Miracle-dz/UniFAR-FTUs huggingface dataset:
Features for multimodal emotion recognition: An extensive study

The ability to recognize emotions in natural human communications is known to be very important for mankind. In recent years, a considerable number of researchers have investigated techniques allowing computer to replicate this capability by analyzing both prosodic (voice) and facial expressions. The applications of the resulting systems are manifold and range from gaming to indexing and retrieval, through chat and health care. No study has, to the best of our knowledge, ever reported results comparing the effectiveness of several features for automatic emotion recognition. In this work, we present an extensive study conducted on feature selection for automatic, audio-visual, real-time, and person independent emotion recognition. More than 300,000 different neural networks have been trained in order to compare the performances of 64 features and 11 different sets of features with 450 different analysis settings. Results show that: 1) to build an optimal emotion recognition system, different emotions should be classified via different features and 2) different features, in general, require different processing.
Architecture for Preserving Privacy During Data Mining by Hybridization of Partitioning on Medical Data

Abstract- Data mining technique, classification and prediction has improved and used in medical domain to helping medical practitioner in making their decisions. As medical data is highly sensitive to personal information of human being, so it is desired to keep private. There are many approaches for classification which have been adapted for privacy preserving in medical data which are based on data separation technique for privacy preserving. There are two scenarios, one is centralized and other is distributed data where several approaches have been developed. In this paper, we propose architecture for privacy preserving in data mining by combining horizontal data distribution and vertical data distribution for breast cancer data set.
Using the NAG Library to calculate ﬁnancial option prices in Excel

This note discusses the use of algorithms from the NAG Library [18] to calculate prices for financial options. More specifically, we focus on the way in which NAG routines can be called from within a Microsoft Excel spreadsheet, and illustrate this with some examples. Our paper is arranged as follows. The following section presents an elementary introduction to options (§ 2.1), and the variety of models that have been developed for their pricing (§ 2.2). The sensitivity of the price to various dependent parameters is also of interest; this information is contained in the partial derivatives of the price, which are known as the Greeks (see § 3). Section 4 presents an example of one of the option pricing models, along with its closed-form expressions for the price and the Greeks. The calculation of the results from this model (and for many others) can be performed using NAG Library routines (§ 5); we discuss this in some detail for the first of our two examples which calls a NAG routine from Excel (§ 5.1), before presenting a more complicated example (§ 5.2) to illustrate further possibilities in the determination and analysis of the behaviour of option prices—for example, the visualization of the results within Excel. Finally, we describe (§ 5.3) how the interested reader can access the examples that we have discussed, and conclude (§ 6) with some ideas for further work.
A SIMPLEX ALGORITHM FOR PIECEWISE-LINEAR PROGRAMMING Ih FINITENESS, FEASIBILITY AND DEGENERACY

The simplex method for linear programming can be extended to permit the minimization of any convex separable piecewise-linear objective, subject to linear constraints. Part I of this paper has developed a general and direct simplex algorithm for piecewise-linear programming, under convenient assumptions that guarantee a finite number of basic solutions, existence of basic feasible solutions, and nondegeneracy of all such solutions. Part II now shows how these assumptions can be weakened so that they pose no obstacle to effective use of the piecewise-linear simplex algorithm. The theory of piecewise-linear programming is thereby extended, and numerous features of linear programming are generalized or are seen in a new light. An analysis of the algorithm's computational requirements and a survey of applications will be presented in Part III.
Draft Genome Sequence of Deinococcus koreensis SJW1-2T, a Gamma Radiation-Resistant Bacterium Isolated from River Water

Deinococcus koreensis SJW1-2T was isolated from river water and was observed to be highly resistant to gamma radiation. In this study, we report a draft genome sequence which revealed that SJW1-2T possesses genes involved in nucleotide excision repair. ABSTRACT Deinococcus koreensis SJW1-2T was isolated from river water and was observed to be highly resistant to gamma radiation. In this study, we report a draft genome sequence which revealed that SJW1-2T possesses genes involved in nucleotide excision repair. The primary genomic information will aid in elucidating the DNA repair mechanism during ionizing radiation.
Constructing Provenance-Aware Distributed Systems with Data Propagation

