN ORGANIZATION

The purpose of the study is to review and integrate various definitions of the employee life cycle (ELC) and to develop new conceptual bases of ELC by applying logical analysis and systemic approach. In this article, we suggest the employee life cycle has a “client-based” approach. Therefore the development of the ELC is similar to the development of the client life cycle and each stage of this process requires adoption and use of external and internal personnel marketing tools. At the same time, it implies simultaneous various activities of employees and employers alike. A customer-centric process on the labor markets (external and internal) is initiated by the personnel marketing product. It is defined by three levels aiming to attract and retain employees. The model of the ELC proposed hereafter shows the bilateral mechanism of the employee-employer interaction. Through the activities of external (employee attraction) or internal (employee retention) personnel marketing the organization can build its “perceived” or “received” Employer Value Proposition (EVP). The refined definition of the ELC is based on the theoretical foundations of personnel marketing and takes into account the employee and the employer’s perspectives; our definition identifies the ELC time frame and defines indicators of the ELC measurement for further empirical studies. Suggested ELC stages were developed in a way to reflect characteristics and actions for the employer and the employee simultaneously. To maintain or renew the employee engagement level a set of measures were suggested for implementation at each stage of the ELC.
A Machine Learning Approach to Modulation Detection in Wireless Communications

Autonomous Modulation Detection (AMD) in the presence of unknown channel conditions is critical in spectrum sensing and channel estimation. Existing works achieve AMD by processing raw spectrum measurement data. However, such approaches are computationally complex and time-consuming in practice. We propose a novel AMD as a conglomeration of symbol level extraction and a simplified machine learning classifier. The salient feature of our work is the development of a symbol level extraction scheme that processes physical layer spectrum measurements. The extracted features further simplify the machine learning model design that not only reduces computational complexity but also increases classification performance. The proposed scheme was evaluated on both software as well as hardware testing platforms. The evaluation results were discussed using different metrics and a comprehensive study on the performance of this scheme is also included in this work.
Zero-shot approach for news and scholarly article classification

With the enormous growth of data and documents in raw and unstructured formats, categorizing data is the need of the hour. Understanding text is an essential step in classifying it. Natural Language Processing (NLP) aids computers in comprehending and interpreting natural languages such as English, allowing them to evaluate and deduce their meaning. Keeping in mind large, diverse data at ever-increasing rates, this paper compares Zero-shot learning, a relatively new technique for classifying text, to multinomial Naive Bayes, which is a traditional classification method. The experiment analysis has been carried out on the BBC News dataset and the ArXiv dataset, which contains metadata of scholarly articles. The Zero-shot approach gives promising results with top-2 accuracy of about 93% and 87.6% on the two unseen datasets and can hence be used to effectively classify unlabelled data without specific training.
Combining Classical and Non-classical Features to Improve Readability Measures for Arabic First Language Texts

Reading is invaluable support for cultural, social, technological and economic development. The wide availability of information has made it particularly easy to access a large number of texts, thus promoting contact between languages and exposing the public to linguistic diversity. Because of all these challenges, the community needs powerful tools to measure texts’ level of difficulty before presenting them to readers. Not surprisingly, therefore, language teaching and readability assessment is a particularly important concern for today’s researchers. Research in this area has emerged from the development of mathematical formulas to Machine Learning (ML) systems. The work on Arabic as a foreign language (L2) is somewhat broader than that of Arabic as a first language (L1). In this study, we introduce an approach to evaluate the readability of Arabic texts dedicated to L1 learners. This approach combines ML techniques and Natural Language Processing (NLP) predictors. It consists mainly of evaluating the impact of combining classical features with more sophisticated ones on the readability prediction. We also present the evaluation results as well as the future directions of this research. Our L1 best readability prediction accuracy is about 78%.
Construction of an Automatic Bengali Text Summarizer Using Machine Learning Approaches

In our study, we chose python as the programming platform for finding an Automatic Bengali Document Summarizer.English has sufficient tools to process and receive summarized records.However, there is no specifically applicable to Bengali since Bengali has a lot of ambiguity, it differs from English in terms of grammar.Afterward, this language holds an important place because this language is spoken by 26 core people all over the world.As a result, it has taken a new method to summarize Bengali documents.The proposed system has been designed by using the following stages: pre-processing the sample doc/input doc, word tagging, pronoun replacement, sentence ranking, as well as summary.Pronoun replacement has been used to reduce the incidence of swinging pronouns in the performance review.We ranked sentences based on sentence frequency, numerical figures, and pronoun replacement.Checking the similarity between two sentences in order to exclude one since it has less duplication.Hereby, we've taken 3000 data as input from newspaper and book documents and learned the words to be appropriate with syntax.In addition, to evaluate the performance of the designed summarizer, the design system looked at the different documents.According to the assessment method, the recall, precision, and F-score were 0.70, 0.82 and 0.74, respectively, representing 70%, 82% and 74% recall, precision, and F-score.It has been found that the proper pronoun replacement was 72%.
Geochemical characterisation of offshore New Zealand phosphorites, and mechanisms for their formation

