Search

In this paper, the clamping effect introduced by several diode-based configurations used for the implementation of the high-value resistors in quasi-floating-gate circuits is analyzed and characterized. In contrast to previous approaches where a parasitic diode is treated as a simple high-value resistor reducing the circuit complexity, in this case the analysis considers the diode behavior which leads to a clamping circuit. This clamping circuit introduces an unwanted amplitude-dependent offset voltage, which affects the performance moving the quiescent point at the quasi-floating-gate transistors. A new anti-parallel diode configuration for quasi-floating-gate applications is proposed in this work, which eliminates this unwanted offset voltage. The proposed design is validated using simulations and experimental data in a CMOS 0.35- μ m technology. [ABSTRACT FROM AUTHOR]

Paralinguistics is an essential component of verbal communication, comprising elements that provide additional information to the language, such as emotional signals. However, the subjective nature of perceiving affective aspects, such as emotions, poses a significant challenge to the development of quality resources for training recognition models of paralinguistic features. Labelers may have different opinions and perceive different emotions from others, making it difficult to achieve a diverse and sufficient representation of considered categories. In this study, we focused on the automatic classification of paralinguistic aspects in Spanish-speaking Mexican children of elementary school age. However, the dataset presents a strong imbalance in all labeled aspects and a low agreement between the labelers. Furthermore, the audio samples were too short, making it challenging to accurately classify affective speech. To address these challenges, we propose a novel method that combines data balancing algorithms and semisupervised learning to improve the classification performance of the trained models. Our method aims to mitigate the subjectivity involved in labeling paralinguistic data, thus advancing the development of robust and accurate recognition models of affective aspects in speech. • Emotional reactions of children to social robots analyzed deeply. • Insights into the challenge of subjectivity in speech annotation that affects recognition models. • A novel method that improves classification via semi-supervised learning in low inter-annotator agreement scenarios. • Implications of the findings to inform design and development of affective-aware systems. [ABSTRACT FROM AUTHOR]

The workload of Cloud data centers is constantly fluctuating causing imbalances across physical hosts that may lead to violations of service-level agreements. To mitigate workload imbalances, this work proposes a concurrent agent-based problem-solving technique supported by cooperative game theory capable of balancing workloads by means of live migration of virtual machines (VMs). Nearby agents managing physical hosts are partitioned into coalitions in which agents play coalitional games to progressively balance separate sections of a data center while considering the coalition's benefit of migrating a VM as well as its associated network overhead. Simulation results show that, in general, the proposed coalition-based load balancing mechanism outperformed a load balancing mechanism based on a hill-climbing algorithm used by a top data center vendor when considering altogether (i) the standard deviation of resource usage, (ii) the number of migrations, and (iii) the number of switch hops per migration. • Agents in coalitions progressively balance data center sections. • Supported by a game-theoretical framework, agents cooperate to balance workloads. • Coalitions composed of nearby hosts are used as a basis to balance workloads. • Coalition-aware heuristics consider both the benefit of migrating a VM and its cost. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Bodyguard Allocation Problem (BAP) game, that illustrates the behavior of processes with contradictory individual goals in distributed systems. In particular, the game deals with the conflict of interest between two classes of processes that maximize/minimize their distance to a special process called the root. A solution of the BAP game represents a rooted spanning tree in which there exists a condition of equilibrium with maximum social welfare. We analyze the inefficiency of equilibria of the game based on both a completely cooperative and noncooperative approach. Additionally, we design two algorithms, CBAP and DBAP, that provide approximated solutions for the BAP game. We prove that both algorithms always terminate in a configuration with equilibrium and we analyze their running time based on the approach of cooperation used. We perform experimental simulations to compare the overall quality of equilibria obtained by the proposed algorithms. [ABSTRACT FROM AUTHOR]

The performance of processors in a distributed system can be measured by parameters such as bandwidth, storage capacity, work capability, reliability, power limitations, years of usage, among others. Each processor defines its preference based on these parameters. The preference represents an indicator of the quality of service that a processor can provide. An algorithm that follows a preference-based approach uses the preference of the processors to make decisions. In this paper we introduce a randomized self-stabilizing leader election algorithm for preference-based anonymous trees. Our algorithm assures that the processor with the highest preference in the system is always selected as the leader; moreover, it is able to solve symmetric configurations where each preference is the same. We prove that our algorithm has an optimal average time complexity and we also performed simulations to illustrate the average performance of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper we present a simulation of an acyclic n×n DR-Mesh on an n×n LR-Mesh. The simulation is efficient in regards to size since both models use the same number of processors. The worst execution time for this simulation is O(n2) time, but we demonstrate that its average execution time is O(logn). The fastest existing simulation takes O(logn) time, but it uses an extremely large number of processors. On the other hand, the most efficient simulation in terms of size takes O(log2n) time with O(n4/log2n) processors. Both of the existing simulations are for the unrestricted DR-Mesh. This paper provides an important step towards efficiently simulating the unrestricted DR-Mesh on weaker models such as the R-Mesh and the LR-Mesh. [ABSTRACT FROM AUTHOR]

Abstract: Traditional software teams that implement agile methodologies frequently experience difficulty working efficiently as agile teams. While agile methodologies support and promote social activities and behaviors, the understanding of the social phenomena and challenges that arise during the transformation of traditional teams into agile teams is limited. We conducted an extensive 10‐month case study in a software enterprise to analyze the changes in the communication and social interactions of employees during the transformation process, and to delineate the social challenges that arise during this process. Using social interaction registers, we collected data on the communication and social interactions of employees, first when they worked as a traditional team and then as an agile team. Then, we processed the data using social network analysis methods to identify the differences between these 2 types of teams and to create graphs of communication and social interaction networks. We identified that the transformation process influences formal, informal, oral, and written communication and increases conjunctive and disjunctive social interactions. Further, from the results of interviews conducted with all the enterprise members, we identified 5 social challenges to transforming a traditional team into an agile one. The results of this research suggest that the transformation process requires broad changes in the relationships and interactions among employees. [ABSTRACT FROM AUTHOR]