Different Levels Of Information Research Articles

Equilibrium balking strategies in Markovian queues with a single working vacation and vacation interruption

In this work, we study customer equilibrium balking strategies in single-server Markovian queueing systems with a single working vacation and vacation interruption. That is, the server undertakes workload with a lower service rate rather than completely stops working during the vacation period. At the instant of completing the service during a vacation period, if there are any customers in the system, the server ends the vacation compulsively and returns to the normal working level, i.e. vacation interruption occurs. Upon arrival, the customers must make a decision whether to join or balk the system based on the information about the number of customers in the system and the server status, along with the reward–cost structure of the system. Accordingly, four cases with respect to different levels of information are introduced and the corresponding Nash equilibria are presented. In addition, the impact of the information levels, as well as of several system parameters, on the equilibrium thresholds and equilibrium joining probabilities is illustrated by numerical examples.

Differences of the Lithuanian Regions Situation in the Labour Market

The article is based on the results of National science program ‘Welfare society’ project ‘Regional disparities of welfare inLithuania’ (project number GER-005/2017). The project is funded by the Lithuanian Council of Science. The aim of the work is toestimate features of index distribution of labor market and labor resources (working-age people) in the main country regions.Considering the fact about migration changes, a lot of attention in the paper is paid to the estimation of demographic situation impacton the labour market. Apart from the ordinary statistical data, we use results of systemic poverty problem analysis in Lithuania.According to the disposition of information and its opportunities comparison, the data of counties and municipalities were usedduring the research. Different levels of information have helped to detect more detailed differences between labour markets in theregions. The comparison of different sources of information has revealed peculiarities of unemployment change and differentiationthat have not been explored as yet.

Place Classification With a Graph Regularized Deep Neural Network

Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. In recent years, there is a high exploitation of artificial intelligence algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. First, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Second, each layer of data are fed into a deep neural network for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effectiveness of our end-to-end place classification framework in which both the multilayer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.

Financial markets with a large trader

We construct a large trader model using tools from nonlinear stochastic integration theory and an impact function. It encompasses many well-known models from the literature. In particular, the model allows price changes to depend on the size as well as on the speed and timing of the large trader’s transactions. Moreover, a volume impact limit order book can be studied in this framework. Relaxing a condition about existence of a universal martingale measure governing all resulting small trader models, we can show absence of arbitrage for the small trader under mild conditions. Furthermore, a case study on utility maximization from terminal wealth highlights new phenomena that can arise in our framework. Finally, an outlook on further research provides insights on (no) arbitrage opportunities for the large trader and how different levels of information may affect our analysis.

Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation

• The aim of this contribution is to analyse the real borderlines of the 'right to explanation' in the GDPR and to discretely distinguish between different levels of information and of consumers' awareness in the 'black box society'. In order to combine transparency and comprehensibility we propose the new concept of algorithm 'legibility'. • We argue that a systemic interpretation is needed in this field, since it can be beneficial not only for individuals but also for businesses. This may be an opportunity for auditing algorithms and correcting unknown machine biases, thus similarly enhancing the quality of decision-making outputs. • Accordingly, we show how a systemic interpretation of articles 13-15 and 22 GDPR is necessary, considering in particular that: the threshold of minimum human intervention required so that the decision-making is 'solely' automated (Art. 22(1)) can also include nominal human intervention; the envisaged 'significant effects' on individuals (Art. 22(1)) can encompass as well marketing manipulation, price discrimination, etc.; 'meaningful information' that should be provided to data subjects about the logic, significance and consequences of decision-making (Art. 15(1)(h)) should be read as 'legibility' of architecture and implementation of algorithmic processing; trade secret protection might limit the right of access of data subjects, but there is a general legal favour for data protection rights that should reduce the impact of trade secrets protection. • In addition, we recommend a 'legibility test' that data controllers should perform in order to comply with the duty to provide meaningful information about the logic involved in an automated decision-making.

Seeing the woods for the trees: the problem of information inefficiency and information overload on operator performance

One of the recurring questions in designing dynamic control environments is whether providing more information leads to better operational decisions. The idea of having every piece of information is increasingly tempting (and in safety critical domains often mandatory) but has become a potential obstacle for designers and operators. The present research study examined this challenge of appropriate information design and usability within a railway control setting. A laboratory study was conducted to investigate the presentation of different levels of information (taken from data processing framework, Dadashi et al. in Ergonomics 57(3):387–402, 2014) and the association with, and potential prediction of, the performance of a human operator when completing a cognitively demanding problem-solving scenario within railways. Results indicated that presenting users only with information corresponding to their cognitive task, and in the absence of other, non task-relevant information, improves the performance of their problem-solving/alarm handling. Knowing the key features of interest to various agents (machine or human) and using the data processing framework to guide the optimal level of information required by each of these agents could potentially lead to safer and more usable designs.

