Miracle-dz/UniFAR-FTUs huggingface dataset:
Personal spatial audio in cars: development of a loudspeaker array for multi-listener transaural reproduction in a vehicle

High quality consumer audio products are well established, however, research into improving the performance and functionality of car audio systems is still growing. This project attempts to enhance the capabilities of in-car audio by combining personal and spatial audio technologies via an array of loudspeakers to reproduce separate spatial audio for both the driver and the front passenger of a car. A MATLAB model of the array has been developed, including source directivity and first order reflections, and the parameters obtained from this acoustic model were used to aid the design of the array. The physical array was CNC milled out of aluminium to create a sleek, high quality product and contained 27 nominally identical loudspeakers. The array was tested using filters created from both modelled and measured transfer functions, created using the least mean squares (LMS) algorithm to control the pressure at four control points. The array was tested in both an anechoic chamber and a car to assess its performance in terms of crosstalk cancellation between control points corresponding to the positions of the listeners’ ears. It was found that the array performed very well in anechoic conditions creating 25 - 45 dB of Crosstalk Cancellation (CTC) between 300 Hz and 11 kHz, and even with the addition of several reflections in the car environment, it was still found to perform well with 18 - 40 dB of CTC over the same frequency range.
Impact of Lecturer's Discourse for Students' Video Engagement: Video Learning Analytics Case Study of MOOCs

Lecture videos are amongst the most widely used instructional methods in present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, students’ engagement behaviour with videos directly impacts the students’ success or failure and accordingly, in-video dropouts positively correlate with dropout from MOOCs. The primary focus of previous video learning analytics studies is on analysing video engagement behaviour using explicit factors (i.e. views or annotations). Limited research studies focus on implicit video learning analytics (e.g. pause, seek, content type) and their impact on students’ success, with existing studies addressing video interactions and their relationship with visual transitions. We aim to explore the association between video interactions and non-visual (i.e. verbal or discourse) transition. This research focuses on text (e.g. cohesion and syntactic complexity) and spoken (e.g. speaking rate) discourse features of lecture videos. We conduct a fine-grained analysis of 3.4 million video interactions of two AdelaideX MOOCs – Programming (Code101x) and Cyber, Surveillance and Security (Cyber101x). According to our results, some discourse features (e.g. lexical diversity and causal connectives) demonstrate statistically significant correlation with video interactions. We present insights for educational video design implications based on discourse processing theories.
Strong approximation for set-indexed partial sum processes via KMT constructions III

We generalize the results of Komlos, Major and Tusnady concerning the strong approximation of partial sums of independent and identically distributed random variables with a finite r -th moment to the case when the parameter set is two-dimensional. The most striking result is that the rates of convergence are exactly the same as in the one-dimensional case.
A Review of Design Science Research in Information Systems: Concept, Process, Outcome, and Evaluation

Design science research is a research paradigm focusing on problem-solving. It is increasingly accepted and adopted by Information Systems (IS) researchers as a legitimate research paradigm because of its capability in balancing research relevance and rigor. In the last fifteen years, many design science research has been published in top IS journals and has received a lot of attentions from IS researchers. However, current confusion and misunderstandings of DSR’s central ideas (e.g., definition, philosophical foundation, research outcomes, etc.) are obstructing it from having a more striking influence on the IS field. The purpose of this paper is to present a comprehensive and critical review of existing DSR literature. In total, 119 papers, published in top IS journals and conference proceedings, were included in the review. The results of this study portray a big picture of current DSR in IS field and build a comprehensive theoretical knowledge base in terms of DSR-related issues. This study also identifies many research issues which can be examined by future DSR.
Theta dependence, sign problems and topological interference

In a Euclidean path integral formulation of gauge theory and quantum mechanics, the theta-term induces a sign problem, and relatedly, a complex phase for the fugacity of topological defects; whereas in Minkowskian formulation, it induces a topological (geometric) phase multiplying ordinary path-amplitudes. In an SU(2) Yang-Mills theory which admits a semi-classical limit, we show that the complex fugacity generates interference between Euclidean path histories, i.e., monopole-instanton events, and radically alters the vacuum structure. At theta=0, a mass gap is due to the monopole-instanton plasma, and the theory has a unique vacuum. At theta=pi, the monopole induced mass gap vanishes, despite the fact that monopole density is independent of theta, due to destructive topological interference. The theory has two options: to remain gapless or to be gapped with a two-fold degenerate vacua. We show the latter is realized by the magnetic bion mechanism, and the two-vacua are realization of spontaneous CP-breaking. The effect of the theta-term in the circle-compactified gauge theory is a generalization of Aharonov-Bohm effect, and the geometric (Berry) phase. As theta varies from 0 to pi, the gauge theory interpolates between even- and odd-integer spin quantum anti-ferromagnets on two spatial dimensional bi-partite lattices, which have ground state degeneracies one and two, respectively, as it is in gauge theory at theta=0 and theta=pi.
Throughput Limits of Wireless Networks With Fading Channels

