Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Framework of A Computational Model of Auditory Memory for Music Emotion Recognition

The bag of frames (BOF) approach commonly used in music emotion recognition (MER) has several limitations. The semantic gap is believed to be responsible for the glass ceiling on the performance of BOF MER systems. However, there are hardly any alternative proposals to address it. In this article, we introduce the theoretical framework of a computational model of auditory memory that incorporates temporal information into MER systems. We advocate that the organization of auditory memory places time at the core of the link between musical meaning and musical emotions. The main goal is to motivate MER researchers to develop an improved class of systems capable of overcoming the limitations of the BOF approach and coping with the inherent complexity of musical emotions.
Treatment of Nongout Joint Deposition Diseases: An Update

This update develops the actual therapeutic options in the management of the joint involvement of calcium pyrophosphate deposition disease (CPPD), basic calcium phosphate (BCP) deposition disease, hemochromatosis (HH), ochronosis, oxalosis, and Wilson's disease. Conventional pharmaceutical treatment provides benefits for most diseases. Anti-interleukine-1 (IL-1) treatment could provide similar results in CPPD than in gout flares. There is only limited evidence about the efficacy of preventive long-term colchicine intake, methotrexate, and hydroxychloroquine in chronic CPPD. Needle aspiration and lavage have satisfactory short and midterm results in BCP. Extracorporeal shockwave therapy has also proved its efficacy for high-doses regimes. Phlebotomy does not seem to have shown real efficacy on joint involvement in HH so far. Iron chelators' effects have not been assessed on joint involvement either, while IL-1 blockade may prove useful. NSAIDs have limited efficacy on joint involvement of oxalosis, while colchicine and steroids have not been assessed either. The use of nitisinone for ochronotic arthropathy is still much debated, but it could provide beneficial effects on joint involvement. The effects of copper chelators have not been assessed either in the joint involvement of Wilson's disease. NSAIDs should be avoided because of the liver affection they may worsen.
BREAST CANCER DETECTION BASED ON A HYBRID APPROACH OF FIREFLY ALGORITHM AND INTELLIGENT SYSTEMS

Prediction and diagnosis of different diseases are two influential components of medical sciences. Increasing expansion of science has led to the use of decision support systems to help physician’s therapeutic policies. In this article, it was tried to predict and identify breast cancer as the most common cancer among women using artificial intelligent systems. The Data were obtained from the Wisconsin Cancer Diagnosis dataset; these data have been categorized based on 9 different properties. The proposed system had an evolutionary algorithm subsystem and a data SVM mining algorithm in order to select the best properties and to separate them from ineffective properties; 4 effective properties were selected. Finally, it used the nervous fuzzy adaptive system to learn prediction and estimation. Comparing the performance of this method with other methods showed the good performance and high accuracy of the proposed system.
"I'm Eating a Sandwich in Hong Kong": Modeling Locations with Tweets

Social media such as Twitter generate large quantities of data about what a person is thinking and doing in a particular location. We leverage this data to build models of locations to improve our understanding of a user’s geographic context. Understanding the user’s geographic context in turn allows us to present information, recommend businesses and services, and place advertisements that are relevant at a hyper-local level. In this paper we create language models of locations using coordinates extracted from geotagged Twitter data. We model locations at varying levels of granularity, from zip code to the country level. We measure the accuracy of these models by the degree to which we can predict the location of an individual tweet, and further by the accuracy with which we can predict the location of a user. We nd that we can meet the performance of the industry standard tool for predicting both the tweet and the user, at the country, state and city levels, and far exceed its performance at the hyper-local level, achieving a three- to ten-fold increase in accuracy at the zip code level.
Linking Social, Open, and Enterprise Data

The new world of big data, of the LOD cloud, of the app economy, and of social media means that organisations no longer own, much less control, all the data they need to make the best informed business decisions. In this paper, we describe how we built a system using Linked Data principles to bring in data from Web 2.0 sites (LinkedIn, Salesforce), and other external business sites such as OpenCorporates, linking these together with pertinent internal British Telecommunications enterprise data into that enterprise data space. We describe the challenges faced during the implementation, which include sourcing the datasets, finding the appropriate "join points" from the individual datasets, as well as developing the client application used for data publication. We describe our solutions to these challenges and discuss the design decisions made. We conclude by drawing some general principles from this work.
A Feminist-Informed Integration of Emotionally Focused and Solution-Focused Therapies