Discriminación temporal condicionada: una evaluación de efectos de secuencia

Through a conditional temporal discrimination task with humans was assessed the learning promoted by different pair sequences. In detail, six groups were exposed to different paired sequences, which were selected to include different levels of information. From the most basic to the most inclusive, the order of the sequences was as follows: Random, Permutations, Disordered, Ramp, Decreasing and Incremental. The results showed that the relationship between information sources amount and hits was direct, and the trajectories between blocks was traced in such groups, or, alternatively, their asymptote was higher. However, the most consistent and significant differences were observed when comparing the first three sequences together over the last three. The results are discussed through two approaches used in the learning area.

Information-driven Persistent Sensing of a Non-cooperative Mobile Target Using UAVs

This paper addresses the persistent sensing problem of moving ground targets of interest using a group of fixed wing UAVs. Especially, we aim to overcome the challenge of physical obscuration in complex mission environments. To this end, the persistent sensing problem is formulated under an optimal control framework, i.e. deploying and managing UAVs in a way maximising the visibility to the non-cooperative target.The main issue with such a persistent sensing problem is that it generally requires the knowledge of future target positions, which is uncertain. To mitigate this issue, a probabilistic map of the future target position is widely utilised. However, most of the probabilistic models use only limited information of the target. This paper proposes an innovative framework that can make the best use of all available information, not only limited information. For the validation of the feasibility, the performance of the proposed framework is tested in a Manhattan-type controlled urban environment. All the simulation tests use the same framework proposed, but utilise different level of information. The simulation results confirm that the performance of the persistent sensing significantly improves, up to 30%, when incorporating all available target information.

Effective HTCondor-based monitoring system for CMS

The CMS experiment at the LHC relies on HTCondor and glideinWMS as its primary batch and pilot-based Grid provisioning systems, respectively. Given the scale of the global queue in CMS, the operators found it increasingly difficult to monitor the pool to find problems and fix them. The operators had to rely on several different web pages, with several different levels of information, and sift tirelessly through log files in order to monitor the pool completely. Therefore, coming up with a suitable monitoring system was one of the crucial items before the beginning of the LHC Run 2 in order to ensure early detection of issues and to give a good overview of the whole pool. Our new monitoring page utilizes the HTCondor ClassAd information to provide a complete picture of the whole submission infrastructure in CMS. The monitoring page includes useful information from HTCondor schedulers, central managers, the glideinWMS frontend, and factories. It also incorporates information about users and tasks making it easy for operators to provide support and debug issues.

A Methodology to Detect and Update Active Deformation Areas Based on Sentinel-1 SAR Images

This work is focused on deformation activity mapping and monitoring using Sentinel-1 (S-1) data and the DInSAR (Differential Interferometric Synthetic Aperture Radar) technique. The main goal is to present a procedure to periodically update and assess the geohazard activity (volcanic activity, landslides and ground-subsidence) of a given area by exploiting the wide area coverage and the high coherence and temporal sampling (revisit time up to six days) provided by the S-1 satellites. The main products of the procedure are two updatable maps: the deformation activity map and the active deformation areas map. These maps present two different levels of information aimed at different levels of geohazard risk management, from a very simplified level of information to the classical deformation map based on SAR interferometry. The methodology has been successfully applied to La Gomera, Tenerife and Gran Canaria Islands (Canary Island archipelago). The main obtained results are discussed.

EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP MAPPING USING SATELLITE IMAGE TIME-SERIES DATA

Abstract. Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

Strategic Joining and Optimal Pricing in the Cognitive Radio System With Delay-Sensitive Secondary Users

The transmission times with generally random distributed for both primary user (PU) and secondary user (SU) in a cognitive radio system with a single PU band is investigated. To characterize the random access protocols of wireless networks, a retrial queueing system is employed to model the decentralized and centralized behavior of SUs. Based on the tradeoff between the service reward and the delay cost, each SU must decide whether to join or balk the system upon arrival based on different levels of information provided by the system. Two situations in which SUs have no information or partially observable information are studied. In each situation, two kinds of strategies are considered: 1) individual equilibrium strategies (i.e., non-cooperative strategies) that maximize SUs’ own profit and 2) socially optimal strategies (i.e., cooperative strategies) that maximize the expected social welfare. Moreover, comparisons are carried out between these two kinds of strategies. The results indicate that the equilibrium joining probability in the non-cooperative case is not less than the optimal joining probability in the cooperative case. To regulate the SUs’ behavior, an equilibrium pricing scheme is proposed to make these strategies coincide. In addition, we observe that the expected delay and the mean number of SUs in the system only depend on the first two moments of the SU’s transmission time. Numerical examples also show that the partially observable queue is more profitable from the perspective of the social planner.