Is it possible to construct a heterogeneous distributed computing architecture capable of solving interesting complex problems? Can we easily use this architecture to maintain a detailed history or provenance of the data processed by it? Most existing distributed architectures can perform only one operation at a time. While they are capable of tracing possession of data, these architectures do not always track the network of operations used to synthesize new data. This thesis presents a distributed implementation of data propagation, a computational model that provides for concurrent processing that is not constrained to a single distributed operation. This system is capable of distributing computation across a heterogeneous network. It allows for the division of multiple simultaneous operations in a single distributed system. I also identify four constraints that may be placed on general-purpose data propagation to allow for deterministic computation in such a distributed propagation network. This thesis also presents an application of distributed propagation by illustrating how a generic transformation may be applied to existing propagator networks to allow for the maintenance of data provenance. I show that the modular structure of data propagation permits the simple modification of a propagator network design to maintain the histories of data. Thesis Supervisor: Gerald Jay Sussman Title: Professor of Electrical Engineering
Multi-Modal Supervised Latent Dirichlet Allocation for Event Classification in Social Media

In social media, many existing websites (e.g., Flickr, YouTube, and Facebook) are for users to share their own interests and opinions of many popular events, and successfully facilitate the event generation, sharing and propagation. As a result, there are substantial amounts of user-contributed media data (e.g., images, videos, and textual content) for a wide variety of real-world events of different types and scales. The aim of this paper is to automatically identify the interesting events from massive social media data, which are useful to browse, search and monitor social events by users or governments. To achieve this goal, we propose a novel multi-modal supervised latent dirichlet allocation (mm-SLDA) for social event classification. Our proposed mm-SLDA has a number of advantages. (1) It can effectively exploit the multi-modality and the multi-class property of social events jointly. (2) It makes use of the supervised social event category label information and is able to classify multi-class social event directly. We evaluate our proposed mm-SLDA on a real world dataset and show extensive experimental results, which demonstrate that our model outperforms state-of-the-art methods.
Joint motion estimation and segmentation of complex scenes with label costs and occlusion modeling

We propose a unified variational formulation for joint motion estimation and segmentation with explicit occlusion handling. This is done by a multi-label representation of the flow field, where each label corresponds to a parametric representation of the motion. We use a convex formulation of the multi-label Potts model with label costs and show that the asymmetric map-uniqueness criterion can be integrated into our formulation by means of convex constraints. Explicit occlusion handling eliminates errors otherwise created by the regularization. As occlusions can occur only at object boundaries, a large number of objects may be required. By using a fast primal-dual algorithm we are able to handle several hundred motion segments. Results are shown on several classical motion segmentation and optical flow examples.
A validated integration algorithm for nonlinear ODEs using Taylor models and ellipsoidal calculus

This paper presents a novel algorithm for bounding the reachable set of parametric nonlinear differential equations. This algorithm is based on a first-discretize-then-bound approach to enclose the reachable set via propagation of a Taylor model with ellipsoidal remainder, and it accounts for truncation errors that are inherent to the discretization. In contrast to existing algorithms that proceed in two phases-an a priori enclosure phase, followed by a tightening phase-the proposed algorithm first predicts a continuous-time enclosure and then seeks a maximal step-size for which validity of the predicted enclosure can be established. It is shown that this reversed approach leads to a natural step-size control mechanism, which no longer relies on the availability of an a priori enclosure. Also described in the paper is an open-source implementation of the algorithm in ACADO Toolkit. A simple numerical case study is presented to illustrate the performance and stability of the algorithm.
Knowledge Management in E-Learning Systems

Current knowledge management systems focus on knowledge acquisition, storage, retrieval and maintenance. Yet, for that knowledge to be operational, to become knowledge rather than information, requires internalization and learning. E- learning systems and courseware, on the other hand, are all too often monolithic and inert and fail to facilitate the development and sharing of knowledge. In this paper we discuss some aspects about knowledge providing and present our research in this field through an e-learning system for major risks management.
An Operational Semantics for Timed CSP

An operational semantics is defined for the language of timed CSP, in terms of two relations: an evolution relation, which describes when a process becomes another simply by allowing time to pass; and a timed transition relation, which describes when a process may become another by performing an action at a particular time. It is shown how the timed behaviours used as the basis for the denotational models of the language may be extracted from the operational semantics. Finally, the failures model for timed CSP is shown to be equivalent to may-testing and, thus, to trace congruence.
Root Canal Transportation and Centering Ability of Nickel-Titanium Rotary Instruments in Mandibular Premolars Assessed Using Cone-Beam Computed Tomography

