Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Opinion Dynamics under Bounded Confidence: A Survey

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al. in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines such as physics, mathematics, computer science, social psychology and philosophy. In these models agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master equation style. The contribution of this survey is fourfold. First, it will present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds o.
Triangles to Capture Social Cohesion

Although community detection has drawn tremendous amount of attention across the sciences in the past decades, no formal consensus has been reached on the very nature of what qualifies a community as such. In this article we take an orthogonal approach by introducing a novel point of view to the problem of overlapping communities. Instead of quantifying the quality of a set of communities, we choose to focus on the intrinsic community-ness of one given set of nodes. To do so, we propose a general metric on graphs, the cohesion, based on counting triangles and inspired by well established sociological considerations. The model has been validated through a large-scale online experiment called Fellows in which users were able to compute their social groups on Face book and rate the quality of the obtained groups. By observing those ratings in relation to the cohesion we assess that the cohesion is a strong indicator of users subjective perception of the community-ness of a set of people.
Prospective of Fifth Generation Mobile Communications

This paper explores future mobile systems with emphasis on re-configurability based on cognitive and software defined radios. 5G (Fifth Generation) network architecture consisting of reconfigurable multitechnology core and a single fully reconfigurable terminal able to autonomously operate in different heterogeneous access networks is proposed. The proposed network is enforced by nanotechnology, cloud computing and based on All IP Platform. The paper highlights 5G main development challenges and illustrates why there is a need for 5G. It also reviews in brief the evolution of wireless and cellular systems focusing on four main key factors: radio access, data rates, bandwidth and switching schemes in addition to change in network architecture. The 3G transitional cellular and wireless systems toward 4G and the true 4G IMT-advanced systems are thoroughly presented.
Fast '13: 11th Usenix Conference on File and Storage Technologies Opening Remarks File Systems Ffsck: the Fast File System Checker

Keith Smith began FAST '13 by telling us that 127 papers were submitted and 24 accepted, and that the attendance had almost reached the same level as 2012, which was the record year for attendance. There were 20 full-length papers, four short ones, nine with just academic authors, five industry-only authors and 10 collaborations. Smith said he enjoyed having people from academia and industry in the same room talking. FAST is a systems conference, and the top topics in submitted papers were those tagged with file-system design and solid state storage. Cloud storage has increased over time, as has caching, while file-system architectures have actually been decreasing. There were more than 500 paper reviews, totaling more than 350,000 words of commentary (about the length of the book David Copperfield).
Up and out: scaling formal analysis using model-based development and architecture modeling

Systems are naturally constructed in hierarchies in which design choices made at higher levels of abstraction ``flow down'' to requirements on system components at lower levels of abstraction. Thus, whether an aspect of the system is a design choice or a requirement depends largely on one's vantage point within the hierarchy of system components. Furthermore, systems are often constructed middle-out rather than top-down; compatibility with existing systems and architectures, or availability of specific components influences high-level requirements. We believe that requirements and architectural design should be more closely aligned: that requirements models must account for hierarchical system construction, and that architectural design notations must better support specification of requirements for system components. In this presentation, I describe tools supporting iterative development of architecture and verification based on software models. We represent the hierarchical composition of the system in the Architecture Analysis & Design Language (AADL), and use an extension to the AADL language to describe requirements at different levels of abstraction for compositional verification. To describe and verify component-level behavior, we use Simulink and Stateflow and multiple analysis tools.
Simple and efficient algorithm for distributed compressed sensing

In this paper we propose a new iterative thresholding algorithm for distributed compressed sensing (CS) based on a set of local cost functions referred as HALS-CS algorithm (compare with). This algorithm allows reconstructing all sources simultaneously by processing row by row of the compressed signals. Moreover, with an adaptive nonlinearly decreasing thresholding strategy, we are able to reconstruct almost perfectly sources for ill-conditioned and ill-posed problems, for example in difficult cases when the number of compressed samples is lower than four times of the number of nonzero coefficients in the signals. The extensive experimental results confirm the validity and high performance of the developed algorithm.
A dynamic multilayer shallow water model for polydisperse sedimentation

