Miracle-dz/UniFAR-FTUs huggingface dataset:
DLTA: A Framework for Dynamic Crowdsourcing Classification Tasks

The increasing popularity of crowdsourcing markets enables the application of crowdsourcing classification tasks. How to conduct quality control in such an application to achieve accurate classification results from noisy workers is an important and challenging task, and has drawn broad research interests. However, most existing works do not exploit the label acquisition phase, which results in their disability of making a proper budget allocation. Moreover, some works impractically make the assumption of managing workers, which is not supported by common crowdsourcing platforms such as AMT or CrowdFlower. To overcome these drawbacks, in this paper, we devise a Dynamic Label Acquisition and Answer Aggregation (DLTA) framework for crowdsourcing classification tasks. The framework proceeds in a sequence of rounds, adaptively conducting label inference and label acquisition. In each round, it analyzes the collected answers of previous rounds to perform proper budget allocation, and then issues the resultant query to the crowd. To support DLTA, we propose a generative model for the collection of labels, and correspondingly strategies for label inference and budget allocation. Experimental results show that compared with existing methods, DLTA obtains competitive accuracy in the binary case. Besides, its extended version, which plugs in the state-of-the-art inference technique, achieves the highest accuracy.
Nanovortex evolution in entrance part of the 2D open type long nanocavity

Non-equilibrium molecular dynamics method (NEMD) is applied to investigate a formation process of water nanovortex in 7 nm wide nanocavity (aspect ratio of which was equal to 3.6). The flow in the nanocavity was induced by Poiseuille 2D water nanoflow in a main nanochannel, to which the nanocavity is situated perpendicularly. The wall of main channel and the nanocavity is made from quartz. Flow is induced by applying constant force to molecules inside the main channel. Based on NEMD simulation data, the sequence of images representing water velocity vector fields was obtained at constant time intervals equal to 1 ns, which shows vortex formation mechanism. Flow field images analysis indicates that the shape and centre position of the nanovortex vary slightly each nanosecond, nevertheless, the structure remains stable in the flow field at the entrance to the nanocavity.
An approach to the theory of gravity with an arbitrary reference level of energy density

Five-vectors theory of gravity is proposed, which admits an arbitrary choice of the energy density reference level. This theory is formulated as the constraint theory, where the Lagrange multipliers turn out to be restricted to some class of vector fields unlike the General Relativity (GR), where they are arbitrary. A possible cosmological implication of the proposed model is that the residual vacuum fluctuations dominate during the whole evolution of the universe. That resembles the universe having a nearly linear dependence of a scale factor on cosmic time.
Individual differences reveal the basis of consonance.

Some combinations of musical notes are consonant (pleasant), while others are dissonant (unpleasant), a distinction central to music. Explanations of consonance in terms of acoustics, auditory neuroscience, and enculturation have been debated for centuries. These debates have remained largely unresolved, in part, because the various theories are difficult to distinguish with conventional methods. This talk will describe our recent studies applying the method of individual differences to this problem. We measured preferences for musical chords as well as nonmusical sounds that isolated particular acoustic factors, including the beating and harmonic relationships between frequency components. Listeners preferred stimuli without beats and with harmonic spectra, but across over 250 subjects, only the preference for harmonic spectra was consistently correlated with preferences for consonant over dissonant chords. Harmonicity preferences were also correlated with the number of years subjects had spent playing a.
Fluctuating Elasticity Fails to Capture Anomalous Sound Scattering in Amorphous Solids.

The fluctuating elasticity (FE) model, introduced phenomenologically and developed by Schirmacher [J. Non-Cryst. Solids 357, 518 (2011)JNCSBJ0022-309310.1016/j.jnoncrysol. 2010.07. 052], is today the only theoretical framework available to analyze low-temperature elastic acoustic scattering in glasses. Its existing formulations, which neglect the tensorial nature of elasticity and exclude long-range disorder correlations, predict that the acoustic damping coefficients obey the standard Rayleigh scaling law: Γ∼k^{d+1}, with k the acoustic wave vector, in dimension d. However, recent numerical data, supported by the analysis of existing experimental results, show that Γ does not obey this scaling law but Γ∼-k^{d+1}lnk. Here we analyze in detail how a fully tensorial FE model can be constructed as a long wavelength approximation of the elastic response of the discrete, atomistic, problem. We show that, although it incorporates all long-range correlations, it fails to capture the observed damping in two respects: (i) it misses the anomalous scaling, and predicts the standard Rayleigh law; (ii) it grossly underestimates the amplitude of scattering by about 2 orders of magnitude. This brings clear evidence that the small scale nonaffine displacement fields, although not simply reducible to local defects, play a crucial role in acoustic wave scattering and hence cannot be ignored.
SUPPORT VECTOR MACHINE BASED APPROACH FOR TRANSLATING VIDEO SCENERIES TO NATURAL LANGUAGE DESCRIPTIONS