Introduction: The aim of this study was to evaluate, using cone-beam computed tomography (CBCT), transportation and centralization of different nickel-titanium (NiTi) rotary instruments. Methods: One hundred and twenty eight mandibular premolars were selected and instrumented using the following brands of NiTi files: WaveOne, WaveOne Gold, Reciproc, ProTaper Next, ProTaper Gold, Mtwo, BioRaCe and RaCe. CBCT imaging was performed before and after root canal preparation to obtain measurements of mesial and distal dentin walls and calculations of root canal transportation and centralization. A normal distribution of data was confirmed by the Kolmogorov-Smirnov and Levene tests, and results were assessed using the Kruskal-Wallis test. Statistical significance was set at 5%. Results: ProTaper Gold produced the lowest canal transportation values, and RaCe, the highest. ProTaper Gold files also showed the highest values for centering ability, whereas BioRaCe showed the lowest. No significant differences were found across the different instruments in terms of canal transportation and centering ability (P > 0.05). Conclusion: Based on the methodology employed, all instruments used for root canal preparation of mandibular premolars performed similarly with regard to canal transportation and centering ability.
A probabilistic approach for fault tolerant multiprocessor real-time scheduling

In this paper we tackle the problem of scheduling a periodic real time system on identical multiprocessor platforms, moreover the tasks considered may fail with a given probability. For each task we compute its duplication rate in order to (1) given a maximum tolerated probability of failure, minimize the size of the platform such at least one replica of each job meets its deadline (and does not fail) using a variant of EDF namely EDF(k) or (2) given the size of the platform, achieve the best possible reliability with the same constraints. Thanks to our probabilistic approach, no assumption is made on the number of failures which can occur. We propose several approaches to duplicate tasks and we show that we are able to find solutions always very close to the optimal one
Notice of RetractionPsychopathological mechanism and compensation psychotherapy of internet addiction

A study on the internet addiction of Chinese youth by psychological scales, deep interview, brain event-related potentials (ERPs) and heart rate variability (HRV). Focus on the causations, psychopathology, diagnosis methods and psychotherapy of internet addiction. Basing on data from the investigation and experiments, four questions were answered: 1. What is internet addiction? 2. Who are internet addicts? 3. Why they become internet addiction? 4. How to intervene internet addiction? Results and conclusions were received. 1. To classify the internet using problems of Chinese youth from angle of psychological developing and advanced diagnosis methods. 2. To analyze the causations of internet addiction and explain the psychopathological mechanisms by Psychological Decompensation Hypothesis. 3. To explain the brain mechanisms by attention facilitation mechanism. 4. Making the Systematic Compensation Psychotherapy Plan, including group, family, individual and parents psychological intervention. This plan obtained very good effect on the clinical study.
Fuzzy logic control approach to the energy management of Parallel Hybrid Electric Vehicles

In this paper, the author proposed an energy management strategy of the Parallel Hybrid Electric Vehicle (PHEV) was that based on the fuzzy logic control for torque distribution between motor and engine. The value D which is the difference value between the torque of the vehicle requirements Tr and the engine goal requires torque Te, and the battery state of charge (SOC) as two of the fuzzy controller input variables and the torque control coefficient C is the output variable of the fuzzy controller. With three different road conditions, simulated respectively between the rule-based of electric assist control strategy and the fuzzy logic control strategies by using the electric vehicle software ADVISOR. The result shows that the design of fuzzy logic energy management strategy can easily control the engine and motor, significantly increasing fuel economy performance and reduced exhaust emissions.
Understandings of Mentoring within Initial Teacher Education School Placement Contexts: a Scottish perspective

ABSTRACT Within the context of Scottish Initial Teacher Education, this qualitative study critically addresses mentor and mentee understandings of mentoring primary education student teachers. It introduces a Scottish perspective to the international body of literature on student teacher mentoring, and contributes new understandings of mentoring by addressing the role of mentees as well as those of the mentor. An instrumental, collective case study research design was employed with semi-structured interviews of six class teacher mentors and their student teachers conducted to ascertain understandings of the mentoring process. Findings indicate that participants understood mentoring as a multidimensional process designed to support the professional learning of student teachers. The key mentoring relationship is between class teacher mentor and mentee, and essential in developing mentee teaching capacity. Analysis of responses suggests an understanding of mentoring as involving both personal and professional dimensions. The data reveals the complex nature of the mentoring process in terms of the multitude of potential interpretations of these two dimensions and of the ways in which they overlap. It is recommended that all teacher education programmes and associated placement schools consider the provision of effective mentor and mentee education to improve the quality and consistency of mentoring for student teachers.
Lagrange Interpolation Learning Particle Swarm Optimization