A multilayer shallow water approach for the approximate description of polydisperse sedimentation in a viscous fluid is presented. The fluid is assumed to carry finely dispersed solid particles that belong to a finite number of species that differ in density and size. These species segregate and form areas of different composition. In addition, the settling of particles influences the motion of the ambient fluid. A distinct feature of the new approach is the particular definition of the average velocity of the mixture. It takes into account the densities of the solid particles and the fluid and allows us to recover the global mass conservation and linear momentum balance laws of the mixture. This definition motivates a modification of the Masliyah–Lockett–Bassoon (MLB) settling velocities of each species. The multilayer shallow water model allows one to determine the spatial distribution of the solid particles, the velocity field, and the evolution of the free surface of the mixture. The final model can be written as a multilayer model with variable density where the unknowns are the average velocities and concentrations in each layer, the transfer terms across each interface, and the total mass. An explicit formula of the transfer terms leads to a reduced form of the system. Finally, an explicit bound of the minimum and maximum eigenvalues of the transport matrix of the system is utilized to design a Harten–Lax–van Leer (HLL)-type path-conservative numerical method. Numerical simulations illustrate the coupled polydisperse sedimentation and flow fields in various scenarios, including sedimentation in a type of basin that is used in practice in mining industry and in a basin whose bottom topography gives rise to recirculations of the fluid and high solids concentrations.
On Different Sides: Investigating the Persuasive Effects of Anger Expression in Political News Messages

Anger expression is increasingly prevalent in political news messages. However, the persuasive effects of expressing anger in a political context have received scant attention from researchers. We conducted two experiments to investigate the hypothesis that anger expression is detrimental to persuasion because it runs counter to well-established social norms for the polite expression of opinions. We created political news messages including a persuasive appeal by a politician that was supported either with an expression of anger or with an expression of nonemotional disagreement. The results of Experiment 1 (N = 120) showed that anger messages were perceived as less appropriate than control messages, and that politicians expressing anger were perceived as less likable and less competent than politicians who disagreed in nonemotional terms. In Experiment 2 (N = 1,005), the negative effects of anger expression on perceived likability and competence were replicated. Also in line with Experiment 1, anger messages were perceived as less appropriate, but this time only for those with negative a priori attitudes toward the advocated position. In contrast, those with positive a priori positions toward the advocated position perceived anger messages as more appropriate than the control messages.
Diversity in Dynamic Class Prediction

Instead of using the same ensemble for all data instances, recent studies have focused on dynamic ensembles in which a new ensemble is chosen from a pool of classifiers for each new data instance. Classifiers agreement in the region where a new data instance resides in has been considered as a major factor in dynamic ensembles. We postulate that the classifiers chosen for a dynamic ensemble should behave similarly in the region in which the new instance resides, but differently outside of this area. In other words, we hypothesize that high local accuracy, combined with high diversity in other regions, is desirable. To verify the validity of this hypothesis we propose two approaches. The first approach focuses on finding the k-nearest data instances to the new instance, which then defines a neighborhood, and maximizes simultaneously local accuracy and distant diversity, based on data instances outside of the neighborhood. The second method makes use of an alternative definition of the neighborhood: all data instances are in the neighborhood. However, the importance of data instances for accuracy and diversity depends on the distance to the new instance. We demonstrate through several experiments that the distance-based diversity and accuracy outperform all benchmark methods.
ALPHA-CLUSTER FORMATION FACTOR IN ( n , α ) REACTION CROSS SECTIONS

Alpha particle clustering in nuclei is a one of important subjects for understanding of α-decay, α-particle emission reactions and nuclear structure [1]. The alpha clustering problem was considered by many authors who used different theoretical approaches. Bonnetti and Milazzo-Colli suggested [2] the preformed alpha particle model which was used in analysis of experimental data for alpha decay, (n,α) and (p,α) reactions. From this analysis the alpha particle preformation factor was found to be around фα ≈ 0.7 – 0.01 for favoured α-transitions. Kadmensky and Furman developed the α-cluster model [3] and obtained the surface αclustering probability 7·10 –4 , 3·10 –5 and 8·10 –7 for favoured, semifavoured and unfavoured αtransitions, respectively. It is seen that a discrepancy between these two approaches to α-clustering is estimated from ~10 to 10 3 times for favoured alpha transitions. Tonozuka and Arima obtained a ratio of the calculated reduced α-width to the observed value and considered [4] surface α-clustering of α-decays for 212 Po. It was shown that the surface α-clustering effect produces a tremendous enhancement of the α-decay widths of 212 Po. Hogan calculated [5] the rate of nucleonnucleon and nucleon-alpha interactions in nuclear matter. He found the alpha-particle preformation factors and number of alpha clusters in several complex nuclei. Zhang, Royer and Li using the semiclassical approach for frequency of the α-cluster motion inside mother nuclei obtained [6,7] the α-particle preformation probability for α-decay of some heavy nuclei to be around 0.0065 ÷ 0.244, which are close to the Bonetti and Milazzo-Colli results. It is seen from here that the authors used different assumptions and essential discrepancies between their results there are. So, from the unified view point more detailed study of the αclustering problem is needed. In this work we in the framework of the statistical model carried out a systematical analysis of fast neutron induced (n,α) reaction cross sections and found the α-clustering factor normalizing the theoretical cross sections to experimental data. Also, the α-clustering factors were obtained for slow neutron induced (n,α) reactions using the different approaches to this problem. Our results are compared with other values of α-clustering factor.
Optimum Decoding Performance on the Basis of Multithreshold Algorithms

