Reviewerly/exHarmony huggingface dataset:
AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning

Machine learning research has been able to solve problems in multiple domains. Machine learning represents an open area of research for solving optimisation problems. The optimisation problems can be solved using a metaheuristic algorithm, which can find a solution in a reasonable amount of time. However, the time required to find an appropriate metaheuristic algorithm, that would have the convenient configurations to solve a set of optimisation problems properly presents a problem. The proposal described in this article contemplates an approach that automatically creates metaheuristic algorithms given a set of optimisation problems. These metaheuristic algorithms are created by modifying their logical structure via the execution of an evolutionary process. This process employs an extension of the reinforcement learning approach that considers multi-agents in their environment, and a learning agent composed of an analysis process and a process of modification of the algorithms. The approach succeeded in creating a metaheuristic algorithm that managed to solve different continuous domain optimisation problems from the experiments performed. The implications of this work are immediate because they describe a basis for the generation of metaheuristic algorithms in an online-evolution.
Functional capabilities of higher education institutions’ repositories - members of the program “Priority-2030”

In order to study the composition and contentof repositories of scientific publications, to identify their functionality, 106 websites of domestic universities – members of the program«Priority-2030» have been monitored. The study has found that only 38% of the total quantity of institutions sites maintain publication repositories. At the same time, most of them include a small number of objects, or limited by the chronological framework.The search capabilities of repositories have been covered, as well as tools for managing search results. It has been determined that repositories mostly provide simple and advanced options for information retrieval, with the ability to use Boolean operators, endings shortening, faceted navigation, ranking search results. It has been found that not all repositories are equipped with instructions and recommendations on searching in them, which makes it difficult for inexperienced users to operate them.It has been revealed that the repository texts are provided with bibliographic metadata: as a rule, they are the author, the title, the imprint, and the source. However, some of them present more extended information: the detailed annotation, the abstract, the picture of the cover. The repositories are not equipped with linguistic tools, which prevents effective information search.It has been noted that the repositories studied are difficult to be found for a remote user. It is necessary to ensure their visibility, to provide information about existing repositories to potential users.In the conclusion, the benefits of creating repositories are enumerated: strengthening the authority of the institution, increasing the demand for library information resources, ensuring the visibility and accessibility of scientific and educational materials, and accelerating the knowledge exchange.
Improving Cancer Metastasis Detection via Effective Contrastive Learning

The metastasis detection in lymph nodes via microscopic examination of H&amp;E stained histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathological images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. The success of supervised deep learning is credited to a large labeled dataset, which is hard to obtain in medical image analysis. Contrastive learning, a branch of self-supervised learning, can help in this aspect through introducing an advanced strategy to learn discriminative feature representations from unlabeled images. In this paper, we propose to improve breast cancer metastasis detection through self-supervised contrastive learning, which is used as an accessional task in the detection pipeline, allowing a feature extractor to learn more valuable representations, even if there are fewer annotation images. Furthermore, we extend the proposed approach to exploit unlabeled images in a semi-supervised manner, as self-supervision does not need labeled data at all. Extensive experiments on the benchmark Camelyon2016 Grand Challenge dataset demonstrate that self-supervision can improve cancer metastasis detection performance leading to state-of-the-art results.
Developing and Comparing Cloud-based Fuzzy Systems for Monitoring Health Related Signals in Assistive Environments

