Agreement Violations Research Articles

A Unique Way To Mitigate Service Level Agreement Violation With Sensor System Energy Saving

A Unique Way To Mitigate Service Level Agreement Violation With Sensor System Energy Saving

Is there a multilingual advantage in gender agreement processing? Evidence from self-paced reading

ABSTRACT Do learners who have mastered two gender systems prior to learning a third have an advantage in gender processing over those who have mastered only one gender system? To address this question, we recruited two groups of adult Polish learners of L3/Ln Swedish with and without prior knowledge of German. They completed a self-paced reading task in Swedish that involved two types of gender marking: the indefinite article, which is realised similarly in Swedish and German, and the definite suffix, which is uniquely present in Swedish. We found that while both groups were sensitive to gender agreement violations involving the indefinite article, only learners with knowledge of German showed sensitivity to gender agreement violations involving the definite suffix. Therefore, knowledge of non-native German conferred an advantage for the processing of dissimilar rather than similar gender marking. We interpret these findings in light of morphological transfer from German that consists in the employment of the existing morphemes and assigning them new grammatical functions. More broadly, the study indicates that the gender system in a non-native language rather than the native language, might be more helpful for the acquisition of a new type of gender marking in L3/Ln.

Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features

Cloud systems, typically comprised of various components ( e.g. , microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance for cloud vendors. In current practice, crucial metrics, i.e. , key performance indicators (KPIs), are monitored periodically to provide insight into the operational status of components. Identifying performance issues is often formulated as an anomaly detection problem, which is tackled by analyzing each metric independently. However, this approach overlooks the complex dependencies existing among cloud components. Some graph neural network-based methods take both temporal and relational information into account, however, the correlation violations in the metrics that serve as indicators of underlying performance issues are difficult for them to identify. Furthermore, a large volume of components in a cloud system results in a vast array of noisy metrics. This complexity renders it impractical for engineers to fully comprehend the correlations, making it challenging to identify performance issues accurately. To address these limitations, we propose Identifying Performance Issues based on Relational-Temporal Features (ISOLATE ), a learning-based approach that leverages both the relational and temporal features of metrics to identify performance issues. In particular, it adopts a graph neural network with attention to characterizing the relations among metrics and extracts long-term and multi-scale temporal patterns using a GRU and a convolution network, respectively. The learned graph attention weights can be further used to localize the correlation-violated metrics. Moreover, to relieve the impact of noisy data, ISOLATE utilizes a positive unlabeled learning strategy that tags pseudo labels based on a small portion of confirmed negative examples. Extensive evaluation on both public and industrial datasets shows that ISOLATE outperforms all baseline models with 0.945 F1-score and 0.920 Hit rate@3. The ablation study also proves the effectiveness of the relational-temporal features and the PU-learning strategy. Furthermore, we share the success stories of leveraging ISOLATE to identify performance issues in Huawei Cloud, which demonstrates its superiority in practice.

Enhanced Ant Colony Based VM Selection and Consolidation for Energy Conservation

Cloud Computing (CC) involves extensive data centers with numerous computing nodes that consume significant electrical energy. Researchers have identified high service-level agreement (SLA) violations and excessive energy consumption (EC) as major challenges in CC. Traditional approaches often focus on reducing EC but tend to overlook SLA violations, particularly when selecting Virtual Machines (VMs) from overloaded hosts. To address these issues, this paper introduces the Enhanced Ant Colony Optimization (EACO) algorithm, aims to reduce high EC and SLA violations by utilizing a unique approach where the best-performing ant explores movement patterns and identifies distances between movements. The algorithm comprises three key steps: tracking pheromone trails, updating pheromones and selecting the cities (VMs). The effectiveness of EACO was validated through simulations using CloudSim. Compared to existing techniques, EACO demonstrated a significant reduction in EC, achieving approximately 41-44% lower energy consumption than the traditional Ant Colony Optimization (ACO) algorithm when applied to Planet Lab data. This suggests that EACO offers a more efficient and stable solution for managing EC and SLA violations in cloud environments.