: The iterative majority improved decoders are described. They are called multithreshold decoders (MTD). These decoders have a property of convergence to the solution of the optimum decoder with keeping linear complexity of implementation, which one is a property of usual threshold procedures. Different decoding applications are discussed. Experimental results are submitted. Decoder for non-binary MTD considered also.
Occurrence of Biosurfactant Producing Bacillus spp. in Diverse Habitats

Diversity among biosurfactant producing Bacillus spp. from diverse habitats was studied among 77 isolates. Cluster analysis based on phenotypic characteristics using unweighted pair-group method with arithmetic averages (UPGMAs) method was performed. Bacillus isolates possessing high surface tension activity and five reference strains were subjected to amplified 16S rDNA restriction analysis (ARDRA). A correlation between the phenotypic and genotypic characterization of Bacillus spp. is explored. Most of the oil reservoir isolates showing high surface activity clustered with B. licheniformis and B. subtilis, the hot water spring isolates clustered in two ingroups, while the petroleum contaminated soil isolates were randomly distributed in all the three ingroups. Present work revealed that diversity exists in distribution of Bacillus spp. from thermal and hydrocarbon containing habitats where majority of organisms belonged to B. licheniformis and B. subtilis group. Isolate B. licheniformis TT42 produced biosurfactant which reduced the surface tension of water from 72 mNm−1 to 28 mNm−1, and 0.05 mNm−1 interfacial tension against crude oil at 80°C. This isolate clustered with B. subtilis and B. licheniformis group on the basis of ARDRA. These findings increase the possibility of exploiting the Bacillus spp. from different habitats and their possible use in oil recovery.
Doubly-expedited one-step Byzantine consensus

It is known that Byzantine consensus algorithms guarantee one-step decision only in favorable situations (e.g. when all processes propose the same value) and no one-step algorithm can support two-step decision. This paper presents DEX, a novel one-step Byzantine algorithm that circumvents these impossibilities using the condition-based approach. Algorithm DEX has two distinguished features: Adaptiveness and Double-expedition property. Adaptiveness makes it sensitive to only actual number of failures so that it provides fast termination for more number of inputs when there are fewer failures (a common case in practice). The double-expedition property facilitates two-step decision in addition to one-step decision by running two condition-based mechanisms in parallel. To the best of our knowledge, double-expedition property is the new concept introduced by this paper, and DEX is the first algorithm having such a feature. Although DEX takes four steps at worst in well-behaved runs while existing one-step algorithms take only three, it is expected to work efficiently because the worst-case does not occur so often in practice.
Dealing with Noisy Data on Point Cloud Models

Most of the studies working on point cloud data focused on complete and clean data (even though some of them took missing values into account), while in practice we often have to deal with incomplete and unclean data, just as there might be missing values and noise in data. We study noise handling, and we put our focus on processing a noisy point cloud of a visual object or a 3D model. We propose an approach where we first identify data points that might be noise and then lower the impact of the noisy values. To identify noise, we use supervised learning on data whose features are density and distance. To lower the impact of the noisy values, we use triangular surfaces and projection. The experimental results show the effectiveness of the proposed approach. Our contributions are as follows: First, we show how machine learning can help computer graphics. Second, we propose to use distance and density as features in learning for noise identification. Third, we propose to use triangular surfaces and projection to save execution time in noise reduction. Fourth, the proposed approach could be used to improve 3D scanning.
Improving Semantic Search Using Query Log Analysis