ABSTRACT This article outlines the integration of emotionally focused and solution-focused therapies through a feminist family therapy lens. Integrating therapy models using a feminist-informed lens strengthens traditional family therapy models by addressing gender, ethnic, racial, and social inequities in the family and therapeutic relationships. The epistemology, theoretical framework, and brief overview of each model are provided and a proposed integration is described in-depth, with a table presenting the merging of the models in regards to the stages and steps of emotionally focused therapy so one can move from abstractly discussing feminist issues to integrating them in theoretically consistent, intentional manner.
Time-Dependent Response Versus Scan Angle for MODIS Reflective Solar Bands

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments currently operate onboard the National Aeronautics and Space Administration (NASA's) Terra and Aqua spacecraft, launched on December 18, 1999 and May 4, 2002, respectively. MODIS has 36 spectral bands, among which 20 are reflective solar bands (RSBs) covering a spectral range from 0.412 to 2.13 μm. The RSBs are calibrated on orbit using a solar diffuser (SD) and an SD stability monitor and with additional measurements from lunar observations via a space view (SV) port. Selected pseudo-invariant desert sites are also used to track the RSB on-orbit gain change, particularly for short-wavelength bands. MODIS views the Earth surface, SV, and the onboard calibrators using a two-sided scan mirror. The response versus scan angle (RVS) of the scan mirror was characterized prior to launch, and its changes are tracked using observations made at different angles of incidence from onboard SD, lunar, and Earth view (EV) measurements. These observations show that the optical properties of the scan mirror have experienced large wavelength-dependent degradation in both the visible and near infrared spectral regions. Algorithms have been developed to track the on-orbit RVS change using the calibrators and the selected desert sites. These algorithms have been applied to both Terra and Aqua MODIS Level 1B (L1B) to improve the EV data accuracy since L1B Collection 4, refined in Collection 5, and further improved in the latest Collection 6 (C6). In C6, two approaches have been used to derive the time-dependent RVS for MODIS RSB. The first approach relies on data collected from sensor onboard calibrators and mirror side ratios from EV observations. The second approach uses onboard calibrators and EV response trending from selected desert sites. This approach is mainly used for the bands with much larger changes in their time-dependent RVS, such as the Terra MODIS bands 1-4, 8, and 9 and the Aqua MODIS bands 8 and 9. In this paper, the algorithms of these approaches are described, their performance is demonstrated, and their impact on L1B products is discussed. In general, the shorter wavelength bands have experienced a larger on-orbit RVS change, which, in general, are mirror side and detector dependent. The on-orbit RVS change due to the degradation of band 8 can be as large as 35% for Terra MODIS and 20% for Aqua MODIS. Vital to maintaining the accuracy of the MODIS L1B products is an accurate characterization of the on-orbit RVS change. The derived time-independent RVS, implemented in C6, makes an important improvement to the quality of the MODIS L1B products.
Robust Face Pose Estimation from Insufficient Data

This paper presents a novel method to estimate the pose of human faces from insufficient video data. We represent each pose of a person's face as a connected low-dimensional appearance manifolds which are approximated by affine plane. To construct affine planes, we first sample exemplars from video data and cluster exemplars into each pose. From exemplars, the affine plane is constructed using PCA. However, the sampled exemplars in each specific pose are often not enough for computing the affine plane. This limits the performance of pose estimation. To overcome it, we propose a new sample generation method in constructing pose manifold for on-line face manifold learning. The proposed method was evaluated under several real situations and promising results were obtained.
Two Novel Sets of Genes Essential for Nicotine Degradation by Sphingomonas melonis TY

Nicotine is a type of environmental pollutant present in the tobacco waste that is generated during tobacco manufacturing. Sphingomonas melonis TY can utilize nicotine as a sole source of carbon, nitrogen and energy via a variant of the pyridine and pyrrolidine pathway (the VPP pathway). In this study, we report the identification of two novel sets of genes, ndrA1A2A3, and ndrB1B2B3B4, which are crucial for nicotine degradation by strain TY. ndrA1A2A3 and ndrB1B2B3B4 exhibit similarity with both nicotine dehydrogenase ndh from Arthrobacter nicotinovorans and nicotine hydroxylase vppA from Ochrobactrum sp. SJY1. The transcriptional levels of ndrA1A2A3 and ndrB1B2B3B4 in strain TY were significantly upregulated in the presence of nicotine. Furthermore, ndrA1 or ndrB2 knockout resulted in a loss of the ability to degrade nicotine, whereas gene complementation restored the capacity of each mutant to utilize nicotine for growth. Biodegradation assays indicated that the mutant strains retained the ability to degrade the first intermediate in the pathway, 6-hydroxynicotine (6 HN). However, heterologous expression of ndrA1A2A3 and ndrB1B2B3B4 did not confer nicotine dehydrogenase activity to E. coli DH5α, Pseudomonas putida KT2440 or Sphingomonas aquatilis. These results provide information on the VPP pathway of nicotine degradation in S. melonis TY, and we conclude that these two sets of genes have essential functions in the conversion of nicotine to 6 HN in strain TY.
Does Elevation Reduce Viewers’ Enjoyment of Media Violence?: Testing the Intervention Potential of Inspiring Media

