Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Twitter to Detect and Tag Important Events in Live Sports

In this paper we examine the effectiveness of using a filtered stream of tweets from Twitter to automatically identify events of interest within the video of live sports transmissions. We show that using just the volume of tweets generated at any moment of a game actually provides a very accurate means of event detection, as well as an automatic method for tagging events with representative words from the tweet stream. We compare this method with an alternative approach that uses complex audio-visual content analysis of the video, showing that it provides near-equivalent accuracy for major event detection at a fraction of the computational cost. Using community tweets and discussion also provides a sense of what the audience themselves found to be the talking points of a video.
Knowledge Acquisition through Social Media towards Academic Performance among Undergraduate Students

The purpose of the study is to investigate knowledge acquisition using social media among final semester students in the Faculty of Information Management, Universiti Teknologi MARA (UiTM). This study used a survey method to collect data. A total of 173 respondents answered to this survey questionnaire which was a self-developed questionnaire. The questionnaire measures the methods of knowledge acquisition through social media, factors influencing knowledge acquisition through social media, and students’ academic performance who acquire knowledge through social media. The results showed that respondents quite agree on the methods of knowledge acquisition through social media (Mean=3.35) and factors influencing knowledge acquisition (Mean=3.49) and about their academic performance (Mean=3.83). The implication of the study is that the academic institutions and organizations can use this study in enhancing the students’ academic performance and productivity in the organization by using social media as a medium to acquire knowledge. For future study, the study can highlight on barriers in knowledge acquisition through social media and extending the target population to the employees in the organizations.
Differential protection of power transformer based on HS-transform and support vector machine

This paper presents a new approach to power transformer differential protection based on HS-transform and SVM (support vector machine). Here, HS-transform is used to generate frequency contours from samples of differential current and parseval’s theorem is used to extract the features like energy and standard deviation. Subsequently these features are used as inputs to SVM for fault classification to identify the inrush current and fault current. The SVM is tested and trained with the features extracted from frequency contours for different fault conditions. Simulation of the fault (with and without noise) was done using MATLAB AND SIMULINK software taken 2 cycles of data each 400 samples. The advantage of the proposed algorithm provides more accurate results even with the presence of noisy inputs (Energy and standard deviation) and accurate in identifying inrush and fault currents. Overall accuracy of the proposed method is found to be 92.85%.
Four-Dimensional Graded Consciousness

Both the multidimensional phenomenon and the polysemous notion of consciousness continue to prove resistant to consistent measurement and unambiguous definition. This is hardly surprising, given that there is no agreement even as regards the most fundamental issues they involve. One of the basic disagreements present in the continuing debate about consciousness pertains to its gradational nature. The general aim of this article is to show how consciousness might be graded and multidimensional at the same time. We therefore focus on the question of what it is, exactly, that is or could be graded in cases of consciousness, and how we can measure it. Ultimately, four different gradable aspects of consciousness will be described: quality, abstractness, complexity and usefulness, which belong to four different dimensions, these being understood, respectively, as phenomenal, semantic, physiological, and functional. Consequently, consciousness may be said to vary with respect to phenomenal quality, semantic abstraction, physiological complexity, and functional usefulness. It is hoped that such a four-dimensional approach will help to clarify and justify claims about the hierarchical nature of consciousness. The approach also proves explanatorily advantageous, as it enables us not only to draw attention to certain new and important differences in respect of subjective measures of awareness and to justify how a given creature may be ranked higher in one dimension of consciousness and lower in terms of another, but also allows for innovative explanations of a variety of well-known phenomena (amongst these, the interpretations of blindsight and locked-in syndrome will be briefly outlined here). Moreover, a 4D framework makes possible many predictions and hypotheses that may be experimentally tested (We point out a few such possibilities pertaining to interdimensional dependencies).
A Music Classification Method based on Timbral Features

This paper describes a method for music classification based solely on the audio contents of the music signal. More specifically, the audio signal is converted into a compact symbolic representation that retains timbral characteris tics and accounts for the temporal structure of a music piece. Models that capture the temporal dependencies observed in the symbolic sequences of a set of music pieces are built using a statistical language modeling approach. The proposed method is evaluated on two classification tasks (Music Genre classification and Artist Identification) using publicly available datasets. Finally, a distance measu re between music pieces is derived from the method and examples of playlists generated using this distance are given . The proposed method is compared with two alternative approaches which include the use of Hidden Markov Models and a classification scheme that ignores the temporal structure of the sequences of symbols. In both cases the proposed approach outperforms the alternatives. Techniques for managing audio music databases are essential to deal with the rapid growth of digital music distribution and the increasing size of personal music collections. The Music Information Retrieval (MIR) community is well aware that most of the tasks pertaining to audio database management are based on similarity measures between songs [1‐4]. A measure of similarity can be used for organizing, browsing, visualizing large music collection s. It is a valuable tool for tasks such as mood, genre or artist classification that also can be used in intelligent music rec ommendation and playlist generation systems. The approaches found in the literature can roughly be divided in two categories: methods based on metadata and methods based on the analysis of the audio content of the songs. The methods based on metadata have the disadvantage of relying on manual annotation of the music contents which is an expensive and error prone process. Furthermore, these methods limit the range of songs that can be analyzed since they rely on textual information which may
Particle swarm optimization for vehicle routing problem with uncertain demand