Human uses communication language either by written, spoken or typed to describe visual the world around them. So, the study of text description for any video goes increasing. This paper represents a framework that gives output as a description for any video having a maximum size of 50 seconds by using natural language processing. The framework is divided into two sections called training and testing. The training section is used to train the video with its description like activities of objects present in that video. The trained data is stored into the database with its features of scenario of video. Another section is testing section. The testing section is used to test the video and retrieve the output as description of video. By using Natural language processing sentences are generated from objects and their activities present in the video.
Accuracy Assessment of Nondispersive Optical Perturbative Models through Capacity Analysis

A number of simplified models, based on perturbation theory, have been proposed for the fiber-optical channel and have been extensively used in the literature. Although these models are mainly developed for the low-power regime, they are used at moderate or high powers as well. It remains unclear to what extent the capacity of these models is affected by the simplifying assumptions under which they are derived. In this paper, we consider single-channel data transmission based on three continuous-time optical models: (i) a regular perturbative channel, (ii) a logarithmic perturbative channel, and (iii) the stochastic nonlinear Schrödinger (NLS) channel. To obtain analytically tractable discrete-time models, we consider zero-dispersion fibers and a sampling receiver. We investigate the per-sample capacity of these models. Specifically, (i) we establish tight bounds on the capacity of the regular perturbative channel; (ii) we obtain the capacity of the logarithmic perturbative channel; and (iii) we present a novel upper bound on the capacity of the zero-dispersion NLS channel. Our results illustrate that the capacity of these models departs from each other at high powers because these models yield different capacity pre-logs. Since all three models are based on the same physical channel, our results highlight that care must be exercised in using simplified channel models in the high-power regime.
Analisis Kinerja Keuangan Dengan Menggunakan Metode Economic Value Added (EVA), Financial Value Added (FVA) dan Market Value Added (MVA) Studi Kasus Pada Operator Telekomunikasi yang Terdaftar di BEI Periode 2008-2015

Kebutuhan manusia akan telekomunikasi semakin penting. Telekomunikasi tidak lagi menjadi kebutuhan sekunder tetapi telah menjadi kebutuhan utama bagi masyarakat. Dari hal tersebut membuat pendapatan operator telekomunikasi semakin meningkat dan menyebabkan potensi saham industri telekomunikasi akan terus berkembang sehingga menarik para investor untuk berinvestasi. Terkait dengan hal tersebut perlu dilakukan penelitian mengenai kinerja keuangan pada perusahaan operator Telekomunikasi yang terdaftar di Bursa Efek Indonesia (BEI) periode 2008-2015 dengan menggunakan ukuran kinerja yang dilihat dari nilai tambah ( Value Based ) yaitu konsep Economic Value Added (EVA) , Financial Value Added (FVA) dan Market Value Added (MVA) . EVA atau nilai tambah ekonomi adalah perbedaan laba usaha setelah pajak (NOPAT) dan beban modal untuk periode tersebut (yaitu, produk dari biaya modal perusahaan dan modal yang diinvestasikan pada awal periode). Sedangkan FVA untuk mengukur kinerja perusahaan yang mempertimbangkan kontribusi dari fixed assets dalam menghasilkan keuntungan bersih perusahaan dan MVA merupakan alat untuk mengukur berapa banyak kekayaan suatu perusahaan yang telah diciptakan untuk saat tertentu. Metode penelitian yang digunakan adalah metode deskriptif kuantitatif dengan sampel perusahaan operator telekomunikasi yang terdaftar di BEI. Hasil dari penelitian ini menunjukan nilai EVA yang negatif pada semua perusahaan tetapi pada tahun 2008, 2013-2015 pada TLKM mempunyai EVA yg positif. Dilihat dari hasil perhitungan FVA, positif pada semua perusahaan. Tetapi pada tahun 2012-2015 pada BTEL mempunyai FVA yg negatif. Dan terdapat nilai FVA yang negatif yaitu pada FREN pada 2008-2010. Dilihat dari hasil perhitungan MVA , negatif pada semua perusahaan. Tetapi 2009 & 2015 BTEL memiliki MVA positif. Dan pada tahun 2008-2011 pada FREN mempunyai MVA yang positif. Juga terdapat nilai MVA yang positif yaitu pada TLKM tahun 2011-2015. Juga tahun 2010-2013 pada EXCL mempunyai MVA yang positif.
Rationalizing accurate structure prediction in the meta-GGA SCAN functional

