Miracle-dz/UniFAR-FTUs huggingface dataset:
A Trust-based Information Propagation Model in Online Social Networks

As a new media, social network gains a wide range of social influence, and the social network based e-commerce becomes more and more popular, therefor, the study of information propagation is great of significance. In this paper, we will presents a trust based information propagation model, just as the trust between people in real life. First, employ the algorithm of data mining on personal information to make the classification of the user. And then calculate the value of trust between the users according their relationships. At last, this paper predicts the range of information propagation by using the trust value between the users and messages. The last part of the paper is the certification of the model by using an actual example.
On Using On-Chip Clock Tuning Elements to Address Delay Degradation Due to Circuit Aging

Lifetime performance of digital integrated circuits degrades as a consequence of circuit aging. In the past few years, there has been extensive research to reduce the impact of aging by different design techniques, or to predict the degradation and adapt the circuit accordingly. In this paper, we explore a novel perspective to this problem by exploiting the presence of clock tuning elements in high-performance designs. By combining on-chip sensors to predict setup or hold-time violations with the clock tuning elements, we provide an effective self-tuning mechanism for each circuit sample. The proposed method can operate in-system to prolong the circuit's maximum performance in its unique operating environment.
Low-Jitter Multi-phase Clock Generation: A Comparison between DLLs and Shift Registers

This paper shows that, for a given power budget, a shift register based multi-phase clock generator (MPCG) generates less jitter than a delay-locked loop (DLL) equivalent when both are realized with current mode logic (CML) circuits and white noise is assumed. This is due to the factor that the shift register MPCG has no jitter accumulation from one clock phase to the other as in the DLL based MPCG. For N-phase clock generation, the shift register MPCG needs a reference clock with N times higher frequency and thus requires a VCO with higher frequency than the DLL counterpart. However, we can show that this does not lead to additional power consumption.
Modelling Conflict : Knowledge Extraction using Bayesian Neural Network and Neuro-fuzzy Models

Much has been written about the lack of transparency of computational intelligence models. This paper investigates the level of transparency of the Takagi-Sugeno neuro-fuzzy model and the Neural Network model by applying them to conflict management, an application which is concerned with causal interpretations of results. The neural network model is trained using the Bayesian framework. It is found that the neural network is able to forecast conflict with an accuracy of 77.30%. Knowledge from the neural network model is then extracted using the Automatic Relevance Determination method and by performing a sensitivity analyis. The Takagi-Sugeno Neuro-fuzzy model is optimised to forecast conflict giving an accuracy 80.36%. Knowledge from the Takagi-Sugeno neuro-fuzzy model is extracted by interpreting the model’s fuzzy rules and their outcomes. It is found that both models offer some transparency which helps in understanding conflict management.
Performability/Energy Tradeoff in Error-Control Schemes for On-Chip Networks

High reliability against noise, high performance, and low energy consumption are key objectives in the design of on-chip networks. Recently some researchers have considered the impact of various error-control schemes on these objectives and on the tradeoff between them. In all these works performance and reliability are measured separately. However, we will argue in this paper that the use of error-control schemes in on-chip networks results in degradable systems, hence, performance and reliability must be measured jointly using a unified measure, i.e., performability. Based on the traditional concept of performability, we provide a definition for the ¿Interconnect Performability¿. Analytical models are developed for interconnect performability and expected energy consumption. A detailed comparative analysis of the error-control schemes using the performability analytical models and SPICE simulations is provided taking into consideration voltage swing variations (used to reduce interconnect energy consumption) and variations in wire length. Furthermore, the impact of noise power and time constraint on the effectiveness of error-control schemes are analyzed.
Probabilistic, context-sensitive, and goal-oriented service selection

In this paper we propose a novel approach and a platform for dynamic service selection in composite Web services. The problem we try to solve is that of selecting, for each composite service execution and for each step in the execution, the service that maximizes the probability of reaching a user-defined goal. We first underline the limitations of a <i>priori</i> approaches based on having each service provider declare non-functional parameters and on trying to select services based on some utility functions over these parameters. Then, we propose an approach that overcomes these limitations by tackling the problem a posteriori: we analyze past executions of the composite service and build, using data mining techniques, a set of context-sensitive service selection models to be applied at each stage in the composite service execution. We show the architecture of a prototype that implements this approach and we discuss its benefits over the a priori approach.
Cognition in pregnancy and motherhood: prospective cohort study