In this paper, we deal with the vehicle routing problem where vehicles have finite capacities and demands of customers are uncertain. We represent the uncertain demands by fuzziness and interpret them as possibility distributions. According to the same consideration as the fuzzy programming with recourse, we treat the influence of the fuzzy of customers' demands as recourse cost. Defining the fuzzy number as it's the generalized mean value, the proposed model is equivalent to an ordinary programming problem and then a solution method based on Particle Swarm Optimization (PSO) can be proposed to give the best solution of the problem. Finally, some examples are given to illustrate the two-stage model and the solution algorithm.
MODEL STUDIES ON THE SIZE DISTRIBUTION DYNAMICS OF ATMOSPHERIC AEROSOLS

Understanding the dynamic processes shaping the atmospheric aerosol particle size distribution is essential in order to evaluate the effect of the particles on environmental issues such as climate change, and human health. This thesis employs process-oriented aerosol models to investigate the formation and growth mechanisms of atmospheric nanosized particles. Model studies suggest that atmospherically relevant concentrations of low volatile organic vapours can grow nucleation mode particles to cloud condensation nuclei sizes. Furthermore, such organic compounds may under certain conditions dominate the composition of 3-4 nm particles initially formed from inorganic vapours. In addition, heterogeneous nucleation of sulphuric acid, ammonia and water offers a possible explanation to the patchy sulphate coating observed on atmospheric mineral dust. Although simulations indicate that the sulphate-coated mineral dust particles typically cannot inhibit new particle formation in the atmosphere altogether, dust particles can provide large coagulation sinks for newly formed clusters and thus supress the formation new detectable particles. In large scale atmospheric models, it is vital to minimize the computational burden of the numerical description of the aerosol size distribution while maintaining the reliability of the model predictions. With the size resolution typical to large scale applications, discretizing the particles into fixed size sections based on their involatile core volume instead of their total volume does not improve the model performance sufficiently. Abandoning the fixed section locations eliminates much of the artificial widening of the size distribution but may introduce additional problems. The moving center method, which lets the particle size vary within the section boundaries, and the retracking method, which lets the size sections move in time but retracks them into a fixed grid after certain time intervals, both predict burst-like rather than continuous formation of detectable particles. The former of the methods may also predict extra dents in the distribution thus distorting its shape. On the other hand, the performance of the simple multimodal monodisperse model in new particle formation simulations can be greatly improved by basing the choice of the pre-existing particle mode locations on the condensation sink of the particle population.
Identification of hysteresis functions using a multiple model approach

Considers the identification of static hysteresis functions which describe phenomena in mechanical systems, piezoelectric actuators and materials. A solution based on a model with a parallel structure of elementary models (with switching) and the interacting multiple model (IMM) approach is proposed. For each elementary model a separate IMM estimator is implemented. The estimated parameters represent a fusion of values from preset grids, weighted by the IMM mode probabilities. The estimated state of each elementary model is a fusion of the estimated states (from the separate Kalman filters) weighted by the IMM probabilities. The nonlinear identification problem is reduced to a linear one. Results from simulation experiments are presented.
Semi‐Nonparametric IV Estimation of Shape‐Invariant Engel Curves

This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identification and estimation of both the nonparametric shapes of the Engel curves and the parametric specification of the demographic scaling parameters. The identification condition relates to the bounded completeness and the estimation procedure applies the sieve minimum distance estimation of conditional moment restrictions, allowing for endogeneity. We establish a new root mean squared convergence rate for the nonparametric instrumental variable regression when the endogenous regressor could have unbounded support. Root-n asymptotic normality and semiparametric efficiency of the parametric components are also given under a set of "low-level" sufficient conditions. Our empirical application using the U.K. Family Expenditure Survey shows the importance of adjusting for endogeneity in terms of both the nonparametric curvatures and the demographic parameters of systems of Engel curves.
Short Duration Combined Mild Hypothermia Improves Resuscitation Outcomes in a Porcine Model of Prolonged Cardiac Arrest

