Reviewerly/exHarmony huggingface dataset:
Scheduling Auction based Cooperation Game for shared resource scheduling and revenue distribution in a Cloud-based manufacturing environment

In a cloud-based manufacturing (CM) environment, participating companies share their manufacturing resources. This paper proposes a matching mechanism between resource sellers and buyers in a CM environment. The target model of this study consists of multiple sellers, buyers, and a coordinator. The coordinator uses the scheduling auction-based cooperation game (SACG) to determine an efficient matching between sellers and buyers and a fair revenue distribution among the sellers. SACG consists of two stages: scheduling auction and revenue distribution. In the first stage, each seller submits information about when it can provide the facilities. A buyer makes multiple bids for different combinations of the release date, due date, and required usage time. In the second stage, SACG evaluates the contribution of the companies providing their resources by the Shapley Value. Since the proposed method determines the buyers’ payments with the Vickrey-Clarke-Groves (VCG) mechanism, it satisfies strategy-proofness for the buyers, but not necessarily for the sellers. However, computational experiments reveals that if sufficient number of sellers and buyers participate in CM, sellers are not rewarded by deceitfully raising their reserve prices.
Smart Healthcare Digitalization during the COVID-19 pandemics

The COVID-19 pandemic is revolutionizing our lives, forcing us to change our habits, and reshaping our relationship with the healthcare system. Digitalization plays a crucial role, allowing for timely and aseptic connections that improve the efficiency of many healthcare processes, shortening the supply chain – especially the last mile, from the hospital to the patient – and boosting the value chain that coalesces around value co- creating stakeholders, starting from the traditionally neglected patients.The digital healthcare value chain starts from IoT and big data that are then stored in cloud, interpreted with artificial intelligence patterns, and validated with blockchains.This study analyzes in an interdisciplinary way some key aspects of healthcare innovations that – thanks to networking digitalization – can become “smart”, improving quality of care and long-term socio-economic sustainability.
A method for archaeological and dendrochronological concept annotation using domain knowledge in information extraction

Advances in Natural Language Processing allow the process of deriving information from large volumes of text to be automated. Attention is turned to one of the most important, but traditionally difficult to access resources in archaeology, commonly known as 'grey literature'. This paper presents the development of two separate Named-Entity Recognition (NER) pipelines aimed at the extraction of Archaeological and of Dendrochronological concepts in Dutch, respectively. The role of domain vocabulary is discussed for the development of a Knowledge Organisation System (KOS)-driven, Rule-Based method of NER which makes complementary use of ontology, thesauri and domain vocabulary for information extraction and attribute assignment of semantic annotations. The NER task is challenged by a series of domain and language-oriented aspects and evaluated against a human-annotated Gold Standard. The results suggest the suitability of Rule-based KOS driven approaches for attaining the low-hanging fruits of NER, using a combination of quality vocabulary and rules.
Selecting the Appropriate Persona Creation Method

In this chapter, we discuss the various methods of persona creation. Persona creation has traditionally been divided into Qualitative, Quantitative, and Mixed-Methods approaches. However, there is a lack of literature systematically contrasting the strengths and weaknesses of these three general approaches. Here, we review the literature to map the strengths and weaknesses of these approaches. We provide insights for better creation and use of data-driven personas from both researchers and practitioners, especially those who are new to personas, deploying personas in a new domain, or familiar with only one of the persona creation approaches. We end with some examples of the APG system that creates data-driven personas.
Awareness, Use and Satisfaction of E-resources among School Children: A Study of School Children of Bharatesh Central School, Halaga, Belagavi, Karnataka

The present study projects on the awareness, use and satisfaction of e-resources among School children of Bharatesh Central School, Halaga, Belagavi, and Karnataka State. This survey was undertaken consisting of 101 students of secondary grades, the study focuses on the awareness, use and satisfaction of e-resources. Through this survey we could analyze the awareness of the school children of the growing information technology and acquaintance with various types of e-resources, internet use, problems faced while searching and using the e-resources and their reasons.
Innovative Application &amp; Enrollment Processes for More Informed Students: Reflections on the Benefits of this Innovative Process through the Analytical Lens of Behavioural Science

This paper examines the practical experience and research background of one private training provider with over 10 years’ experience reskilling and upskilling mid-career workers with academic qualifications for employment in a growing technical and highly regulated pharmaceutical manufacturing industry. This paper offers insights into how to maximise the efficiency and effectiveness of the admissions, enrollment, induction and orientation process for adult learners looking to commence studies on university accredited continuous professional development (CPD) courses. This process is based on and analysed through the lens of Behavioural Science, while also building upon the work of Dirksen, Colvin Clark, Stolovitch, and Keeps. The data set has been gathered during 2020 and 2021, from 425 adult learner applicants, coming from a variety of educational and employment backgrounds, with 5 to 25 years of work experience.
THE USE OF INFORMATIONAL AND COMMUNICATIONAL TECHNOLOGIES IN THE PROCESS OF TEACHING CHILDREN WITH DISABILITIES