Background Research has reported that pregnant women and mothers become forgetful. However, in these studies, women are not recruited prior to pregnancy, samples are not representative and studies are underpowered. Aims The current study sought to determine whether pregnancy and motherhood are associated with brief or long-term cognitive deterioration using a representative sample and measuring cognition during and before the onset of pregnancy and motherhood. Method Women aged 20–24 years were recruited prospectively and assessed in 1999, 2003 and 2007. Seventy-six women were pregnant at follow-up assessments, 188 became mothers between study waves and 542 remained nulliparous. Results No significant differences in cognitive change were found as a function of pregnancy or motherhood, although late pregnancy was associated with deterioration on one of four tests of memory and cognition. Conclusions The hypothesis that pregnancy and motherhood are associated with persistent cognitive deterioration was not supported. Previous negative findings may be a result of biased sampling.
A comparison of Social Support and Mood between Team and Individual athletes during their transition to university competitive sport.

ii CHAPTER ONE: INTRODUCTION 1.0 INTRODUCTION. 1 1.1 Background. 1 1.2 Aims. 3 CHAPTER TWO: LITERATURE REVIEW 2.0 LITERATURE REVIEW. 4 2.1 Mood. 4 2.2 Transitional Process. 7 2.21 Transition to University. 8 2.3 Social Support. 9 CHAPTER THREE: METHODOLOGY 3.0 METHODOLOGY. 13 3.1 Participants. 13 3.2 Measures. 13 3.3 Interview Guide. 14 3.4 Interview Procedure. 15 3.5 Questionnaire Procedure. 16 3.6 Data Analysis. 16 CHAPTER FOUR: RESULTS 4.0 RESULTS. 18 4.1 Profile of Mood States. 18 4.2 Social Support Questionnaire 6. 19 4.21 Total Social Support Scores. 19 CHAPTER FIVE: DISCUSSION 5.0 DISCUSSION. 21 5.1 Profile of Mood States. 21 5.2 Social Support Questionnaire 6. 23 5.3 Interviews. 24 5.4 Importance of Social Support. 25 5.5 Utilisation of Functional Social Support. 28 5.51 Informational Support. 29 5.52 Tangible Support. 30 5.53 Emotional Support. 30 5.54 Esteem Support. 31 5.6 Changes in Social Support Patterns as a result of the Transition to University.32 5.61 Independence and Freedom. 32 5.62 Commitment. 32 5.7 Practical Implications. 34 5.8 Strengths and Limitations. 34 5.9 Future Recommendations. 35 CHAPTER SIX: CONCLUSION 6.0 CONCLUSION. 38 CHAPTER SEVEN: REFERENCES REFERENCES. 39 CHAPTER EIGHT: APPENDICES APPENDICES. APPENDIX AEthics Status. A-1 APPENDIX BInformed Consent Form. B-1 APPENDIX CParticipant Information Sheet. C-1 APPENDIX DInterview Guide. D-1 APPENDIX ESample Transcript. E-1 APPENDIX FProfile of Mood States Questionnaire. F-1 APPENDIX GSocial Support Questionnaire 6. G-1
Control States and Motivated Agency

One of the challenges faced by researchers in the behaviour modelling of life-like characters is the need to develop a systematic framework in which to ask questions about the types of internal state life-like characters might possess, and how those different states interact. We propose a solution based on a cognitively inspired multi-layered agent architecture (composed of reactive, deliberative and meta-management layers), and a recursive “design-based” research methodology – wherein each new design gradually increases our explanatory power and allows us to account for more and more of the phenomena of interest. By describing a variety of “broad but shallow” complete agents at the information-level, and showing how these designs realise mental states and processes, we aim to provide a rich and deep explanatory framework from which to explore motivated autonomous agency. Early experiments have concentrated on: (a) the requirements of goal-processing; (b) the emergence of perturbant (emotional) states; and (c) the relationship between motives, goals, emotions, and personality.
Empirical Study of Using Big Data for Business Process Improvement at Private Manufacturing Firm in Cloud Computing

The implementations of new technologies have been broadly accepted by multiple industries in recent years, such as big data nad cloud computing. A quick and efficient data mining has become an alternative of creating values ever, the dynamic economic context and continuous changing business envoronment have driven numerous demands and applications in various industries. This phenomenon results in the problem of forming proper strategies in applying big data and cloud computing, which is one of the major challenges of reach the goal of value creations for current enterprises. This paper focuses on this problem and presents an empirical study on the issue of using big data for business process improvements in cloud computing. The investigation target is a Chinese large-size private enterprise that strives to be a global enterpriise in the manufacturing industry. The completed research is based on the real data collected from the collaboration partner. The main findings of this research include two parts:1) the efforts of using big data are varied, which are relatedto the operation levels,2) implementating cloud computing solutions is at an exploring stage for Chinese provate sector due to a few restrictions.
On the Duality of Resource Leases and Jobs