Service Selection under Uncertainty

Cloud computing has emerged as a popular computing paradigm, providing scalable and pay-per-use services to execute a variety of tasks. However, selecting the most suitable cloud services for a client can be challenging, as it involves taking into account the service characteristics and a set of client restrictions. In this paper, we formalize the problem of Service Selection under Uncertainty (SSuU) as an optimization problem. Our goal is to allocate tasks to appropriate cloud services while considering the probability of Service Level Agreement (SLA) violations and a set of client restrictions. We introduce an efficient dynamic programming approach to calculate the probability of SLA violations incrementally over time. We prove that the SSuU problem is strongly NP-complete, and propose an Integer Linear Programming (ILP) formulation and an Iterated Local Search-based algorithm for tackling it. To facilitate evaluation, we also introduce a set of benchmark instances for the SSuU problem. We extensively evaluate our proposed solutions on 94 input instances and compare them to the exact method (i.e., which always produces the optimal solution). Our results demonstrate that the metaheuristic approach is significantly faster and leads to exact solutions in the great majority of the evaluation scenarios.

Network load balancing and data categorization in cloud computing

Cloud computing (CC) is rising quickly as a successful model presenting an on-demand structure. In the CC, the present investigation shows that load-balancing methods established on meta-heuristics offer better solutions for appropriate scheduling and allotment of resources. Conversely, several traditional approaches believe in only some quality of service (QoS) metrics and reject several significant components. Network load balancing and data categorization (NBDC) is proposed. This approach aims to enhance load balancing in the cloud field. This approach consists of two phases: the support vector machine (SVM) algorithm-based data categorization and the ant colony optimization (ACO) algorithm for distributing the network load on the virtual machine (VM). The SVM algorithm performs several data formats, such as text, image, audio, and video, resultant data class that offers high categorization accuracy in the cloud. The ACO algorithm reaches an efficient load balancing based on the time of execution (TE), time of throughput (TT), time of overhead (TO), time of optimization, and migration count (MC). Simulation results related to the baseline approach demonstrate an enhanced system function in terms of service level agreement violation, throughput, execution time, energy utilization, and execution time.

Law Enforcement of Environmental Agreement Violations by Business Entities

In the increasing demands for environmental protection and management which are currently increasing and require attention from every child of the nation as both policy makers and the community in general, the environmental law since the enactment of Law Number 4 of 1982 to Law Number 32 of 2009 which was then codified in the main ideas of Law Number 6 of 2023 concerning Job Creation, this is the answer to the government's efforts in enforcing environmental law to be better and more beneficial. In the application of criminal law sanctions as a result of criminal acts by reviewing and highlighting the application of law to the implementation of environmental management as an effort for environmentally sustainable development, problems regarding the consequences of criminal acts committed by Business Entities so that they can result in additional criminal penalties must be based on legal norms by paying attention to the level of public awareness and global environmental developments and international legal instruments related to the environment as well as the awareness and life of the community in relation to environmental management have developed in such a way that they need to be refined to achieve the goals of environmentally sustainable development.

Working memory and L2 gender agreement processing in structural distance conditions

Previous studies examining the role of executive function in L2 processing show that working memory (WM) facilitates the processing of agreement in local domains in adult second language (L2) learners. Furthermore, other studies explored whether L2 learners can establish the agreement operation across phrases (i.e., structural distance) and whether WM intervenes in the said linguistic computation. However, these studies have often included both syntactic and linear distance in their stimuli, making it impossible to discern whether WM effects emerge from physical or syntactic reasons. The present study assesses how verbal WM updating and L2 proficiency modulate syntactic processing. Beginner and advanced adult English L2 learners of Spanish and Spanish monolinguals completed a verbal WM updating task, and a self-paced reading task containing Spanish sentences with gender agreement and disagreement within and across phrases. Results show that Spanish monolinguals exhibited sensitivity to gender agreement violations in local domains and in structural distance conditions, while beginner L2 learners were not sensitive to violations in either condition. Advanced learners, on the other hand, detected violations in local domains, and their verbal WM updating spans were associated with sensitivity to violations across phrases. Taken together, the findings suggest that (a) morphosyntactically complex structures consume cognitive resources in great number, and (b) L2 processing is qualitatively similar albeit quantitatively different from native processing, thus providing evidence that late bilinguals may process the L2 in a native-like manner.

An integrated optimization method to task scheduling and VM placement for green datacenters

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average.