Despite the attention Semantic Search is continuously gaining, several challenges affecting tool performance and user experience remain unsolved. Among these are: matching user terms with the searchspace, adopting view-based interfaces in the Open Web as well as supporting users while building their queries. This paper proposes an approach to move a step forward towards tackling these challenges by creating models of usage of Linked Data concepts and properties extracted from semantic query logs as a source of collaborative knowledge. We use two sets of query logs from the USEWOD workshops to create our models and show the potential of using them in the mentioned areas.
The Effectiveness of Mindfulness Training on Quality of Perceptual Marital Relationship and Psychological Well-being of Women with Addicted Wife

Objective: The aim of the research was to investigate the effect of Mindfulness Training on Quality of Perceptual Marital Relationship and Psychological Well-being of Women with Addicted Wife.Methods: The research method was quasi-experimental with pretest- posttest and 3 months follow up, with a control group. Among the population of Women with Addicted Wife, twenty Women with Addicted Wife (10 persons in experimental group, 10 persons in control group) were selected by purposeful sampling method and were assigned in experimental and control groups. The experimental group received 8 session (2 hours for each) of positive thinking training. For data collection, Fletcher Perceived Quality of Marital Relationship Scale and Ryff Psychological Well-Being Questionnaire were used.Results: Multivariate analysis of covariance (MANCOVA) was used for analyzing the data. The results showed the Mindfulness Training was effective on the quality of marital relationship and psychological well-being among Women with Addicted Wife. Also, the training was effective in a 3 months follow up.Conclusion: Therefore, Mindfulness Training skills can be used in prevention actions.
Offender and family member perceptions after an offender-focused hot spots policing strategy

Purpose The purpose of this paper is to examine how both offenders and their families perceived their interactions with police and whether there were negative consequences of the offender-focused strategy that was implemented in a hot spots policing experiment. Design/methodology/approach Data from interviews of 32 offenders and 29 family members are examined qualitatively for themes to evaluate how the strategy was carried out and how it impacted offenders’ behavior and both groups’ perceptions of the police detectives and the strategy overall. Findings The results show that there was overwhelming agreement by both offenders and their family members that the police detectives who contacted them treated both groups with dignity and respect. After the contact was over, the offenders appeared to commit less crime, followed probation more closely, and had positive feelings about what the police detectives were trying to do. Improvement of the offenders’ relationships with their families was an unanticipated finding indicating a diffusion of benefits of the strategy. Practical implications The results suggest that when procedural justice principles are used in an offender-focused police intervention, positive impact can be achieved without negative consequences. Originality/value This is a rare example of an in-depth evaluation of the perceptions of offenders and family members contacted through a hot spots policing offender-focused strategy.
An automatic health surveillance chart interpretation system based on Indonesian language

The Indonesian communities have the difficulties for interpreting the chart image, especially the health surveillance chart image. This condition is impacted on late awareness of disease threat. In Indonesia, the chart interpreter system based on Indonesian language is available, but it still has the limitation in generating the textual representation (i.e the health surveillance summaries) in the case of the user involvement. This paper proposed the automatic chart interpreter system as the Natural Language Generation (NLG) system adopting Data-to-Text architecture for overcoming the intended problem. In other words, it emphasizes on the automation system, where the automation system refers to the ability of system for determining the related events and the inference of the related events as well as to generating the related summaries without requiring the manual input from the users. Based on the experiment result, the proposed system (HS-CISys) can perform the automation with involving Data Interpretation and Micro Planning. Therefore, Data Interpretation and Micro Planning ensure that the proposed system can generate the health surveillance summaries fully automatic without relying on the manual input from the users.
Performance analysis of RF-pilot phase noise compensation techniques in coherent optical OFDM systems

In coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems, phase noise has an important impact on the system performance. They are highly sensitive to phase noise induced by transmitter laser and local oscillator laser. We explain different kinds of phase noise effects on the OFDM signals and analyze the phase noise effect induced by transmitter laser and local oscillator. We analyze the standard RF-pilot (RFP) phase noise compensation method and explain its drawbacks. We provide two extended RFP schemes that overcome the drawbacks of the standard RFP scheme and enhance the performance of CO-OFDM systems. Furthermore, the phase noise effects for various laser linewidths and the effect of the received optical power (ROP) on the system performance are quantified. A CO-OFDM transmission system with 16-QAM is simulated with VPItransmissionMaker™ V8.5.