Today’s batch computing systems derive from a job scheduling paradigm. In contrast, utility service environments assign measured quantities of resources to long-running customer activities according to policies for resource matching and allocation. In this paper we explore the duality between job scheduling and utility resource management using a leasing abstraction that generalizes reservations. We suggest that advanced job management systems will increasingly incorporate elements of utility systems to support new capabilities for urgent computing, predictable execution of large composite workflows, manageability and customization, and technology trends such as server virtualization. We show that similar resource sharing policies may be implemented using either abstraction, and explore a design space of hybrid systems that combine leases and job scheduling to balance various objectives. Experiments compare and characterize representative models from the design space in terms of job scheduling and resource management.
Document Clustering using Linear Partitioning and Reallocation using EM Algorithm

Document clustering is a subset of the larger field of data clustering, which borrows concepts from the fields of information retrieval (IR), natural language processing (NLP), and machine learning (ML), there exist a wide variety of unsupervised clustering algorithms. In this paper presents a novel algorithm for document clustering based with an enhancement on the features of the existing algorithms. This paper illustrates the Principal Direction Divisive Partitioning (PDDP) algorithm and describes its drawbacks and introduces a combinatorial framework of the PDDP algorithm and then describes the simplified version of the EM algorithm called the spherical Gaussian EM (sGEM) algorithm. The PDDP algorithm recursively splits the data samples into two sub -clusters using the hyper plane normal to the principal direction derived from the covariance matrix, which is the central logic of the algorithm. However, the PDDP algorithm can yield poor results, especially when clusters are not well separated from one another. To improve the quality of the clustering results problem, it is resolved by reallocating new cluster membership using the sGEM algorithm with different settings. Furthermore, based on the theoretical background of the sGEM algorithm, it can be obvious to extend the framework to cover the problem of estimating the number of clusters using the Bayesian Information Criterion. Experimental results are given to show the effectiveness of the proposed algorithm with comparison to the existing algorithm.
Direct numerical simulation of the thermal effects in plasma/turbulence interaction

The thermal effects of laser–induced plasma on isotropic turbulence are studied using direct numerical simulation. Laminar and turbulent simulations are conducted. For the laminar simulations, a tear–drop shaped blast wave propagates into the background becoming spherical in time. Shock radius and jumps in fluid properties at the shock front are compared to experiment. Both short and long time behavior of the plasma is studied. At short times, baroclinic vorticity is generated as a consequence of propagation of the curved shock through the domain. At long times, the flow field forms toroidal vortex rings, as observed in experiments. For the turbulent simulations, turbulence levels get enhanced in regions of compression across the blast wave and suppressed in regions of expansion in the plasma core.
An Empirical Investigation into the Adoption of Green Procurement Practices in the German Food Service Industry

This paper analyzes the relationship between sustainability views, drivers and barriers and concrete procurement practices for sustainability in the German food service industry (FSI), building on earlier work of Rankin et al. (2011) on this topic. Further innovation lies in the explicit regard to product-specific differences in the importance and level of implementation of sustainable sourcing, as well as in the special focus on the FSI, a sector with significant economic importance, for which only small amounts of information can be found. For this study, qualitative interviews among head chefs and quantitative standardized surveys among decision makers from the FSI were conducted.
Bayesian sparse regularization in near-field wideband aeroacoustic imaging for wind tunnel test

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Bayesian sparse regularization in near-field wideband aeroacoustic imaging for wind tunnel test Ning Chu, José Picheral, Ali Mohammad-Djafari
PAPR Reduction in OFDM using Active and Non-Active Channels

According to the request of advance communication field there should be high data rate in addition to both power efficiency and lower bit error rate. This request of high data rate can be achieved by the multi carrier modulation scheme using the OFDM technique. But the great drawback of the OFDM technique is its high peak to average power ratio (PAPR). High PAPR reduces OFDM signals by driving the analog amplifier to work in the nonlinear region, changing this way the signal and making the amplifier to consume more power. To reduce the PAPR methods exist which adjust or present new signals to battle large signal peaks. The methods which use data carrying channels are called active channel methods and which use redundant channel are called non active channel methods. This work deals with reduction of the PAPR of OFDM signal using both Active and Non Active Channels. Clipping technique has been applied to active channels and Tone reservation has been applied to Non Active Channels. By using both channels we can get considerable reduction in PAPR.
University of Pennsylvania Working Papers in Linguistics English Auxiliary Realization and the Independence of Morphology and Phonetics English Auxiliary Realization and the Independence of Morphology and Phonetics English Auxiliary Realization and the Independence of Morphology and Phonetics