Morphosyntactic prediction in automatic neural processing of spoken language: EEG evidence

Automatic parsing of syntactic information by the human brain is a well-established phenomenon, but its mechanisms remain poorly understood. Its best-known neurophysiological reflection is the so-called early left-anterior negativity (ELAN) component of event-related potentials (ERPs), with two alternative hypotheses for its origin: (1) error detection, or (2) morphosyntactic prediction/priming. To test these alternatives, we conducted two experiments using a non-attend passive design with visual distraction and recorded ERPs to spoken pronoun-verb phrases with/without agreement violations and to the same critical verbs presented in isolation without preceding pronouns. The results revealed an ELAN at ∼130-220 ms for pronoun-verb gender agreement violations, confirming a high degree of automaticity in early morphosyntactic parsing. Critically, the strongest ELAN was elicited by verbs outside phrasal context, which suggests that the typical ELAN pattern is underpinned by a reduction of ERP amplitudes for felicitous combinations, reflecting syntactic priming/predictability between related words/morphemes (potentially mediated by associative links formed during previous linguistic experience) rather than specialised error-detection processes.

Secure Virtual Machine Migration and Host Overload Detection Using Modified Pelican Optimization with Variable Load Mean Function

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system.

Crosslinguistic influence in bilingual morphosyntactic processing: Effects of language-common, language-contrasting, and language-specific information

Abstract This study investigated how second language (L2) learners process the Korean numeral quantifier construction by using transferable and nontransferable information. For Chinese-speaking learners of Korean (Chinese group), agreement between an honorific numeral quantifier and a noun in Korean constitutes transferable information in the canonical structure and nontransferable L2-specific information in the scrambled structure. For Japanese-speaking learners of Korean (Japanese group), this information gives rise to crosslinguistic conflicts in both structures. The results from a self-paced reading task showed that the Japanese group did not exhibit sensitivity to grammatical errors in both structures, whereas the Chinese group detected the agreement violation in the canonical but not in the scrambled structure. When a context sentence was provided to license scrambling in the test sentence, however, another group of Chinese-speaking learners of Korean showed sensitivity to the violation. These findings suggest varying degrees of crosslinguistic influence in L2 sentence processing.

A workload prediction model for reducing service level agreement violations in cloud data centers

Cloud computing has become an emerging technology that offers services based on the pay-as-usage model. The cloud provides several advantages, but these advantages come with challenges, such as reducing Service Level Agreement (SLA) violations, efficient resource utilization, reducing energy consumption, etc., needing attention to leverage customer satisfaction and benefit cloud service providers. Workload prediction is a strategy that provides many benefits: reduced SLA violation, resource scaling, and resource optimization by predicting future workload. However, due to the varying workload of cloud applications, it is difficult to predict the workload accurately, and it fails for long-term dependencies. We propose a methodology based on Multiplicative Long Short Term Memory (mLSTM) that allows input-dependent transitions and considers long-term dependencies to predict the workload to address this issue. The proposed method is implemented and compared with other variants of LSTM used in literature for workload prediction purposes. The proposed work outperforms existing variants of LSTM in terms of prediction accuracy.

Exposing Legal Pitfalls: Trademark Misuse in Franchise Agreements and Partnership Liability

The legitimate use of the trademark is performed by developing a license agreement containing the rights and obligations between the licensor and licensee. This license agreement is based on the principles of the consensus contained in contract law, namely the principle of good faith and freedom of treaty. In reality, some parties do not comply with these principles, causing disputes related to the rights and obligations of the parties, as well as license misuse. Therefore, this study aimed to analyze the trademark license arrangement and the form of legal liability for contract misuse in franchise system. Doctrinal or normative legal study methodswere used, with primary and secondary data obtained through interviews and literature review. As a civil law country, the regulation of trademark license agreements in Indonesia emphasized applicable policies to provide legal certainty. The results showed that the misuse of these agreements caused legal consequences for the offending party. These consequences included liability for aspects of franchise agreement violation, trademark infringement, as well as civil and criminal liabilities.

