Cesar U. Solis, Jorge Morales, Carlos M. Montelongo
This work establishes a simple algorithm to recover an information vector from a predefined database available every time. It is considered that the information analyzed may be incomplete, damaged, or corrupted. This algorithm is inspired by Hopfield Neural Networks (HNN), which allows the recursive reconstruction of an information vector through an energy-minimizing optimal process, but this paper presents a procedure that generates results in a single iteration. Images have been chosen for the information recovery application to build the vector information. In addition, a filter is added to the algorithm to focus on the most important information when reconstructing data, allowing it to work with damaged or incomplete vectors, even without losing the ability to be a non-iterative process. A brief theoretical introduction and a numerical validation for recovery information are shown with an example of a database containing 40 images.
The rapid proliferation of Internet of Things (IoT) devices has dramatically increased the demand for efficient data processing, making caching a critical solution for achieving high-performance and cost-effective storage in edge environments. However, small-scale edge devices often suffer from severe resource constraints. Furthermore, there is a scarcity of academic analyses addressing how various caching algorithms perform in such environments. To bridge this knowledge gap, we have proposed a cache simulation framework, CacheSim, as an open-source software solution for caching evaluation. CacheSim provides comprehensive metrics, including hit rate, performance, CPU usage, and power consumption, offering researchers valuable insights into the efficiency of different caching strategies. Through this platform, we aim to stimulate innovation in caching algorithms, encouraging the development of techniques optimized for the unique challenges posed by edge devices.
Yet the problem of evaluating sustainability policies over time stems from needing to monitor the changing nature of expert agreement needed for dependency-based improvements in the environment. While this is undoubtedly helpful for relatively sustainable action, easier said than done requires methodological systems to assess how agreement grows and solidifies. Here it should be noted that the conventional STATIS approach, while successful in multivariate assessment, lacks the capacity to evaluate expert assessments factoring uncertainty, indeterminacy, and subjectivity. Therefore, this research goes on to fill this void with the neutrosophic STATIS method applied to a panel of experts assessing ten (10) sustainability policies over three (3) time periods, resulting in ease of modeling opinion and visualization of intent. Ultimately, the results indicate that a consensus structure exists and evolves over time, yet there is agreement on the success of energy policies, while circular economy initiatives never become certain. Thus, this study contributes to the literature in two ways: First, theoretically, it legitimizes a consistent framework for dynamic evaluation of complex subjective assessments; Second, practically, it allows for managers to understand where there is agreement and disagreement to create more fluid public policies.
The success of a website depends heavily on the quality of an interface, which is assessed by a few factors: functionality, aesthetics, ergonomics. The objectives of this work's research are two web services dedicated to books, Lubimyczytać and Bookworm. In this study two experiments were conducted: eye tracking and a cognitive journey. In both cases, participants received a few problems to solve. Participants also filled out surveys that asked for their feedback. The research revealed issues with usability of the both interfaces. Lubimyczytać was found to be the winner of this comparison. In order to draw correct conclusions, it was necessary to take into consideration different aspects of examined applications: readability, web page’s logical layout. Mentioned points made tasks faster and easier to complete.
Information technology, Electronic computers. Computer science
In cloud databases,there are numerous configuration options,including internal database parameters and virtual machine resource configuration for the environment deployment,which collectively determine the database’s read/write performance and resource consumption.In the cloud environment with elastic resources,users are concerned about both the database’s service performance and resource consumption costs.However,due to the large number of configuration options and rapid workload changes,finding the optimal combination of configurations becomes challenging.To address the online tuning scenario with dynamically changing workloads,this paper proposes CoTune,a fast tuning method for coordinating cloud database resources and parameters.This method focuses on OLTP workloads and iteratively adjusts the configurations of virtual machine resources and database parameters to minimize resource consumption while ensuring service quality.The method introduces several key innovations:firstly,it adopts a three-stage approach within each tuning cycle to adjust resource quotas and database parameters,prioritizing service quality;secondly,it classifies the impact of database parameters on different resources,reducing the search space and enabling rapid parameter adjustments;and finally,it incorporates a reinforcement learning model for database parameter tuning,with a specific reward function designed to quickly obtain reward values and accelerate the tuning frequency.Experimental results demonstrate that,compared to approaches that simultaneously tune resources and parameters or solely focus on resource tuning,the proposed method reduces resource consumption while maintaining service quality.Through rapid iterative tuning,it effectively addresses the challenges posed by workload variations and achieves more efficient resource utilization in dynamic workload environments.