Fuzzy logic system (FLS) based applications in Ambient Intelligence (AmI) have exhibited their capabilities in realizing intelligent environments. FLS deployment is usually associated with non-scalable hardware and software platforms. For intensive computation-based FLSs, distributing FLS openness as web services is known as fuzzy-as-a-service (FaaS), wherein a service is self-sufficiently developed from FLS itself, permits system independence, ingenuousness, load balancing and well-organised resource allocation. The fuzzy mark-up language (FML) and its related software libraries such as Simpful and Java FML (JFML) have added to the novelty. This study aims to develop and compare service-oriented architecture based FaaS by employing Microsoft Azure and Amazon Web Service (AWS) for AmI implementation in e-healthcare. Data is collected, processed and monitored via the Internet. Heart rate and SpO2 data are used in the study. The obtained data is evaluated by a fuzzy inference system using Microsoft Azure and AWS and displayed via a mobile application. A simulation scenario of real-time human activity recognition using a rule-based FLS is demonstrated. Results indicate that the proposed FaaS requires an average of 0.306 seconds to process a single real-time data using AWS and JFML. Conversely, Azure and Simpful require 0.456 seconds for processing. Thus, as per the selected scenario in this research, it can be concluded that there is a variation in the processing time using different cloud-based services. For example, AWS in one of the use case scenarios takes 19.65% less processing time of compared to Microsoft Azure.
Best Effort Voting Power Control for Byzantine-resilient Federated Learning Over the Air

Analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning in edge computing paradigm. When all edge devices (workers) simultaneously upload their local updates to the parameter server (PS) through the commonly shared time-frequency resources, the PS can only obtains the averaged update rather than the individual local ones. As a result, such a concurrent transmission and aggregation scheme reduces the latency and costs of communication but makes FLOA vulnerable to Byzantine attacks. For the design of Byzantine-resilient FLOA, this paper starts from analyzing the channel inversion (CI) power control mechanism that is widely used in existing FLOA literature. Our theoretical analysis indicates that although CI can achieve good learning performance in the non-attacking scenarios, it fails to work well with limited defensive capability to Byzantine attacks. Then, we propose a novel scheme called the best effort voting (BEV) power control policy, integrated with stochastic gradient descent (SGD). Our proposed BEV-SGD improves the robustness of FLOA to Byzantine attacks, by allowing all the workers to send their local updates at their maximum transmit power. Under the strongest-attacking circumstance, we derive the expected convergence rates of FLOA with CI and BEV, respectively. The comparison reveals that our BEV outperforms its counterpart with CI in terms of better convergence behavior, which is verified by experimental simulations.
Fast Generation of Superpixels With Lattice Topology

Serving as an essential step for many applications of image processing, superpixel generation has attracted a lot of attentions. Most existing superpixel generation algorithms focus on the boundary adherence and compactness of the superpixels, but ignore the topological consistency between the superpixels, which severely limites their applications in the subsequent tasks, especially in the CNN based image processing tasks. In this paper, we present a fast lattice superpixel generation algorithm, which can generate superpixels with lattice topology like the original pixels. We also propose a local similarity loss function to improve the segmentation accuracy of the generated lattice superpixels. The whole algorithm is parallelly implemented on GPU. We perform extensive experiments on three datasets (i.e., BSDS500, NYUv2 and VOC) to verify the efficacy of our algorithm. The experimental results show that our method achieves competitive results compared to the state-of-the-art methods.
MetaAge: Meta-Learning Personalized Age Estimators

Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-term aging pattern. In this paper, we aim to learn personalized age estimators without the above requirements and propose a meta-learning method named MetaAge for age estimation. Unlike most existing personalized methods that learn the parameters of a personalized estimator for each person in the training set, our method learns the mapping from identity information to age estimator parameters. Specifically, we introduce a personalized estimator meta-learner, which takes identity features as the input and outputs the parameters of customized estimators. In this way, our method learns the meta knowledge without the above requirements and seamlessly transfers the learned meta knowledge to the test set, which enables us to leverage the existing large-scale age datasets without any additional annotations. Extensive experimental results on three benchmark datasets including MORPH II, ChaLearn LAP 2015 and ChaLearn LAP 2016 databases demonstrate that our MetaAge significantly boosts the performance of existing personalized methods and outperforms the state-of-the-art approaches.
Visual analysis of blow molding machine multivariate time series data

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.
Cognitive mapping and its implication for understanding cultural behaviors and experiences in libraries