Author(s): Yang, JH; Kitchaev, DA; Ceder, G | Abstract: © 2019 American Physical Society. The ability of first-principles computational methods to reproduce ground-state crystal structure selection is key to their application in the discovery of new materials, and yet presents a formidable challenge due to the low-energy scale of the problem and lack of systematic error cancellation. The recently developed Strongly Constrained and Appropriately Normed (SCAN) functional is notable for accurately calculating physical properties such as formation energies and in particular, correctly predicting ground-state structures. Here, we attempt to rationalize the improved structure prediction accuracy in SCAN by investigating the relationship between preferred coordination environments, the description of attractive van der Waals (vdW) interactions, and the overall ground-state prediction in bulk main-group solids. We observe a systematic undercoordination error in the traditional Perdew, Burke, and Ernzerhof (PBE) functional which is not present in SCAN results and find that semiempirical dispersion corrections in the form of PBE+D3 fail to correct this error in a consistent or physical manner. We conclude that the medium-range vdW interaction is correctly parametrized in SCAN and yields meaningful relative energies between coordination environments.
Dynamic cues to binocular depth

Aim: Subjects with no clinically measurable stereoacuity report compelling ‘pop-out’ depth effects when viewing a 3D stereoscopic video. The purpose of this study was to systematically investigate the effectiveness of static and dynamic stereoscopic stimuli, by isolating cues to depth present in stereoscopic 3D entertainment media. Methods: Stereoscopic stimuli were developed that either featured or lacked changes of disparity and/or of stimulus pattern. A PC-controlled 4-alternativeforced- choice (4AFC) task was used to assess the depth detection thresholds of visually normal subjects, with stimuli presented on a passive polarised stereoscopic monitor at 3 m. Thresholds were determined in four conditions: baseline STATIC (fixed disparity level), STATIC PATTERN CHANGE (fixed disparity level with a change in stimulus pattern), Z-LOCATION CHANGE (disparity increase towards target level with a fixed pattern) and CDOT (disparity increase with pattern change). Results: In total 32 subjects aged 18–41 years were recruited from the University of Liverpool. The mean(SD) thresholds were: STATIC 183@(101), Z-LOCATION CHANGE 120@(60), CDOT 167@(111) and STATIC PATTERN CHANGE 241@(129). The conditions which contained a change in z-location yielded a significantly lower threshold than those with fixed disparity ( p > 0.01), whereas the presence of a pattern change resulted in a statistically significant reduction in threshold ( p > 0.05). There was no significant interaction between the factors. Conclusion: By directly comparing thresholds for static and dynamic conditions using stimuli presented on the same device with the same settings (such as display duration, size, contrast, colour, display method, luminance, testing protocol), we can conclude that it is the dynamic nature of the disparity information that confers a benefit on individuals’ depth detection. The dynamic facet of stereopsis may contribute to the compelling ‘pop-out’ effect described when viewing 3D entertainment media.
Developing a Theory of Diagnosticity for Online Reviews

Diagnosticity of a given online review is defined as the extent to which it helps users make informed purchase decisions. Users’ perception of review diagnosticity can be associated with five factors, namely, review rating, review depth, review readability, reviewer profile and product type. Review rating refers to the numerical valence of reviews on a scale of one to five. Review depth refers to the quantity of textual arguments provided in reviews. Review readability measures the extent to which the textual arguments are comprehensible. Reviewer profile indicates the past track record of users who contribute reviews. Product type includes experience products and search products. Few studies hitherto have analyzed review diagnosticity taking into account all these factors concurrently. Hence, this paper attempts to augment prior research by developing a theory of diagnosticity for online reviews. The theory posits that review diagnosticity is shaped by the interplay among review rating, review depth, review readability and reviewer profile albeit differently between experience products and search products.
Aerodynamic noise characteristics of a thin airfoil with line distribution of holes adjacent to the trailing edge