Analisis Prinsip Kesantunan Dalam Teks Anekdot Pada Buku Cerdas Cergas Berbahasa Dan Bersastra Indonesia Kelas X

Oral and written communication is broad and free. However, the content in it must comply with applicable rules, one of which is the principle of politeness contained in three anecdotal texts entitled “Liburan Kuli Bangunan”, “Perundungan Tanda Sayang”, and “Korupsi Kecil”. Criticism in it is conveyed by paying attention to the principles of politeness. However, not all speech contained in the text meets the principles of politeness. Therefore, the author was moved to carry out an analysis of the principles of politeness in the anecdotal texts entitled “Liburan Kuli Bangunan”, “Perundungan Tanda Sayang”, and “Korupsi Kecil” contained in the Cerdas Cergas Berbahasa dan Bersastra Indonesia SMA/SMK Kelas X. This analysis aims to discover and examine further the principles of language politeness by the discipline of pragmatics. The approach used in this research is a qualitative descriptive approach. The results of data analysis were collected using reading and note-taking techniques. The results of the analysis in this research found 19 violations of politeness principles, dominated by violations of the thimble agreement. Apart from that, there is compliance with the principles of politeness, totaling 4 utterances, namely 2 on the thimble of agreement, and the other 2 on the thimble of humility and the thimble of feelings. With this research, it is hoped that it will be useful for providing understanding and as additional reference material for further research regarding the principles of language politeness in communication.

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

The effect of asymmetric grammatical gender systems on second language processing: Evidence from ERPs

Aims and objectives: This study examined the effect of the first language (L1) grammatical gender (GG) system on the second language (L2) gender agreement processing, particularly when L1 and L2 are asymmetric in their number of gender values such as in Spanish and German languages. Methodology: Behavioral and brain responses (ERPs) were registered while German native-speakers (16) and Spanish-German bilinguals (24) read sentences for comprehension. We manipulated gender agreement between the determiner and the noun: Der/*die Ring, “themasc/*thefem ringmasc.” The nouns were either of similar or opposite gender across languages or unique in L2 (neuter gender). Data and analysis: Behavioral responses measured participants’ ability to discriminate grammatical from ungrammatical sentences. For ERPs, N400 and P600 components were analyzed by means of a linear mixed-effect model. Findings/conclusion: Behavioral data showed that German native-speakers discriminated gender agreement violations better than Spanish-German bilinguals, irrespective of lexical gender overlap across languages. Native speakers showed a P600 effect to gender agreement violations, whereas bilinguals showed an N400-like effect to gender mismatches that involved same-gender nouns across languages and neuter gender nouns. Interestingly, opposite gender nouns between L1 and L2 that agreed in gender with the determiner were processed as incorrect for the bilinguals. These results suggest transfer effects from L2 learners’ L1 GG system that facilitate L2 gender agreement processing for similar and neuter gender nouns or hinder it for opposite gender nouns across languages. Originality: This study used ERPs to demonstrate how asymmetries in terms of the number of lexical gender categories between L1 and L2 GG systems can affect L2 learners’ ability to process L2 gender agreement in a native-like manner. Significance/implications: These findings elucidate the challenges L2 learners face when learning a three-gender value system in L2, including a great number of gender incongruences between L1 and L2 nouns and a new neuter gender category.

Energy-performance aware virtual machines migration in cloud network by using prediction and fuzzy approaches

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration.

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds.

Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.

SSEPC cloud: Carbon footprint aware power efficient virtual machine placement in cloud milieu

The consumption of energy and carbon emission in cloud datacenters are the alarming issues in recent times, while optimizing the average response time and service level agreement (SLA) violations. Handful of researches have been conducted in these domains during virtual machine placement (VMP) in cloud milieu. Moreover it is hard to find researches on VMP considering the cloud regions and the availability zones along with the datacenters, although both of them play significant roles in VMP. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable regions, availability zones, datacenters, and servers in a cloud environment, while optimizing the mentioned quality of service parameters. Our suggested technique is compared with some of the contemporary hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that the proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy and emitting 28.8%, 32.83%, and 37.45% less carbon, whereas reducing the average response time by 11.43%, 18.57%, and 26% as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. In case of SLA violations, SSEPC has shown its effectiveness by lessening the value of this parameter by 0.4%, 1.2%, and 2.8% as compared to SCA-SSA, GATA, and OEMACS respectively.