The syntactic information of a dependency tree is an essential feature in relation extraction studies. Traditional dependency-based relation extraction methods can be categorized into hard pruning methods, which aim to remove unnecessary information, and soft pruning methods, which aim to utilize all lexical information. However, hard pruning has the potential to overlook important lexical information, while soft pruning can weaken the syntactic information between entities. As a result, recent studies in relation extraction have been shifting from dependency-based methods to pre-trained language model (LM) based methods. Nonetheless, LM-based methods increasingly demand larger language models and additional data. This trend leads to higher resource consumption, longer training times, and increased computational costs, yet often results in only marginal performance improvements. To address this problem, we propose a relation extraction model based on an entity-centric dependency tree: a dependency tree that is reconstructed by considering entities as root nodes. Using the entity-centric dependency tree, the proposed method can capture the syntactic information of an input sentence without losing lexical information. Additionally, we propose a novel model that utilizes entity-centric dependency trees in conjunction with language models, enabling efficient relation extraction without the need for additional data or larger models. In experiments with representative sentence-level relation extraction datasets such as TACRED, Re-TACRED, and SemEval 2010 Task 8, the proposed method achieves F1-scores of 74.9%, 91.2%, and 90.5%, respectively, which are state-of-the-art performances.
In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data.
Abstract In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data. The data‐driven machine learning models are now offering more or less worthy results when they are certainly balanced in the input data sets. Imbalanced data occurs when an unequal distribution of classes occurs in the input datasets. Building a predictive model on the imbalanced data set would cause a model that appears to yield high accuracy but does not generalize well to the new data in the minority class. Now the time has come to look into the datasets which are not so‐called ‘balanced’ in nature but such datasets are generally encountered frequently in a workspace. To prevent creating models with false levels of accuracy, the imbalanced data should be rearranged before creating a predictive model. Those data are, sometimes, voluminous, heterogeneous and complex in nature and generate from different autonomous sources with distributed and decentralized control. The driving force is to efficiently handle these data sets using latest tools and techniques for research and commercial insights. The present article provides different such tools and techniques, in different computing frameworks, to handle such Internet of Things and other related datasets to review common techniques for handling imbalanced data in data ecosystems and offers a comparative data modelling framework in Keras for balanced and imbalanced datasets.
Computational linguistics. Natural language processing, Computer software
A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate <i>t</i> tests (Hotelling’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>T</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.
Daphne Economou, Melissa Russi, Ioannis Doumanis
et al.
One in every six persons in the UK suffers a hearing loss, either as a condition they have been born with, or they developed during their life. Nine hundred thousand people in the UK are severely or profoundly deaf. Based on a study by Action on Hearing Loss UK in 2013 only 17 percent of this population, can use the British Sign Language (BSL). That leaves a massive proportion of people with a hearing impediment who do not use sign language struggling in social interaction and suffering from emotional distress. It also leaves even a larger proportion of Hearing people who cannot communicate with those of the deaf community. This paper presents a Serious Game (SG) that aims to close the communication gap between able hearing people and people with hearing impairment by providing a tool that facilitates BSL learning targeting the adult population. The paper presents the theoretical framework supporting adult learning based on which a SG game using Virtual Reality (VR) technology has been developed. The paper explains the experimental framework of the study. It presents the creation of the research instruments to facilitate the study comprising of a SG that integrates video and conventional video-based educational material. It reports and analyses the study results that demonstrate the advantage of the SG in effectively supporting users learning a set of BSL signs. It also presents qualitative outcomes that inform the further development of the game to serve learning needs. The paper closes with conclusions, directions for further development of this educational resource, and future studies.