The article considers the use of information and communication technologies in the education of children with disabilities. Information and communication technologies help to make the learning process of a child with a disability more individualized, varied and effective. The lesson with the use of modern information technologies for children with disabilities contributes to the solution of one of the main tasks of correctional education – the development of the student's personality, his ability to navigate and adapt in modern society. It was found that the computer significantly facilitates the process of information exchange of children with the world around them, access to relevant information and presentation of the results of their own information activities in a common form, and expands real opportunities for participation in various spheres of sociocultural life, including education and professional activities. The modern content of education involves the active introduction of computer technology in educational institutions. New technologies will not replace teachers, textbooks or the classroom, they help to focus on student learning, create new opportunities for the development of the entire education system.
Methodology for CNN Implementation in FPGA-Based Embedded Systems

The increasing interest in convolutional neural networks (CNNs) is driving the study and design of different implementations for a variety of platforms, each intended to optimize performance, power consumption, or latency, according to the application’s needs. While GPUs have dominated the high-performance terrain, FPGAs have proved to be a promising alternative due to their relatively high performance and reduced power consumption and costs, compared with GPUs. The main concern regarding FPGA implementations lies in the effort needed to develop the systems and difficulties reusing or combining designs by different authors, due to the highly heterogeneous architectures used in each project. This work proposes a methodology and a high-level architecture designed for CNN implementations in FPGAs, which eases the development process, allows the reusability of designs, and helps to maximize performance, minimize latency, reduce resource utilization, and avoid possible bottlenecks, while allowing high design flexibility. This proposal is validated by implementing a set of blocks that are later used to build different CNNs.
Worker-Centric Model Allocation for Federated Learning in Mobile Edge Computing

Federated Learning (FL) is believed as a promising manner of distributed machine learning for 5G and future 6G networks in the context of mobile edge computing (MEC). From the worker’s viewpoint, a problem is that no incentive drives them to participate in FL. Thus, different from the conventional server-centric model allocation methods, we study the worker-centric strategy in this paper. Because communicating with the FL server and training an FL model locally are energy-hungry, a dilemma for each worker is how to make the tradeoff between participating in FL training and the volume restriction of its battery. To address this issue, we formulate a WorkerFirst problem. Its NP-hardness is also proved. Next, we devise a DDQN algorithm and a DQL algorithm to strive for near-optimal decisions for each worker. These two algorithms are proposed by leveraging the deep reinforcement learning framework, while considering the energy consumption, training timespan, and the communication overheads of workers, simultaneously. The benefit of adopting such a DRL-based decision-making framework is that workers can execute the algorithms locally and dynamically adapt to the varying MEC environment. The evaluation results show that the proposed DDQN and DQL algorithms can learn good policies without knowing any prior knowledge of network conditions.
S-Glint: Secure Federated Graph Learning With Traffic Throttling and Flow Scheduling

Federated learning has been proposed as a promising distributed machine learning paradigm with strong privacy protection on training data. In this paper, we study federated graph learning (FGL) under a cross-silo setting where several servers are connected by a wide-area network and cooperate in training a graph convolutional network (GCN) model. We find that communication becomes the main system bottleneck because of frequent information exchanges among federated severs and limited network bandwidth. To tackle this challenge, we design S-Glint, a secure federated graph learning system, containing a homomorphic encryption-based protocol with pre-aggregation and batching strategies. The network traffic throttling and flows scheduling are also proposed to accelerate the learning process. To evaluate the effectiveness of S-Glint, we conduct experiments using trace-driven simulations. The results show that S-Glint can significantly outperform existing federated learning solutions.
The comparative analysis on the text difficulty among English reading question types in CSAT