Moving beyond the MSY concept to reflect multidimensional fisheries management objectives

Maximising the long term average catch of single stock fisheries as prescribed by the globally-legislated MSY objective is unlikely to ensure ecosystem, economic, social and governance sustainability unless an effort is made to explicitly include these considerations. We investigated how objectives to be maximised can be combined with sustainability constraints aiming specifically at one or more of these four sustainability pillars. The study was conducted as a three-year interactive process involving 290 participating science, industry, NGO and management representatives from six different European regions. Economic considerations and inclusive governance were generally preferred as the key objectives to be maximised in complex fisheries, recognising that ecosystem, social and governance constraints are also key aspects of sustainability in all regions. Relative preferences differed between regions and cases but were similar across a series of workshops, different levels of information provided and the form of elicitation methods used as long as major shifts in context or stakeholder composition did not occur. Maximising inclusiveness in governance, particularly the inclusiveness of affected stakeholders, was highly preferred by participants across the project. This suggests that advice incorporating flexibility in the interpretation of objectives to leave room for meaningful inclusiveness in decision-making processes is likely to be a prerequisite for stakeholder buy-in to management decisions.

Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.

Government Transparency: Reality or Mirage?

All citizens are entitled to access governmental information as, only if properly informed, that they can participate in politics. The right to information is enshrined in a number of international agreements, including Article 19 of the Universal Declaration of Human Rights. The right of citizens to access public authorities’ information is called Freedom of information. This information plays a crucial role in informing the population so that they can make accurate political choices. The mere disposal of information is not enough, as much of public documents suffer from complex language and other technicalities that can prevent an informed use of the information. Thus, sites displaying information must be adapted in order to guarantee usability, functionality and accessibility; there must be different levels of information, as there are diverse groups of individuals with different skills. Such usability, functionality and accessibility require designing effective communications, paying attention to balancing the complexity that citizens can manage and the complexity that those responsible for the requested information are producing.

Trading against disorderly liquidation of a large position under asymmetric information and market impact

We consider trading against a hedge fund or large trader that must liquidate a large position in a risky asset if the market price of the asset crosses a certain threshold. Liquidation occurs in a disorderly manner and negatively impacts the market price of the asset. We consider the perspective of small investors whose trades do not induce market impact and who possess different levels of information about the liquidation trigger mechanism and the market impact. We classify these market participants into three types: fully informed, partially informed and uninformed investors. We consider the portfolio optimization problems and compare the optimal trading and wealth processes for the three classes of investors theoretically and by numerical illustrations.

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

Can hospital dashboards provide visibility of information from bedside to board? A case study approach

PurposeThe purpose of this paper is to analyse hospital dashboards’ visibility of information at different management levels to improve quality and performance in an acute general hospital.Design/methodology/approachData were generated via 21 semi-structured interviews across different management levels.FindingsAll management levels had greater visibility of information, could make informed decisions, and registered performance improvement. Specifically, waiting time improved, however since introduction of hospital dashboards was work-in-progress at time of study, managers could not record improvement in terms of cost reductions, clinical effectiveness, patient safety and patient satisfaction. Different managerial levels had different visibility with top management having the greatest.Research limitations/implicationsIn single case studies, where only one context is used, the findings cannot be reproduced in different contexts; even though most of the results could be matched with the current literature.Practical implicationsThe need to have balanced key performance indicators that take into account other facets of improvements, apart from time, has been emphasised. Furthermore, if middle and departmental managers have greater visibility, this would allow them to work towards a strategic fit between the departments that they manage with the rest of the hospital.Originality/valueThere is scant literature regarding performance dashboards’ enhancement of visibility of information at different management levels. Furthermore, according to the authors’ knowledge, no other paper has tried to identify and discuss the different levels of information, which should be visible from bedside to board namely to management, clinicians and public.

Speed Perception In L2

Theories of speech perception provide a theoretical framework within which to conduct experiments and test hypotheses. These theories should not only show the relation and degree of integration between different levels of information but should incorporate mechanisms to deal with developmental change or the effects of the linguitic environment on speech perception. The relevance of these factors is particularly important when concerning the perceptual difficulties encountered in the acquisition of second languages. The present paper is a review of the maing current theories of L2 Phonetics which try to predict (and explain) the relative perceptual difficulty of non-native phonetic categories.

Exploiting NoSQL Graph Databases and in Memory Architectures for Extracting Graph Structural Data Summaries

NoSQL graph databases have been introduced in recent years for dealing with large collections of graph-based data. Scientific data and social networks are among the best examples of the dramatic increase of the use of such structures. NoSQL repositories allow the management of large amounts of data in order to store and query them. Such data are not structured with a predefined schema as relational databases could be. They are rather composed by nodes and relationships of a certain type. For instance, a node can represent a Person and a relationship Friendship. Retrieving the structure of the graph database is thus of great help to users, for example when they must know how to query the data or to identify relevant data sources for recommender systems. For this reason, this paper introduces methods to retrieve structural summaries. Such structural summaries are extracted at different levels of information from the NoSQL graph database. The expression of the mining queries is facilitated by the use of two frame-works: Fuzzy4S allowing to define fuzzy operators and operations with Scala; Cypherf allowing the use of fuzzy operators and operations in the declarative queries over NoSQL graph databases. We show that extracting such summaries can be impossible with the NoSQL query engines because of the data volume and the complexity of the task of automatic knowledge extraction. A novel method based on in memory architectures is thus introduced. This paper provides the definitions of the summaries with the methods to automatically extract them from NoSQL graph databases only and with the help of in-memory architectures. The benefit of our proposition is demonstrated by experimental results.