Media violence is often accompanied by moral disengagement cues that allow viewers to minimize the moral concerns that violence in real life typically evokes. What happens, however, when preceding media activate viewers’ moral emotions? Can the affective states associated with elevation decrease subsequent enjoyment of media violence? The current study examined these questions with a one-factor, between-subjects experiment that tested how prior exposure to eudaimonic media affects viewers’ violence enjoyment and prosocial attitudes. Feelings of meaningful affect elicited by eudaimonic media decreased viewers’ enjoyment of violent media and increased prosocial attitudes. Evidence for a boomerang effect through mixed affect and transportation was also found. The implications of these findings for media violence interventions and theory on enjoyment are discussed.
Understanding Term Premia on Real Bonds

Real bonds are a very important asset class and deciphering their risk-return relationship deepens our already extensive understanding about term premia. However, there has been little research on the dynamic behavior and economic determinants of risk premia on such bonds. This paper takes a series of steps towards extending extant evidence on nominal bond premia to their real counterparts. First, we document empirically that the real bond risk premium changes over time and fluctuates between positive and negative values. Second, we find that the real term structure itself contains a component that, albeit undetectable from cross section of bond yields, drives risk premia. Finally, we examine the potential link between the real bond premia and macroeconomic variables. We find that macro factors associated with real estate and consumer income and expenditure can capture a large portion of forecastable variations in excess returns on real bonds. Our empirical results indicate an essential need to propose asset pricing models such that implied real yields are able to account for these stylized facts.
Transformational leadership, work satisfaction and groupperformance : Mediation analysis

This study investigated whether followers’ work satisfaction mediates the relationship between a transformational leadership style and group performance. An evaluation of 32 CEOs by 500 subordinates took place after three months of intensive cooperation within a managerial simulation game. All of the respondents were college students. The followers assessed their work satisfaction using three questions from the Job Diagnostic Survey. Each CEO was evaluated by an average of 15 followers in terms of his/her transformational leadership style using the Czech Leadership Questionnaire. Group performance was assessed based on the final profits of the 32 game companies. Multi-level regression was performed to test the hypothesis. Individual transformational leadership perception related weakly to individual work satisfaction. Group transformational leadership related to group performance partially through group work satisfaction. The mediation effect was strong but significant only at a 10% level because of the limited number of teams (clusters). The advantage of the study was the assessment of the leaders by a large number of followers and the multi-level data analysis. The study is unique because the standardized design of the managerial simulation game enabled an objective and comparable appraisal of the group performance. Contributions to transformational leadership theory, research, and practices are discussed.
Consumer Informedness and Hyperdifferentiation: An Empirical Test of the 'Trading Down' and 'Trading Out' Hypotheses

Consumer informedness plays a critical role in determining consumer choice. Companies now use hyperdifferentiation and resonance marketing strategies to benefit from the long-tail. We test the theory of consumer informedness in a field experiment. The data are from two stated choice experiments in the pubic transport industry in the Netherlands. The increasing implementation of smart card and mobile technology provides opportunities for service providers to achieve new 'best practices' in revenue management. The results provide evidence for trading down (purchase the product with the lowest price) and trading out behavior (purchase the product that suits a specific need). They also have implications for pricing and revenue management for public transport market.
A Numerical Approach to Fokker-Planck Equation with Space-and Time-Fractional and Non Fractional Derivatives

In this paper, we consider the Fokker-Planck equation (FPE) and the FokkerPlanck equation with spaceand time-fractional derivatives. The Fokker-Planck equation arises in various fields in chemistry, natural science, including astrophysics problems, biological applications, chemical physics and other fields. Transforming the Fokker-Planck equation into optimization problem and using polynomial basis functions, we obtain the system of algebraic equation. Then, we solve the system of nonlinear algebraic equation and we have the coefficients of polynomial basis functions expansion. We extensively discuss the convergence of the method. Illustrative examples are included to demonstrate the validity and applicability of the technique.
Disulfide Connectivity Prediction with Extreme Learning Machines

Our paper emphasizes the relevance of Extreme Learning Machine (ELM) in Bioinformatics applications by addressing the problem of predicting the disulfide connectivity from protein sequences. We test different activation functions of the hidden neurons and we show that for the task at hand the Radial Basis Functions are the best performing. We also show that the ELM approach performs better than the Back Propagation learning algorithm both in terms of generalization accuracy and running time. Moreover, we find that for the problem of the prediction of the disulfide connectivity it is possible to increase the predicting performance by initializing the Radial Basis Function kernels with a k-mean clustering algorithm. Finally, the ELM procedure is not only very fast but the final predicting networks can achieve an accuracy of 0.51 and 0.45, per-bonds and per-pattern, respectively. Our ELM results are in line with the state of the art predictors addressing the same problem.
Ikaros‐1 couples cell cycle arrest of late striatal precursors with neurogenesis of enkephalinergic neurons