The present study examines the different linguistic characteristics and the text difficulty of the College Scholastic Ability Test (CSAT) among English reading question types. The study first focused on comparing the linguistic features of the different question types in the CSAT English reading test. Second the study examined whether the consistency of each linguistic features was maintained over a period of five years (2017-2021). CSAT reading passages conducted from 2017 to 2021 (for 2018-2022 college entrance) were collected. The types of each question were classified into four groups (identifying the main idea, identifying the details, identifying the context and discourse, indirect writing). Corpus analysis was conducted using Coh-Metrix 3.0. The indices were selected for descriptive analysis, lexical diversity, cohesion and connectives; and syntactic complexity. A one-way ANOVA and Kruskal-Wallis H test were conducted to analyze the Coh-Metrix results.&#x0D; The result showed the statistical differences in cohesion and coherence and lexical indices among the question types. In addition, the fluctuation of the coherence and cohesion indices was evident over five years (2017-2021). The implications and limitations of the study were discussed.
Cascaded MPN: Cascaded Moment Proposal Network for Video Corpus Moment Retrieval

Video corpus moment retrieval aims to localize temporal moments corresponding to textual query in a large video corpus.Previous moment retrieval systems are largely grouped into two categories:(1) anchor-based method which presets a set of video segment proposals (via sliding window) and predicts proposal that best matches with the query, and (2) anchor-free method which directly predicts frame-level start-end time of the moment related to the query (via regression).Both methods have their own inherent weaknesses: (1) anchor-based method is vulnerable to heuristic rules of generating video proposals, which causes restrictive moment prediction in variant length; and (2) anchor-free method, as is based on framelevel interplay, suffers from insufficient understanding of contextual semantics from long and sequential video.To overcome the aforementioned challenges, our proposed Cascaded Moment Proposal Network incorporates the following two main properties: (1) Hierarchical Semantic Reasoning which provides video understanding from anchor-free level to anchor-based level via building hierarchical video graph, and (2) Cascaded Moment Proposal Generation which precisely performs moment retrieval via devising cascaded multi-modal feature interaction among anchor-free and anchor-based video semantics.Extensive experiments show state-of-the-art performance on three moment retrieval benchmarks (TVR, ActivityNet, DiDeMo), while qualitative analysis shows improved interpretability.The code will be made publicly available.
Steps Forward to Quantum Learning Vector Quantization for Classification Learning on a Theoretical Quantum Computer

This paper introduces concepts for quantum computing based learning vector quantization and prototype learning models. These concepts relate to current quantum computing patterns and quantum hardware. For this purpose, we introduce a new computing pattern for prototype updates and possible measurement strategies in the quantum computing regime. Further, we consider numerical errors which are induced by the theoretical model and their impact on the learning process.
MAI-CBert: Multidirectional Attention Interaction Construction-Bert Sentiment Sentence Representation

Considering that when the traditional network structure is connected with the pre-trained language model for sentence embedding representation, it relies on simple word weights to generate sentence vectors, which easily ignores the global and contextual semantic details of sentences, reducing the accuracy of sentence representation. To address these issues, we develop a Multidirectional Attention Interaction Construction-Bert Sentence Representation Framework (MAI-CBert). First, the model uses deep attention to transform the input emotional sentences to reduce misunderstanding caused by the unbalanced distribution of word weights in the sentences; at the same time, it applies horizontal and vertical attention to emotional constructions, which are generated from two different directions. Construction vectors, aim to focus on words with salient features in sentences and establish close dependencies. Second, the dynamic interaction strategy is adopted to realize the interaction between attention in different directions, so that the information flow forms a complementary relationship, resulting in more effective sentence vectors. Notably, the loss function is refactored to improve the representation robustness of the model to avoid catastrophic forgetting. Experimental results on the SemEval-14 and ACL-14 baseline datasets demonstrate that the MAI-CBert sentence representation framework is robust and competitive.
On the computational power of the light: An optical method for the subgraph isomorphism problem

NP-complete and NP-hard problems, such as subgraph isomorphism, are well-known benchmarks to evaluate computation power of unconventional computing approaches. Optical computing, as one of the most important unconventional approaches, has determinant features such as massive parallelism that allows us to solve hard computational problems in an efficient time, while in the similar situations conventional computers require exponential time. In this paper, we solve subgraph isomorphism, as an NP-complete problem, using optical computing in a polynomial time after a polynomial preprocessing time. The proposed method has two main steps: first, possible solution space of the problem is generated using an optical manner and then all invalid solutions are eliminated from the space by several designed optical filters. The filters are made in the preprocessing phase in a polynomial time while the space of each filter is exponential. In this study, we have provided a solution to the YES/NO decision version of the problem.
Sparse Weighted K-Means for Groups of Mixed-Type Variables

Assessing the underlying structure of a dataset is often done by training a clustering procedure on the features describing the data. In practice, while the data may be described by a large number of features, only a minority of them may be actually informative with regard to the structure. Furthermore, redundant features may also bias the clustering, whether one speaks of redundancy in the informative or the uninformative features. The present contribution aims at illustrating two sparse clustering methods designed for mixed data (made of numerical and categorical features). The proposed methods summarise redundant features into groups, and select the most relevant groups of features only in the clustering procedure. The performances and the interpretability of the sparse methods are illustrated on a real-life data set.
Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory

In this paper we propose a modification of the Classification-by-Components (CbC) model for classification. Particularly, we integrate explicitly the Dempster-Shafer-theory, which in the original approach was mentioned to be implicitly realized but not explained deeply. Thus, we redefine the CbC keeping the main aspects of positive and negative reasoning about detected components/features and relate this to the Demspster-Shafer-theory of evidence.
SOM Visualization Framework in Python, Including SOMStreamVis, a Time Series Visualization

The unique power of the Self-Organizing Map (SOM) lies specifically in the wide range of visualizations available to support explorative data analysis. However, these are usually scattered across independent libraries, making their integration and comparative application in data exploration difficult. Additionally, few visualizations focus on the dynamics of data forming a time series, while the importance of understanding data evolution is gaining importance in many settings. In this paper, we thus present SOMStreamVis, an extension to a visualization specifically tuned to capture and represent time-series information on self-organizing maps. We furthermore provide an PySOMVis, an open-source software framework realized in Python that integrates SOMStreamVis with a wide range of additional visualizations of the SOM, making it a powerful data exploration tool.
Application of Kohonen Maps in Predicting and Characterizing VAT Fraud in a Sub-Saharan African Country

This paper describes the use Self-Organizing Maps (or Kohonen Networks) in helping to detect Value Added Tax (VAT) fraud. The sub-Saharan African Country presented as a case study has had its largest share in tax revenues since the introduction of VAT, nearly two decades ago, but still has a relatively modest tax efficiency when compared to European or worldwide standards. This trend can be reversed by strengthening the audit and inspection processes of its Revenue Authority (RA) with Data Mining, taking advantage of historical data stored by different information systems. A Case Study in the southern region of this country is presented, where historical data available from tax audits are compared with the VAT returns using Kohonen Maps. Comparing the experimental results with other anomaly detection algorithms, Kohonen maps prove to be of great value in predicting and characterizing VAT fraud in this case study.
Fast Parallel Search of Best Matching Units in Self-organizing Maps

Self-Organizing Maps (SOM) are unsupervised neural networks that map an underlying regular neighbourhood structure onto a codebook that is learned to perform vector quantization onto an input space. They are used in a wide range of applications, where an increase in the number of neurons in the SOM often leads to better results or new emerging properties. Therefore highly efficient algorithms for learning and evaluation are key to improve the performance of such models. The most time-greedy component of the SOM learning algorithm is the computation of the best matching unit (BMU) that is usually performed by means of a standard Winner Takes All algorithm. In a previous paper, we have proposed FastBMU, an algorithm to compute the BMU that scales better with a large number of neurons. Our algorithm has shown a significant improvement in computing time with a minimal degradation of performance in the context of a sequential implementation. In the perspective of hardware implementations, we propose here a parallel version of FastBMU, and we analyse its behavior and its performance. Based on the performed analysis, we finally derive principles of a parallel hardware structure that maximize resource utilization.
High Median Utility Itemset Mining for recovering streaming window transaction using novel Modified Heap‐based Optimization

Abstract High utility itemsets (HUIs) are items in the dynamically streaming transaction list that generate a high‐profit margin. Many of the real‐time applications depend on finding HUIs from the transaction list. However, HUI mining is time‐consuming and results in high complexity due to memory requirements, a large search space, and the cost of HUI estimation. To overcome these issues we have proposed a novel High Median Utility Itemset Mining (HMUIM) approach. This approach utilizes the HMUI‐Miner which effectively ignores the unnecessary itemsets, that is, items with less profit, and mines the HUIs from the database. Furthermore, it has the limitation of deleting the transaction list based on the sliding window size used while conducting a dynamic streaming dataset. To tackle this issue we proposed a novel Modified Heap‐based Optimizer (MHBO) algorithm which effectively copies the HUIs transaction list and preserves it for further process. The MHBO is the combination of Heap Based Optimizer and nine fuzzy rules. The fuzzy rules are used to analyze the priority of the transaction list and based on that it ignores the transaction list with low priorities. Experimental analysis is performed for the proposed method on the real‐time dataset and compared with HUI and HAUIM approaches. The proposed method reduces the execution time and memory usage based on the minimum threshold. Meanwhile, the proposed MHBO approach is compared with iMEFIM, REX, and FCHUIM state‐of‐art works based on the sliding window concept. The execution time to store the replicated copies of original data is less for our proposed method.