Wireless Networks have been the topic of fundamental research in recent years with the aim of achieving reliable and efficient communications. However, due to their complexity, there are still many aspects of such configurations that remain as open problems. The focus of this thesis is to investigate some throughput limits of wireless networks. The network under consideration consists of n source-destination pairs (links) operating in a single-hop fashion. In Chapters 2 and 3, it is assumed that each link can be active and transmit with a constant power P or remain silent. Also, fading is assumed to be the dominant factor affecting the strength of the channels between transmitter and receiver terminals. The objective is to choose a set of active links such that the throughput is maximized, where the rate of active links are either unconstrained or constrained. For the unconstrained throughput maximization, by deriving an upper bound and a lower bound, it is shown that in the case of Rayleigh fading: (i) the maximum throughput scales like log n, (ii) the maximum throughput is achievable in a distributed fashion. The upper bound is obtained using probabilistic methods, where the key point is to upper bound the throughput of any random set of active links by a chi-squared random variable. To obtain the lower bound, a threshold-based link activation strategy (TBLAS) is proposed and analyzed. The achieved throughput of TBLAS is by a factor of four larger than what was obtained in previous works with centralized methods and with multihop communications. When the active links are constrained to transmit with a constant rate λ, an upper bound is derived that shows the number of active links scales at most like 1 λ logn. It is proved that TBLAS asymptotically almost surely
The Early-Time Optical Properties of Gamma-Ray Burst Afterglows

We present a multiwavelength analysis of 63 gamma-ray bursts observed with the world’s three largest robotic optical telescopes, the Liverpool and Faulkes Telescopes (North and South). Optical emission was detected for 24 GRBs with brightnesses ranging from R = 10 to 22 mag in the first 10 minutes after the burst. By comparing optical and X-ray light curves from t = 100 to ~106 seconds, we introduce four main classes, defined by the presence or absence of temporal breaks at optical and/or X-ray wavelengths. While 14/24 GRBs can be modeled with the forward-shock model, explaining the remaining 10 is very challenging in the standard framework even with the introduction of energy injection or an ambient density gradient. Early X-ray afterglows, even segments of light curves described by a power law, may be due to additional emission from the central engine. Thirty-nine GRBs in our sample were not detected and have deep upper limits (R < 22 mag) at early time. Of these, only 10 were identified by other facilities, primarily at near infrared wavelengths, resulting in a dark burst fraction of ~50%. Additional emission in the early-time X-ray afterglow due to late-time central engine activity may also explain some dark bursts by making the bursts brighter than expected in the X-ray band compared to the optical band.
Turbo Equalizers for MIMO Systems: Optimality Consideration

In this paper, we show how many of the well-known low complexity linear turbo equalizers, including the zero-forcing (ZF) and minimum mean square error (MMSE) soft-input soft-output (SISO) equalizers, can be obtained as solutions to the variational optimization problem, originating from statistical physics. The imposed variational optimization framework provides an interesting link between the a posteriori probability (APP) based demodulators and the linear SISO equalizers, enabling us to gain new insight into the optimality of these equalizers in the context of turbo processing. Moreover, it suggests improved designs which either tune the known ones or combine the linear filtering and the nonlinear message-passing algorithms. Finally, simulation results are provided to confirm the advantages of the proposed new designs for the MIMO systems
Magnetic Flux in Toroidal Type I Compactification

: We discuss the compactiﬁcation of type I strings on a torus with additional background gauge ﬂux on the D9-branes. The solutions to the cancellation of the RR tadpoles display various phenomenologically attractive features: supersymmetry breaking, chiral fermions and the opportunity to reduce the rank of the gauge group as desired. We also point out the equivalence of the concept of various diﬀerent background ﬁelds and noncommutative deformations of the geometry on the individual D9-branes by con-structing the relevant boundary states to describe such objects.
Gradient-Magnitude-Based Support Regions in Structural Land Use Classification