Phosphate deposits can form in a range of environments, and despite similar P contents, their mineralogy, and hence major and trace element chemistry, can vary substantially. Continental margins with high surface-water productivity are recognised as a major environment for the formation of extensive phosphate deposits. The geochemical characterisation of continental-margin-derived phosphorites from Chatham Rise and south-eastern offshore New Zealand, as well as newly discovered seamount phosphorites from Bollons Seamount (first documented here), offer insights into the different mechanisms of phosphorite formation from these two different environments. Though less studied, seamounts are increasingly recognised for their phosphorite deposits as well as Fe-Mn crusts and Mn nodules. We propose the first model for the formation of nodules at Bollons Seamount, and an updated model for Chatham Rise nodules (and south-eastern offshore NZ phosphorites) is presented. Suboxic-to-anoxic enrichments of U and V are observed in Chatham Rise and south-eastern offshore phosphorites that contrast with oxic enrichments of Mn, Ce, Co, and Cr in Bollons Seamount phosphorites. Differences in uptake of Light Rare Earth Elements (LREE) vs. Heavy Rare Earth Elements (HREE) and variations in mineralogy (e.g., presence of glauconite vs. Fe-Mn crusts), allow phosphate deposits that formed in organic-rich, upwelling continental-margin environments to be distinguished from those that formed in oxic-suboxic organic-poor seamount environments.
Evaluating the Efficacy of Different Neural Network Deep Reinforcement Algorithms in Complex Search-and-Retrieve Virtual Simulations

In recent years, Deep Reinforcement Learning (DRL) has been extensively used to solve problems in various domains like traffic control, healthcare, and simulation-based training. Proximal Policy Optimization (PPO) and Soft-Actor Critic (SAC) methods are DRL’s latest state of art on-policy and off-policy algorithms. Though previous studies have shown that SAC generally performs better than PPO, hyperparameter tuning can significantly impact the performance of these algorithms. Also, a systematic evaluation of the efficacy of these algorithms after hyperparameter tuning in dynamic and complex environments is missing and much needed in literature. This research aims to evaluate the effect of the number of layers and nodes in SAC and PPO algorithms in a search-and-retrieve task developed in the Unity 3D game engine. In the task, a bot had to navigate through the physical mesh and collect ‘target’ objects while avoiding ‘distractor’ objects. We compared the SAC and PPO models on four different test conditions that differed in the ratios of targets and distractors. Results revealed that PPO performed better than SAC for all test conditions when the number of layers and units present in the architecture was the lowest. When the number of targets was more than the distractors (9:1), PPO outperformed SAC, especially when the number of units and layers were large. Furthermore, increasing the layers and units per layer was responsible for increasing PPO and SAC performance. Results also implied that similar hyperparameter settings might be used while comparing models developed using DRL algorithms. We discuss the implications of these results and explore the possible applications of using modern, state-of-the-art DRL algorithms to learn the semantics and idiosyncrasies associated with complex and dynamic environments.
Tracking Planar Objects by Segment Pixels

The methods of representing a planar object by four corner points have been widely used in planar object tracking. These previous approaches match tracking objects between video frames by handcraft features or by directly minimizing errors. However, these methods lack the ability to classify objects and backgrounds with high-level semantic information, resulting in a less robust performance in complex scenes such as motion blur. In this paper, we propose a robust approach to pixel-level tracking by learning semantic information about the tracking object. We solve the planar object tracking in terms of finding each pixel in the region where the planar object is located. Our proposed STMPOT network can memorize the object and background in-formation of each frame. The proposed STMPOT network learns to classify the pixels belonging to the object or background of the current frame. We transform the POT dataset into a POT-seg dataset to train our method. We also compare our method with other planar object trackers by the proposed evaluation metrics, and the experimental results show that our method achieves the best results in most scenes.
Using Universal Sentence Encoder for Semantic Search of Employee Data

This paper describes an application that performs a semantic search on an employee database. It helps Human Resources employees to target relevant people for their events and trainings. Syntactic or lexical searching involves keyword matching but does not match synonyms and other contextually related data. By using regular keyword search, a document either contains the given word or not, and there is no middle ground. Semantic Search allows the matching of data contextually linked with the search term. High dimensional vectors, also known as embeddings, are generated for a complete sentence and are then used for searching. Under the hood, Google&#x2019;s Universal Sentence Encoder. The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks to provide better performance of the model as compared to a custom trained Convolution Neural Network which also requires more training data.