Objective. In this study, our aim was to investigate the effects of combined hypothermia with short duration maintenance on the resuscitation outcomes in a porcine model of ventricular fibrillation (VF). Methods. Fourteen porcine models were electrically induced with VF and untreated for 11 mins. All animals were successfully resuscitated manually and then randomized into two groups: combined mild hypothermia (CH group) and normothermia group (NT group). A combined hypothermia of ice cold saline infusion and surface cooling was implemented in the animals of the CH group and maintained for 4 hours. The survival outcomes and neurological function were evaluated every 24 hours until a maximum of 96 hours. Neuron apoptosis in hippocampus was analyzed. Results. There were no significant differences in baseline physiologies and primary resuscitation outcomes between both groups. Obvious improvements of cardiac output were observed in the CH group at 120, 180, and 240 mins following resuscitation. The animals demonstrated better survival at 96 hours in the CH group when compared to the NT group. In comparison with the NT group, favorable neurological functions were observed in the CH group. Conclusion. Short duration combined cooling initiated after resuscitation improves survival and neurological outcomes in a porcine model of prolonged VF.
A measurement study on user behavior of p2p VoD system

Nowadays, as the fast growth in population of p2p VoD system, user behaviors are playing an increasingly crucial role for the performance of system. In this paper, the user watching and sharing behaviors of p2p VoD system were studied by analyzing a large amount of real data. Then, a comparison between user behavior in p2p VoD system and other main kinds VoD systems was made. After that, a mathematic model was constructed to describe the characters based on those analyses. Finally, the correctness of conclusions was verified by an experiment on real system and an improved method was provided. Those studies were believed can possess guiding significance for analyzing system performance, traffic loads and studies on user behavior of other online applications.
A study on the requirements of accountable cloud services and log management

As services themselves and computer resources of cloud services become increasingly non-local, accountability to users for the results and implementation status of the service have emerged as important issues. This paper gives a detailed overview of what would be required to (1) link log data for multiple services supported by non-local servers and (2) trace cloud-based services, focusing on prevailing web-based cloud services. The paper presents a robust accountability framework and discusses how cryptography could be applied to log data accountability.
Physical and perceptual correlates of voice using acoustic analysis

December 13-15, 2007: Firenze, Italy, ed. by C. Manfredi, ISBN 978 88-8453-673-3 (print) ISBN 978-88-8453-674-7 (online) © Firenze university press, 2007. Abstract: Acoustic analysis of voice is potentially useful for objective assessment and characterization of voice disorders. However, before extracting acoustic measures of voice it is firstly pertinent to ask; what do we mean by voice? In describing voice, the perceptual impression formed by the listener or the physical characteristics of the production mechanism may be of primary interest. With this in mind specific correlations with perception and source production are worthy of attention. The voiced speech signal recorded using a microphone comprises a glottal source signal, which has been resonated and radiated. Hence this signal is only indirectly related to the underlying source production mechanism. Furthermore it is only indirectly related to the perception of voice quality because auditory processing is not considered. Indices commonly extracted from the acoustic speech waveform include the harmonics-to-noise ratio (HNR), jitter and shimmer. This presentation inquires into how these measures relate to physical and perceptual characterizations and into how progress on these issues may be advanced.
Daily Rhythms of Core Temperature and Locomotor Activity Indicate Different Adaptive Strategies to Cold Exposure in Adult and Aged Mouse Lemurs Acclimated to a Summer-Like Photoperiod

Daily variations in core temperature (Tc) within the normothermic range imply thermoregulatory processes that are essential for optimal function and survival. Higher susceptibility towards cold exposure in older animals suggests that these processes are disturbed with age. In the mouse lemur, a long-day breeder, we tested whether aging affected circadian rhythmicity of Tc, locomotor activity (LA), and energy balance under long-day conditions when exposed to cold. Adult (N = 7) and aged (N = 5) mouse lemurs acclimated to LD14/10 were exposed to 10–day periods at 25 and 12°C. Tc and LA rhythms were recorded by telemetry, and caloric intake (CI), body mass changes, and plasma IGF-1 were measured. During exposure to 25°C, both adult and aged mouse lemurs exhibited strong daily variations in Tc. Aged animals exhibited lower levels of nocturnal LA and nocturnal and diurnal Tc levels in comparison to adults. Body mass and IGF-1 levels remained unchanged with aging. Under cold exposure, torpor bout occurrence was never observed whatever the age category. Adult and aged mouse lemurs maintained their Tc in the normothermic range and a positive energy balance. All animals exhibited increase in CI and decrease in IGF-1 in response to cold. The decrease in IGF-1 was delayed in aged mouse lemurs compared to adults. Moreover, both adult and aged animals responded to cold exposure by increasing their diurnal LA compared to those under Ta = 25°C. However, aged animals exhibited a strong decrease in nocturnal LA and Tc, whereas cold effects were only slight in adults. The temporal organization and amplitude of the daily phase of low Tc were particularly well preserved under cold exposure in both age groups. Sexually active mouse lemurs exposed to cold thus seemed to prevent torpor exhibition and temporal disorganization of daily rhythms of Tc, even during aging. However, although energy balance was not impaired with age in mouse lemurs after cold exposure, aging was associated with lower LA and Tc during the night and delayed decrease in IGF-1. This might reflect that adaptive strategies to cold exposure differ with age in mouse lemurs acclimated to a summer-like photoperiod.
The optical to γ-ray emission of the Crab pulsar: a multicomponent model