Land use classification is one of the major problems in remote sensing. Previous studies focused on multispectral information, texture-based features, and features based on edge detection to classify land usage from satellite images. In a previous study, structural features are introduced to classify land development using high-resolution satellite images. These structural features were based on line support regions (LSRs). LSRs are introduced to detect and represent straight lines in images using a pixel-grouping process. The structural features are calculated on these grouped pixels. It is shown that gradient-magnitude-based pixel grouping may also be used in structural feature calculations. Therefore, the aim of this letter is twofold. First, the previous structural feature calculation method is shown to be more general than the LSR. Second, LSR-based features are shown to require fairly high computation compared to gradient-magnitude-based features with similar classification performance
Localization and the interface between quantum mechanics, quantum field theory and quantum gravity dedicated to the memory of Rob Clifton submitted to "Studies in History and Philosophy of Physics"

We show that there are significant conceptual differences between QM and QFT which make it difficult to view the latter as just a relativistic extension of the principles of QM. At the root of this is a fundamental distinction between Born-localization in QM (which in the relativistic context changes its name to Newton-Wigner localization) and modular localization which is the localization underlying QFT, after one liberates it from its standard presentation in terms of field coordinates. The first comes with a probability notion and projection operators, whereas the latter describes causal propagation in QFT and leads to thermal aspects of locally reduced finite energy states. The Born-Newton-Wigner localization in QFT is only applicable asymptotically and the covariant correlation between asymptotic in and out localization projectors is the basis of the existence of an invariant scattering matrix. Taking these significant differences serious has not only repercussions for the philosophy of science, but also leads to a new structural properties as a consequence of vacuum polarization: the area law for localization entropy near the the causal localization horizon and a more realistic cutoff independent setting for the cosmological vacuum energy density which is compatible with local covariance. The article presents some observations about the interface between QFT in CST and QG.
Estimating signal parameters using the nonlinear instantaneous least squares approach

A novel method for signal parameter estimation is presented, termed the nonlinear instantaneous least squares (NILS) estimator. The basic idea is to use the observations in a sliding window to compute an instantaneous (short-term) estimate of the amplitude used in the separated nonlinear least squares (NLLS) criterion. The effect is a significant improvement of the numerical properties in the criterion function, which becomes well-suited for a signal parameter search. For small-sized sliding windows, the global minimum in the NLIS criterion function is wide and becomes easy to find. For maximum size windows, the NILS is equivalent to the NLLS estimator, which implies statistical efficiency for Gaussian noise. A "blind" signal parameter search algorithm that does not use any a priori information is proposed. The NILS estimator can be interpreted as a signal-subspace projection-based algorithm. Moreover, the NILS estimator can be interpreted as an estimator based on the prediction error of a (structured) linear predictor. Hereby, a link is established between NLLS, signal-subspace fitting, and linear prediction-based estimation approaches. The NILS approach is primarily applicable to deterministic signal models. Specifically, polynomial-phase signals are studied, and the NILS approach is evaluated and compared with other approaches. Simulations show that the signal-to-noise ratio (SNR) threshold is significantly lower than that of the other methods, and it is confirmed that the estimates are statistically efficient. Just as the NLLS approach, the NILS estimator can be applied to nonuniformly sampled data.
FD-CABOSFV High Dimensional Data Clustering for Interval-scaled Variables

FD-CABOSFV,an improved algorithm of CABOSFV based on fuzzy discretizaton,is proposed for highdim ensional data clustering of interval-scaled variables.It discretizes the data of each attribute portfolio by using the idea of fuzzy C means clustering,and determines each object's discretized attribute category byλcut turning the attribute value into binary variables,and then uses CABOSFV algorithm to complete clustering.Three UCI benchmark data sets were used to compare FD-CABOSFV with famous K-means clustering algorithm.The empirical tests show that FDCABOSFV is more effective.
Dynamic and adaptive multi-path routing algorithm based on software-defined network

Recently, there has been a surge of the video services over the Internet. However, service providers still have difficulties in providing high-quality video streaming due to the problem of scheduling efficiency and the wide fluctuations of end-to-end delays in the existing multi-path algorithms. To solve these two problems affecting video transmission quality, networks are expected to have the capability of dynamically managing the network nodes for satisfying quality-of-service requirements, which is a challenging issue for media streaming applications. Against this changing network landscape, this article proposes a dynamic and adaptive multi-path routing algorithm under three constraints (packet loss, time delay, and bandwidth) that are based on software-defined network for centralized routing computations and real-time network state updating in multimedia applications. Compared with related multi-path routing proposals, dynamic and adaptive multi-path routing makes efficient use of the latest global network state information achieved by the OpenFlow controller and calculates the optimal routes dynamically according to the real-time status information of the link. Moreover, our proposed algorithm can significantly reduce the computational overhead of the controller while completing a fine-grained flow balance. Experimental results show that dynamic and adaptive multi-path routing significantly outperforms other existing scheduling approaches in achieving a 35%–70% improvement in quality-of-service.
Semi-Supervised Learning Based on GAN With Mean and Variance Feature Matching