Aiming at the background subtraction problem of surveillance video mixed with impact noise during compression sampling,a robust video reconstruction and decomposition model based on Welsch M-estimation and tensor decomposition regularization is proposed.In order to reduce the impact of effect noise on reconstruction performance,Welsch M-estimation is used to replace the mean square error as a cost function to measure the reconstruction error,and a more robust reconstruction model is constructed.Under the tensor framework,the low-rank difference priors of the background in different dimensions and different scenarios are introduced into the background modeling to obtain the reconstruction and decomposition model,and based on half-quadratic theory and multi-block ADMM method,the corresponding optimization algorithm is given.Experimental results show that compared with the algorithms such as SpaRCS and CS-L1PCA,the algorithm can maintain the robustness of video reconstruction and decomposition under the condition of mixed impact noise.
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in the consensus process, hence no private data are transferred. With the proposed use of entropy as an internal measure of consensus clustering validation at each machine, the cluster centers of the local machines with higher expected clustering validity have more influence in the final consensus centers. We also employ relative cost function of the local Fuzzy C-Means (FCM) and the number of data points in each machine as measures of relative machine validity as compared to other machines and its reliability, respectively. The utility of the proposed consensus strategy is examined on 18 datasets from the UCI repository in terms of clustering accuracy and speed up against the centralized version of FCM. Several experiments confirm that the proposed approach yields to higher speed up and accuracy while maintaining data security due to its protected and distributed processing approach.
Network function virtualization technology improves the flexibility of service function chains' deployment.However,the virtual network functions are under the pressure of uncertain failures and malicious attacks.The existing redundant backup methods can solve the problem of VNF failures to some extent,it does not consider the defects of node homogeneity in the face of malicious attacks.A deployment method considering the heterogeneity of nodes was proposed,guaranteeing the heterogeneity of nodes when perform redundant backup and remapping.Simulation experiments demonstrate that the proposed method significantly increases attacker's attack time cost under the cost of the request acceptance rate decreases by 3.8% and the bandwidth consumption increase by 9.2% comparing to the homogeneity backup method.
To improve the optimize efficiency of interactive genetic algorithm,this paper proposes an interactive genetic algorithm based on hybrid fitness of evolutionary individuals.The calculation method of fitness uncertainty is designed while analyzing the characteristics of fitness noise,uncertainty analysis is performed on individual adaptive values of the user evaluation,and two kinds of adaptive value types including the single value type and the interval value type are divided on the basis of the uncertainty degree of the adaptive value.It builds a corresponding mathematical model by aiming at the adaptive value types,the individual adaptive values are corrected,and participate in the subsequent evolution.The two kinds of corrected adaptive values simultaneously participate in the evolution optimization,the design conforming to the psychological need of the user is expected to be generated,and the goal of efficient optimization is achieved.The proposed algorithm is applied to a portable wine pot design system.Experimental results confirm that,compared with IGA-IIF algorithm and T-IGA algorithm,it has advantages in improving optimization efficiency and alleviating user fatigue while improving its efficiency in exploration and practical application.
It is a major challenge to transfer target sensing data efficiently to sink in Internet of things. The low-efficiency data transmission can cause low quality of service. To realize the emergent detection and periodic data gathering, the sensed data should be transferred to the sink efficiently and quickly. Recently, there are many related studies. However, there are few researches taking energy efficiency, transport delay, and network reliability into comprehensive consideration. In this article, a novel adaptive green and reliable routing scheme based on a fuzzy logic system is proposed in consideration of energy efficiency, end-to-end transport delay, and network transmission reliability. The key idea of the proposed scheme is to generate different number of renewed packet copies after certain steps according to the fuzzy inference. The fuzzy inference reflects the knowledge that the nodes in the region far to the sink and with more remaining energy initiate and transmit more packet copies concurrently by multiple routing paths to ensure the success rate of data transmission, whereas less. Thus, the high energy efficiency and low latency are obtained for data collection. Our analysis and simulation results show that adaptive green and reliable routing is more superior than the existing scheme.
Nowadays, one of the biggest threats for modern computer networks are the cyber attacks. One of the possible ways how to increase the level of computer networks security is a deployment of a network intrusion detection system. This paper deals with the behavior of the network intrusion detection system during specific network intrusion. We formally describe this network intrusion by the modal linear logic formula. Based on this formula, logical space and logical time is expressed from the attacker, and the network environment point of view in the usage of the Ludics theory.