One of the key questions in the study of language concerns the architecture of the grammar. At issue is the nature of and relationship between the systems that generate linguistic output. The present paper tests the predictions of two competing theories of grammatical architecture for a variable process of h-deletion in connected speech. A usage-based theory of grammar predicts that this phonetic lenition rule will show word-sensitivity, while a modular, feed-forward theory predicts a uniform rate of h-deletion across lexical items. Data from the Switchboard corpus supports the latter prediction.
Modelling the influence of self-heating on characteristics of IGBTs

The paper refers to modelling characteristics of IGBT in SPICE software with self-heating taken into account. The form of the electrothermal model of the considered transistor and equations describing this model are proposed. The correctness of the proposed model is verified experimentally during the operation of the examined transistor at different cooling conditions. Particular attention is paid to the non-typical course of characteristics of the considered device at weak excitation and the shape of the obtained characteristics is discussed.
Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors

As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Using abstract feature vectors, that can generated on any subscription based company's user event logs, the paper proves that through the use of the intrinsic property of Deep Neural Networks (learning secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance. Furthermore the research documented in the paper was performed for Framed Data (a company that sells churn prediction as a service for other companies) in conjunction with the Data Science Institute at Lancaster University, UK. This paper is the intellectual property of Framed Data.
Type I Interferon Impairs Specific Antibody Responses Early during Establishment of LCMV Infection

Elicitation of type I interferon (IFN-I) has been shown to both enhance and impair cell-mediated immune responses in acute and persistent viral infections, respectively. Here, we show that, in addition to its effect on T cells, IFN-I drives impairment of specific antibody responses through interaction with B cells in the acute phase of lymphocytic choriomeningitis virus (LCMV) infection. This impairment was limited to the T cell-dependent B cell response and was associated with disruption of B cell follicles, development of hypergammaglobulinemia (HGG), and expansion of the T follicular helper cell population. Antigen-specific antibody responses were restored by ablation of IFN-I signaling through antibody-mediated IFN-I receptor blockade and B cell-specific IFN-I receptor knockout. Importantly, IFN-I receptor deficiency in B cells also accelerated the development of LCMV neutralizing antibodies and alleviated HGG. These results provide a potential therapeutic target toward efficient treatment measures that limit immunopathology in persistent viral infections.
Quick Linguistic Representations and Precise Perceptual Representations: Language Statistics and Perceptual Simulations under Time Constraints

Many studies have argued that language comprehension requires perceptual simulation. In previous work we have demonstrated that because language encodes perceptual relations, comprehenders can also rely on language statistics to bootstrap meaning through limited grounding. The extent comprehenders do this depends on the nature of the cognitive task, the stimulus, the individual, as well as the speed of processing, with linguistic representations preceding perceptual simulation. In the current study we report results that investigated whether time constraints impacted the use of perceptual and linguistic factors during language processing. Participants made fast or slow speeded judgments about whether pairs of words were semantically related. Subjects were also instructed to either respond as quickly as possible to the words they were presented, or respond as accurately as possible. The perceptual factor was operationalized as an iconicity rating of the stimulus pairs occurring in a particular orientation in the real world and the linguistic factor was operationalized as the frequency of the stimulus pairs in language. The linguistic factor best explained the RTs when subjects had to respond quickly. On the other hand, when given more time to respond, both linguistic and perceptual factors explained response times. These findings support the view that language processing is both linguistic and embodied, with linguistic representations being relevant for quick good-enough representations and perceptual simulations being important for more precise information.
DO SEMIDEFINITE RELAXATIONS SOLVE SPARSE PCA UP TO THE INFORMATION LIMIT

Estimating the leading principal components of data, assuming they are sparse, is a central task in modern high-dimensional statistics. Many algorithms were developed for this sparse PCA problem, from simple diagonal thresholding to sophisticated semidefinite programming (SDP) methods. A key theoretical question is under what conditions can such algorithms recover the sparse principal components? We study this question for a single-spike model with an $\ell_0$-sparse eigenvector, in the asymptotic regime as dimension $p$ and sample size $n$ both tend to infinity. Amini and Wainwright [Ann. Statist. 37 (2009) 2877-2921] proved that for sparsity levels $k\geq\Omega(n/\log p)$, no algorithm, efficient or not, can reliably recover the sparse eigenvector. In contrast, for $k\leq O(\sqrt{n/\log p})$, diagonal thresholding is consistent. It was further conjectured that an SDP approach may close this gap between computational and information limits. We prove that when $k\geq\Omega(\sqrt{n})$, the proposed SDP approach, at least in its standard usage, cannot recover the sparse spike. In fact, we conjecture that in the single-spike model, no computationally-efficient algorithm can recover a spike of $\ell_0$-sparsity $k\geq\Omega(\sqrt{n})$. Finally, we present empirical results suggesting that up to sparsity levels $k=O(\sqrt{n})$, recovery is possible by a simple covariance thresholding algorithm.