This paper investigates the noise emissions from thin airfoils experimentally. An attempt has been made to study the effect of line distribution of 3 mm diameter holes adjacent to the trailing edge on noise emission characteristics. Thin airfoils having chord length and span of 150 and 240 mm, respectively, are considered for the investigation. The airfoil is subjected to the flow Reynolds number (defined based on the chord length of airfoil) in the range of 2.0 × 105 to 5.0 × 105, and angles of attack of 0°, 2°, and 4°. The spectral results indicate that the modified airfoil with holes is effective in minimizing the lower frequency noise (<3.5 kHz) compared to that of the reference airfoil. The reduction in the low-frequency sound pressure levels for the modified airfoil is found up to 5 dB. The modified airfoil with holes is ineffective in reducing the higher frequency noise components, and in fact, generated a broadband frequency noise at higher Reynolds number. From the spectral studies, a critical Strouhal number is estimated to be around 0.15 that defines the limiting value for the effectiveness of the modified airfoil. The overall sound pressure level studies depicted that the modified airfoil is effective at higher angles of attack with the reduction of around 1–2 dB. The noise generated from the thin airfoils is found to have the directivity towards the upstream.
The 1+1 dimensional Kardar-Parisi-Zhang equation: more surprises

In its original version the KPZ equation models the dynamics of an interface bordering a stable phase against a metastable one. Over past years the corresponding two-dimensional field theory has been applied to models with different physics. Out of a wide choice, I will mostly discuss the spin-spin time correlations for the Heisenberg chain and time-correlations of the conserved fields for 1D fluids. Mentioned will be the recent construction of the scale-invariant asymptotic theory by Matetski, Quastel, and Remenik.
EEG-based detection of modality-specific visual and auditory sensory processing

Objective. A passive brain–computer interface (pBCI) is a system that enhances a human–machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e. the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the ‘level’ of cognitive resources required (e.g. high vs. low), but we argue that having information regarding the specific ‘type’ of resources (e.g. visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level. Approach. 15 participants performed carefully designed visual and auditory tasks while electroencephalography (EEG) data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks. Main results. The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance. Significance. These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.
Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer's Work

The sampling reconstruction theory is one of the great areas of the analysis in which Paul Leo Butzer earned longstanding and excellent theoretical results. Thus, we are forced either by earlier exhaustive presentations of his research activity and/or the highly voluminous material to restrict ourselves to a more narrow and precise sub-area in consideration; we discuss here, giving deeper insight, Paul Butzer’s sampling theoretical work with special attention concerning sampling stochastic signals.
A 3-D Display Pipeline: Capture, Factorize, and Display the Light Field of a Real 3-D Scene

Inspired by pioneering work on modern light ﬁeld displays, we developed a prototype display in which three liquid crystal display (LCD) panels are stacked in front of a backlight. The stacked LCD panels constitute a set of semi-transparent layers, which modulate the out-going light rays. Even though only three layers are used, this display can emit a light ﬁeld, i.e., a dense set of many multi-view images, in diﬀerent viewing directions simultaneously. We established a process pipeline from capture to display of the light ﬁeld of a real 3-D scene. We analyzed the amount of pop-out and motion parallax that can be presented by the display using a given light ﬁeld data. We used a light ﬁeld camera (Lytro Illum) and a multi-view camera (ViewPLUS ProFUSIOIN 25) to capture the light ﬁeld, which was then factorized into layer representations to be displayed. We show several successful results using our prototype display.
Dictionary and deep learning algorithms with applications to remote health monitoring systems

Dictionary and deep learning algorithms facilitate efficient signal representations, thereby offering tremendous representational power along with achieving good recognition rates in real-world machine learning problems. In this dissertation, we present three dictionary learning approaches and a deep learning framework for classification tasks related to remote health monitoring systems. This dissertation presents a more robust class specific centralized dictionary learning method to solve the wearable sensor-based physical activity classification problem. Inspired by experiments that achieved high recognition rates using a few representative samples on high dimensional data, we explore the physical activity recognition signals from wearable sensors and propose a dictionary pair learning-based framework for human physical activity monitoring and recognition. The essential strategy involves integrating the class specific centralized regularizer term into the dictionary pair learning objective function and efficiently optimizing the objective function by combining the alternating direction method of multipliers and the l1 − ls minimization method. Specifically, the class specific regularizer term ensures that the sparse codes belonging to the same class will be concentrated thereby enhancing the classification performance. Experimental results show that the classifiers built in this framework achieve higher recognition rate over four activity recognition tasks and outperforms state-of-the-art methods. Physical activity recognition involves variations in different walking styles and human body movements which result in the erroneous classification of similar activities. To address this issue, we present a correntropy induced dictionary pair learning