Cognitive mapping is a method typically adopted in ethnographical research to learn about library user behavior and experience in a cross-cultural context. Through reporting practical findings from a case study, where it served as a key method, the usefulness of cognitive mapping in revealing cognitive style and perceptions of the library and in understanding cultural users' behaviors and experiences is explored. Cognitive mapping offers an open exploratory way to help self-reflection of personal contexts regardless of language constraints. As such, it provides for the study of multiple cultural communities. In addition, its potential of usage in a virtual environment is presented. One limitation of this method is the extent to which it can reveal the holistic picture of library experience in isolation. As such, it works well when applied with other data collection techniques to form a flexible yet robust methodological framework to learning about library cultural groups.
Adaptive Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization

Weakly-supervised temporal action localization (W-TAL) aims to classify and localize all action instances in untrimmed videos under only video-level supervision. Without frame-level annotations, it is challenging for W-TAL methods to clearly distinguish actions and background, which severely degrades the action boundary localization and action proposal scoring. In this paper, we present an adaptive two-stream consensus network (A-TSCN) to address this problem. Our A-TSCN features an iterative refinement training scheme: a frame-level pseudo ground truth is generated and iteratively updated from a late-fusion activation sequence, and used to provide frame-level supervision for improved model training. Besides, we introduce an adaptive attention normalization loss, which adaptively selects action and background snippets according to video attention distribution. By differentiating the attention values of the selected action snippets and background snippets, it forces the predicted attention to act as a binary selection and promotes the precise localization of action boundaries. Furthermore, we propose a video-level and a snippet-level uncertainty estimator, and they can mitigate the adverse effect caused by learning from noisy pseudo ground truth. Experiments conducted on the THUMOS14, ActivityNet v1.2, ActivityNet v1.3, and HACS datasets show that our A-TSCN outperforms current state-of-the-art methods, and even achieves comparable performance with several fully-supervised methods.
Pose Estimation and Damage Characterization of Turbine Blades during Inspection Cycles and Component-Protective Disassembly Processes

Inspection in confined spaces and difficult-to-access machines is a challenging quality assurance task and particularly difficult to quantify and automate. Using the example of aero engine inspection, an approach for the high-precision inspection of movable turbine blades in confined spaces will be demonstrated. To assess the condition and damages of turbine blades, a borescopic inspection approach in which the pose of the turbine blades is estimated on the basis of measured point clouds is presented. By means of a feature extraction approach, film-cooling holes are identified and used to pre-align the measured point clouds to a reference geometry. Based on the segmented features of the measurement and reference geometry a RANSAC-based feature matching is applied, and a multi-stage registration process is performed. Subsequently, an initial damage assessment of the turbine blades is derived, and engine disassembly decisions can be assisted by metric geometry deviations. During engine disassembly, the blade root is exposed to high disassembly forces, which can damage the blade root and is crucial for possible repair. To check for dismantling damage, a fast inspection of the blade root is executed using the borescopic sensor.
Factors Influencing the Selection of a Blockchain Platform for Incorporating Data Provenance into Smart Contracts

A prominent application of blockchain smart contracts is in the modern manufacturing supply chain. This is mostly done to ensure data provenance. This research paper analyse the factors influencing the selection of a blockchain platform for incorporating data provenance into smart contracts. This research paper is written based on the literature study of various blockchain platforms. The current challenge that necessitated this research stems from the low performance, auction-based consensus mechanism, and high transaction fees of the blockchain platforms. Such contributing factors have negative financial implications associated with the deployment or interaction of smart contracts incorporating data provenance in the modern manufacturing supply chain. This research paper first gives details of some of the factors influencing the selection of smart contracts, then goes to compare how some of the latest blockchain technologies perform against these factors. Finally, analysis of the best possible blockchain platform for incorporating smart contracts is proposed.
Attention Based Large Scale Multi-agent Reinforcement Learning