The improved generative adversarial network (improved GAN) is a successful method using a generative adversarial model to solve the problem of semi-supervised learning (SSL). The improved GAN learns a generator with the technique of mean feature matching which penalizes the discrepancy of the first-order moment of the latent features. To better describe common attributes of a distribution, this paper proposes a novel SSL method which incorporates the first-order and the second-order moments of the features in an intermediate layer of the discriminator, called mean and variance feature matching GAN (MVFM-GAN). To capture more precisely the data manifold, not only the mean but also the variance is used in the latent feature learning. Compared with improved GAN and other traditional methods, MVFM-GAN achieves superior performance in semi-supervised classification tasks and a better stability of GAN training, particularly in the cases when the number of labeled samples is low. It shows a comparable performance with the state-of-the-art methods on several benchmark data sets. As a byproduct of the novel approach, MVFM-GAN generates realistic images of good visual quality.
Automatic Estimation of Lexical Concreteness in 77 Languages

We estimate lexical Concreteness for millions of words across 77 languages. Using a simple regression framework, we combine vector-based models of lexical semantics with experimental norms of Concreteness in English and Dutch. By applying techniques to align vector-based semantics across distinct languages, we compute and release Concreteness estimates at scale in numerous languages for which experimental norms are not currently available. This paper lays out the technique and its efﬁcacy. Although this is a difﬁcult dataset to evaluate immediately, Concreteness estimates computed from English correlate with Dutch experimental norms at ρ = . 75 in the vocabulary at large, increasing to ρ = . 8 among Nouns. Our predictions also recapitulate attested relationships with word frequency. The approach we describe can be readily applied to numerous lexical measures beyond Concreteness.
Understanding commitment and apathy in is security extra-role behavior from a person-organization fit perspective

ABSTRACT This study presents an empirical investigation of employees’ extra-role behaviour in the information security context based on person – organisation fit theory. The perspective of fit evaluates the differences and similarities between information security policy makers and practitioners to provide employees with an approach to decide whether and how to participate in the implementation of extra security actions. We developed a research model and then conducted a survey and PLS-SEM analysis to test the corresponding hypothesis. The results illustrate that perceived demand – ability fit, perceived need – supply fit, and perceived value fit are effective in motivating security commitment. The empirical evidence shows that security commitment is a partial mediator between complementary fits (demand-ability fit and need-supply fit) and participation intention and is a full mediator between supplementary fit (value fit) and participation intention. In addition, apathy reduces motivation to engage in extra-role behaviour, while value fit and security commitment eliminate such apathy.
Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval

In order to exploit the abundant potential information of the unlabeled data and contribute to analyzing the correlation among heterogeneous data, we propose the semi-supervised model named adaptive semi-supervised feature selection for cross-modal retrieval. First, we utilize the semantic regression to strengthen the neighboring relationship between the data with the same semantic. And the correlation between heterogeneous data can be optimized via keeping the pairwise closeness when learning the common latent space. Second, we adopt the graph-based constraint to predict accurate labels for unlabeled data, and it can also keep the geometric structure consistency between the label space and the feature space of heterogeneous data in the common latent space. Finally, an efficient joint optimization algorithm is proposed to update the mapping matrices and the label matrix for unlabeled data simultaneously and iteratively. It makes samples from different classes to be far apart, while the samples from same class lie as close as possible. Meanwhile, the ${l_{2,1}}$-norm constraint is used for feature selection and outlier reduction when the mapping matrices are learned. In addition, we propose learning different mapping matrices corresponding to different sub-tasks to emphasize the semantic and structural information of query data. Experiment results on three datasets demonstrate that our method performs better than the state-of-the-art methods.
Estimating parameters of GMM based on split EM

The expectation maximization algorithm has been classically used to find the maximum likelihood estimates of parameters in mixture probabilistic models.Problems of the EM algorithm are that parameters initialization depends on some prior knowledge,and it is easy to converge to a local maximum in the iteration process.In this paper,a new method of estimating the parameter of GMM based on split EM is proposed,it starts from a single mixture component,sequentially split and estimates the parameter of the mixture components during expectation maximization steps.Extensive experiments show the advantages and efficiency of the proposed method.