During central nervous system development, several transcription factors regulate the differentiation of progenitor cells to postmitotic neurons. Here we describe a novel role for Ikaros‐1 in the generation of late‐born striatal neurons. Our results show that Ikaros‐1 is expressed in the boundary of the striatal germinal zone (GZ)/mantle zone (MZ), where it induces cell cycle arrest of neural progenitors by up‐regulation of the cyclin‐dependent kinase inhibitor (CDKi) p21Cip1/Waf1. This effect is coupled with the neuronal differentiation of late precursors, which in turn is critical for the second wave of striatal neurogenesis that gives rise to matrix neurons. Consistently, Ikaros−/− mice had fewer striatal projecting neurons and, in particular, enkephalin (ENK)‐positive neurons. In addition, overexpression of Ikaros‐1 in primary striatal cultures increases the number of calbindin‐ and ENK‐positive neurons. Our results also show that Ikaros‐1 acts downstream of the Dlx family of transcription factors, insofar as its expression is lost in Dlx1/2 double knockout mice. However, we demonstrate that Ikaros‐1 and Ebf‐1 independently regulate the final determination of the two populations of striatal projection neurons of the matrix compartment, ENK‐ and substance P‐positive neurons. In conclusion, our findings identify Ikaros‐1 as a modulator of cell cycle exit of neural progenitors that gives rise to the neurogenesis of ENK‐positive striatal neurons. J. Comp. Neurol. 518:329–351, 2010. © 2009 Wiley‐Liss, Inc.
Discovering processes and patterns of learning in collaborative learning environments using multi-modal discourse analysis

Multimodal learning analytics, with a focus on discourse analysis, can be used to discover, and subsequently understand, the processes and patterns of learning in complex learning environments. Our work builds upon and integrates two types of research: (a) process analytic approaches of dynamically captured video and computer-screen activity and (b) learning analytics. By combining previous analyses of a dataset with new analyses of the processes of learning, patterns of successful and unsuccessful collaboration were identified. In this paper, the results of the application of a heuristics miner to utterances coded with the Decision-Function Coding Scheme, are combined with the results of First Order Markov transitions and in-depth linguistic analysis of the discourse to analyse the processes of collaborative problem solving within a scenario-based virtual world. The analysis of dependency graphs extracted from students’ event logs revealed problem solving actions enacted by students, as well as the dependency relationships between these actions. The addition of in-depth linguistic analysis explained the micro-level discourse of students, producing the observable patterns. Integration of these findings with those previously reported added to the depth of our understanding about this complex learning environment. We conclude with a discussion about the design of the tasks, the processes of collaboration, and the analytic approach that is presented in this paper.
Research and analysis of the Virtual Reality with FLARToolKit

Currently most technologies used by virtual studio systems are extended applications of Virtual Reality (VR) technologies. But for small TV stations and web stations, virtual studio based on traditional VR is so expensive while the FLARToolKit technology used by Augmented Reality (AR) technologies is their top option. However, the calculation speed of FLARToolKit is a little slow and the frame rate is very difficult to reach the live broadcasting video level. My improvements are to apply adaptive thresholds, use sample blurring and change identification frequency, which solves the shortcomings and makes the Virtual Studio faster and better.
Efficient Dual-Resolution Layer Indexing for Top-k Queries

Top-k queries have gained considerable attention as an effective means for narrowing down the overwhelming amount of data. This paper studies the problem of constructing an indexing structure that efficiently supports top-k queries for varying scoring functions and retrieval sizes. The existing work can be categorized into three classes: list-, layer-, and view-based approaches. This paper focuses on the layer-based approach, pre-materializing tuples into consecutive multiple layers. The layer-based index enables us to return top-k answers efficiently by restricting access to tuples in the k layers. However, we observe that the number of tuples accessed in each layer can be reduced further. For this purpose, we propose a dual-resolution layer structure. Specifically, we iteratively build coarse-level layers using skylines, and divide each coarse-level layer into fine-level sub layers using convex skylines. The dual-resolution layer is able to leverage not only the dominance relationship between coarse-level layers, named for all-dominance, but also a relaxed dominance relationship between fine-level sub layers, named exists-dominance. Our extensive evaluation results demonstrate that our proposed method significantly reduces the number of tuples accessed than the state-of-the-art methods.