Learning in large scale Multi-Agent Reinforcement Learning is fundamentally difficult due to the curse of dimensionality. In homogeneous multi-agent setting, mean field theory provides an effective way of scaling MARL to environments with many agents by abstracting other agents to a virtual mean agent, which assumes the impact of each player on the outcome is equal and infinitesimal. However, in some real scenarios, it is only several neighboring agents that affect the decision-making of an agent, need not all other agents. In addition, different neighboring agents may have different degrees of influence on the decision-making of an agent. In this paper, not restricted to homogeneous setting, we propose Adaptive Mean Field Multi-Agent Reinforcement Learning (AMF-MARL), which is based on the attention mechanism and can be used to deal with many agent scenarios in which there may be different influence relationships among agents. Specifically, we firstly derive the mean field approximation with adaptive weight. Then, we propose the Adaptive Mean Field Q-learning (AMF-Q) approach, and describe how to obtain the adaptive weight. Finally, we conduct experiment to study the learning effectiveness of proposed approach.
Jurisdictional arbitrage: combatting an inevitable by-product of cryptoasset regulation

Purpose This paper aims to provide an overview of different issues related to jurisdictional arbitrage found in general regulatory arbitrage literature and their projection to the specific area of cryptoasset regulation. Design/methodology/approach By distinguishing any parallel, analogous and neighbouring concepts, this paper attempts to clarify the notion of jurisdictional arbitrage. By discussing certain aspects and effects of three regulatory regimes, BitLicense, 5th Anti-Money Laundering Directive (AMLD5) and the European Commission’s Proposal for a Regulation on Markets in Crypto-assets (MiCa), it makes clear that national/State/regional policymakers have already failed to create arbitrage-proof regulatory frameworks by acting exclusively within their jurisdictional limits. Against this background, this paper discusses briefly regulatory competition and international harmonisation as alternative solutions to inappropriate and ineffective national/regional legislative approaches. Findings Based on a structured theoretical analysis, this paper reaches three important findings. First, academics, international bodies and other commentators use inaccurately the general concept of “regulatory arbitrage” to refer to the specific problem of jurisdictional arbitrage creating in this way an interpretative confusion; second, commentators confuse jurisdictional conflicts with jurisdictional arbitrage; third, the solutions to this regulatory problem can actually be found in its underlying causes. Originality/value To the best of the author’s knowledge, this is the first specific-issue paper on jurisdictional arbitrage in the context of cryptoasset regulation and aims to trigger further academic discussion on this evolving phenomenon and inform the development of future cryptoasset regulation combatting this problem.
Extended Kalman Filter-Based Position Estimation in Autonomous Vehicle Applications

As there is great demand in self-operating driving cars, probabilistic-based model is one of the emerging areas while considering the estimation of position of vehicles. In this paper, two probabilistic models are implemented in application to autonomous ground vehicles. Bayesian-state estimator has been repeatedly used for estimating the position as well as state of the vehicle. In this paper, simulation results are executed for Kalman filter (KF), extended Kalman filter (EKF) for predicting the positions of vehicle. A brief description on improvement of each method is being discussed by drawing comparison table.
Predictive 5G Uplink Slicing for Blockchain-driven Smart Energy Contracts

The energy grid is facing a paradigm shift away from traditionally centralized electricity generation towards dis-tributed renewable energy resources. These so-called Smart Grids (SGs) require a mechanism for balancing power consumption and generation. In this context, Blockchain (BC)-based Smart Contracts (SCs) have emerged as a means to facilitate distrib-uted transactions without requiring trust among the involved parties. Yet, resulting communication traffic loads need to be considered. Here, 5G network slicing promises to enable the coexistence of such mission critical services on a single shared physical communication infrastructure. Nevertheless, challenges in terms of latencies and resource efficiency exist. As static slicing mechanisms can be inefficient, we propose a predictive Machine Learning (ML)-driven approach to Resource Block (RB) scheduling by harnessing the Configured Grant (CG) mechanism in the 5G uplink. The developed solution is evaluated on the particularly challenging example of an energy grid driven by SCs. Based on an energy model derived from a real-world setup, we generate corresponding SC communication traffic. For this, predictive 5G slice radio resource allocation is employed to demonstrate significant improvements in terms of latency and spectrum usage efficiency. Thus, ML-enabled 5G network slicing for mission critical SCs is evaluated within large-scalable SGs.