In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles’ diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle’s historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO’s comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence.
An Empirically Based Framework for Content Selection and Management in Divorce Education Programs

ABSTRACT Contemporary research has shown that divorce education programs are becoming mandated or recommended in numerous states throughout the nation. Cooccurring with the popularity of these programs is the variation in content of divorce education programs. The focus of this project was to systematically explore the research literature on factors related to divorce adjustment and identify findings to be used in content selection and development for divorce education programs. The divorce-stress-adjustment perspective was utilized to frame the divorce process affecting both children and adults. A systematic literature review resulted in identification of 103 published studies that fit the project criteria. Selected research findings identified in the literature review process were organized according to a conceptual framework designed to categorize content in a tiered approach useful for selecting and managing content in divorce education programs. Implications of the project findings for content identification in divorce education, program development, and program evaluation are explored.
Corrected confidence intervals after sequential testing with applications to survival analysis

Corrected confidence intervals are developed for an unknown parameter for data from a sequential experiment. The corrections are obtained from asymptotic expansions for the distribution of the signed root transformation and they have a simple form. The model does not require independent and identically distributed random variables, or smooth stopping boundaries. Inference about the failure rate of an exponential distribution when data are subject to random censoring, censoring by real time, and optional stopping is used to illustrate the main results. The accuracy of the method is confirmed through simulation studies.
Geometrical Computations Explain Projection Patterns of Long-Range Horizontal Connections in Visual Cortex

Neurons in primary visual cortex respond selectively to oriented stimuli such as edges and lines. The long-range horizontal connections between them are thought to facilitate contour integration. While many physiological and psychophysical findings suggest that collinear or association field models of good continuation dictate particular projection patterns of horizontal connections to guide this integration process, significant evidence of interactions inconsistent with these hypotheses is accumulating. We first show that natural random variations around the collinear and association field models cannot account for these inconsistencies, a fact that motivates the search for more principled explanations. We then develop a model of long-range projection fields that formalizes good continuation based on differential geometry. The analysis implicates curvature(s) in a fundamental way, and the resulting model explains both consistent data and apparent outliers. It quantitatively predicts the (typically ignored) spread in projection distribution, its nonmonotonic variance, and the differences found among individual neurons. Surprisingly, and for the first time, this model also indicates that texture (and shading) continuation can serve as alternative and complementary functional explanations to contour integration. Because current anatomical data support both (curve and texture) integration models equally and because both are important computationally, new testable predictions are derived to allow their differentiation and identification.
Distinguishing between individual and collective representations in values transmission

The present article examines the processes of values transmission and explores the mechanisms by which the stable pattern of intra-cultural variation in values is maintained. The research design distinguishes between values endorsed on individual and collective levels (i. e. values relevant to one’s personal priorities vs. cultural identity). Two studies conducted in New England (2007–2008) investigated the possibility that individual and collective level values (1) rely on different channels of transmission and (2) differ in the degree of sensitivity to family support and parental investment. Theoretical and methodological implications for quantitative analysis of values are discussed.
Transduction of ovarian cancer cells: a recombinant adeno-associated viral vector compared to an adenoviral vector

Recombinant adeno-associated virus (rAAV) vectors have emerged as vehicles for gene therapy. In addition, anti-neoplastic properties have been attributed to wild-type AAV. To take advantage of both features and to overcome technical problems associated with rAAV preparation, we developed a production method in which rAAV particles are amplified in an infectious cycle in the presence of wtAAV. This results in a 103–104-fold amplification of rAAV input particles. rAAV-GFP particles generated by this method were used to transduce ovarian cancer cell lines to evaluate their potential in ovarian cancer gene therapy, in comparison to a rAd-GFP vector. The transduction efficiency of NIH-OVCAR3, MDAH 2774 and SKOV3 cells with rAAV-GFP particles was low (< 1%) and did not improve by increasing the number of particles/cell. Repeated administration and continued exposure of NIH-OVCAR3 and MDAH 2774 improved transduction to over 3%. In contrast, these cell lines were more efficiently transduced by rAAV-GFP in the presence of adenovirus (~15%) and by rAd-GFP (> 50%). These results indicate that in contrast to rAd vectors, rAAV particles are not suitable for therapeutic gene transfer in ovarian cancer cells unless efficient help can be provided to mediate ss to ds DNA conversion. © 2001 Cancer Research Campaign http://www.bjcancer. com