We present a multicomponent model to explain the features of the pulsed emission and spectrum of the Crab Pulsar, on the basis of X and γ -ray observations obtained with BeppoSAX, INTEGRAL and CGRO. This model explains the evolution of the pulse shape and of the phase-resolved spectra, ranging from the optical/UV to the GeV energy band, on the assumption that the observed emission is due to more components. The first component, C O , is assumed to have the pulsed double-peaked profile observed at the optical frequencies, while the second component, C X , is dominant in the interpeak and second peak phase regions. The spectra of these components are modelled with log-parabolic laws and their spectral energy distributions have peak energies at 12.2 and 178 keV, respectively. To explain the properties of the pulsed emission in the MeV-GeV band, we introduce two more components, $C_{O\gamma}$ and $C_{X\gamma}$, with phase distributions similar to those of C O and C X and log-parabolic spectra with the same curvature but peak energies at about 300 MeV and 2 GeV. This multicomponent model is able to reproduce both the broadband phase-resolved spectral behaviour and the changes of the pulse shape with energy. We also propose some possible physical interpretations in which C O and C X are emitted by secondary pairs via a synchrotron mechanism while $C_{O\gamma}$ and $C_{X\gamma}$ can originate either from Compton scattered or primary curvature photons.
Overexpression of neuromedin U is correlated with regional metastasis of head and neck squamous cell carcinoma

Regional metastasis is an important prognostic factor for patients with head and neck squamous cell carcinoma (HNSCC). Neuromedin U (Nmu) is a secreted neuropeptide, named due to its potent uterine contraction-inducing activity. The aim of the present study was to analyze the significance of Nmu in the regional metastasis of HNSCC. The characteristics of 240 patients recruited from the Department of Otolaryngology Head and Neck Surgery, Renmin Hospital of Wuhan University (Wuhan, China) were summarized retrospectively. The positive rate of neck dissection was analyzed according to the material. The expression levels of Nmu in human tumor samples were analyzed using immunohistochemistry. Subsequently, the expression of Nmu was investigated using a tissue microassay to analyze the association between Nmu protein expression and Tumor Node Metastasis (TNM) status. The positive rate of neck dissection was 51.4% in the study sample. The expression levels of Nmu in primary tumors with regional metastasis were higher, compared with those without metastasis. There was increased protein expression of Nmu in the advanced tumor tissues. The data obtained in the present study demonstrated that the expression of Nmu was correlated with regional metastasis and TNM status. Overexpression of Nmu may be involved in the process of regional metastasis of HNSCC, and may serve as a novel and valuable biomarker for predicting regional metastasis in patients with HNSCC.
Barriers to Reporting Sexual Victimization: Prevalence and Correlates among Undergraduate Women

This study examined the frequency and correlates of barriers to reporting sexual victimization to law enforcement. Participants were 127 female undergraduate sexual assault victims who completed self-report surveys. The most frequently reported barriers were “I handled it myself” and “I didn't think it was serious enough. ” Factor analysis of the reported barriers items revealed two factors: shame/not wanting others involved and did not acknowledge the event as a crime /handled it myself. Shame/not wanting others involved was positively associated with physical injury, being victimized by a relative, and self-blame. Acknowledgment/handled it myself was negatively associated with being victimized by a relative. Findings suggest that intervention efforts should focus on increasing acknowledgment, decreasing negative reactions to disclosure, and decreasing victims' self-blame.
On the Efficiency of Spam Mailing and Portal Advertising

The objective of this paper is to provide a simple approach endogenizing the welfare reducing or welfare enhancing effect of informative advertising. Using this approach, it is possible to analyse the welfare consequences of a technology shock that reduces unit information costs, and to explain whether electronic junk mails or portal advertising will cause welfare gains or losses in a competitive environment.