Reviewerly/exHarmony huggingface dataset:
Smartphone help contents re-organization considering user specification via conditional GAN

There are various help systems embedded in smartphones that are intended to provide assistance to users. These systems should be conveniently accessible in locations where users may need assistance. Moreover, when help contents are provided without regard to the users interest, it makes it difficult for the user to find relevant content. Thus, the present study provides a new method of re-organizing help content by considering each users interests and preferences using their app usage sequence. Based on the user specification derived from the app usage sequence, help contents usage prediction is generated for each of them with conditional generative adversarial network (GAN) architecture in a new way. Further, another method to pre-process data in applying conditional GAN, originally devised to generate image data, is proposed in our problem. The experiment result showed a higher absolute performance level of help contents usage prediction and better performance of effectiveness in re-organization of top-k contents compared to the existing benchmark method. Thus, the proposed method reflects the users interest and provides appropriate help contents for each user effectively.
On the Usefulness of Statistical Normalisation of Bottleneck Features for Speech Recognition

DNNs play a major role in the state-of-the-art ASR systems. They can be used for extracting features and building probabilistic models for acoustic and language modelling. Despite their huge practical success, the level of theoretical understanding has remained shallow. This paper investigates DNNs from a statistical standpoint. In particular, the effect of activation functions on the distribution of the pre-activations and activations is investigated and discussed from both analytic and empirical viewpoints. This study, among others, shows that the pre-activation density in the bottleneck layer can be well fitted with a diagonal GMM with a few Gaussians and how and why the ReLU activation function promotes sparsity. Motivated by the statistical properties of the pre-activations, the usefulness of statistical normalisation of bottleneck features was also investigated. To this end, methods such as mean(-variance) normalisation, Gaussianisation, and histogram equalisation (HEQ) were employed and up to 2% (absolute) WER reduction achieved in the Aurora-4 task.
Target and Non-target Speaker Discrimination by Humans and Machines

The manner in which acoustic features contribute to perceiving speaker identity remains unclear. In an attempt to better understand speaker perception, we investigated human and machine speaker discrimination with utterances shorter than 2 seconds. Sixty-five listeners performed a same vs. different task. Machine performance was estimated with i-vector/PLDA-based automatic speaker verification systems, one using mel-frequency cepstral coefficients (MFCCs) and the other using voice quality features (VQual2) inspired by a psychoacoustic model of voice quality. Machine performance was measured in terms of the detection and log-likelihood-ratio cost functions. Humans showed higher confidence for correct target decisions compared to correct non-target decisions, suggesting that they rely on different features and/or decision making strategies when identifying a single speaker compared to when distinguishing between speakers. For non-target trials, responses were highly correlated between humans and the VQual2-based system, especially when speakers were perceptually marked. Fusing human responses with an MFCC-based system improved performance over human-only or MFCC-only results, while fusing with the VQual2-based system did not. The study is a step towards understanding human speaker discrimination strategies and suggests that automatic systems might be able to supplement human decisions especially when speakers are marked.
Embedded Cyclegan For Shape-Agnostic Image-To-Image Translation

Image-to-image translation is the task of translating images between domains while maintaining the identities of the images. Generative Adversarial Networks (GANs), and in particular conditional GANs have recently shown incredible success in image-to-image translation and semantic manipulation. Such methods require paired data, meaning that an image must have ground-truth translations across domains. Cycle-consistent GANs solve this problem by using unpaired data. Such methods work well for translations that involve color and texture changes but fail when shape changes are required. This paper firstly analyzes the trade-offs between the cycle-consistency importance and the necessary shape changes required for natural looking imagery. We then propose computationally simple architectural and loss changes to allow the model to perform color, texture, and shape changes as required. The results demonstrate improved translations between domains that require shape changes. We additionally show how the embeddings learned by our model learn interesting and useful attention/segmentation information about the translated images.
Multi-Objective Scheduling for Scientific Workflows on Cloud with Peer-to-Peer Clustering

Running scientific workflows, often involves large-scale data-intensive applications, on cloud. The reduction of data movement is necessary because of the major impact to network utilization and energy consumption in network equipment in cloud data center. In order to reduce the Data Movement, this paper proposes Peer-to-peer clustering technique which can be applied to Directed Acyclic Graph (DAG) workflows. This research also includes Cost and Makespan which are probably the most common objectives for workflow scheduling. Due to dealing with the multi-objective optimization problem, Nondominated Sorting Genetic Algorithm-III (NSGA-III) is selected for finding solutions. Three well-known scientific workflows including Epigenomics, LIGO, and Montage are chosen as testbeds. Pareto front is adopted as the mean for visualization of the results while Hypervolume is selected as the performance metric. DAGs with Peer-to-peer clustering is evaluated by comparing its performance against the original DAG versions. The comparison reveals that DAGs with Peer-to-peer clustering enable NSGA-III to generate solutions having better Hypervolume and Pareto front in all three scientific workflows.
Combining evolutionary computation with the variable neighbourhood search in creating an artificial music composer

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Intrasystem Entanglement Generator and Unambiguos Bell States Discriminator on Chip

Bell measurements, jointly projecting two qubits onto the so-called Bell basis, constitute a crucial step in many quantum computation and communication protocols, including dense coding, quantum repeaters, and teleportation-based quantum computation. A problem is the impossibility of deterministic unambiguous Bell measurements using passive linear optics, even when arbitrarily many auxiliary photons, photon-number-resolving detectors, and dynamical (conditionally changing) networks are available. Current proposals for going over the 50% upper bound without using experimentally challenging nonlinearities rely on using entangled photon ancilla states and a sufficiently large interferometer to combine the signal and ancilla modes. We demonstrate that the novel Multiple Rail architecture, based on the propagation of a single photon in a complex multipath optical circuit (or multiwaveguide optical circuit), provides the possibility to perform deterministic Bell measurements so to unambiguously discrimate all four Bell States.
Basketball Action Data Processing Method Based on Mode Symmetric Algorithm

In the course of basketball training, a large number of basketball action data are generated according to the athletes’ body movements. Due to the low precision of the basketball action data processed by the traditional method in basketball technical training, basketball action processing is not in place. The basketball motion data processing method, based on the mode symmetric algorithm was studied. The basketball motion detection algorithm based on symmetric difference and background reduction was used to remove the background influence of basketball movement and obtain the binary basketball action target image containing the data. On this basis, the pole symmetric mode decomposition (ESMD) method was used to modally decompose the binary basketball action target image containing the data, and the least squares method was used to optimize the elliptic (AGM) curve to realize the screening of basketball action modal data. Through the cleaning and integration of basketball action modal data, integration and data reduction basketball action modal data, the data was processed efficiently. The experimental results showed that the proposed method was a high precision and high efficiency basketball action data processing method.
Moving Object Detection against Sudden Illumination Change Using Improved Background Modeling

This paper presents a moving object detection method against sudden illumination change using improved background modeling. Initially, background model is created for every pixel from the first frame. The sample values for the model of a pixel are collected from the neighborhood of that pixel. Then the new pixel values from the new frames are compared to make background foreground decision. Conventional background modeling faces problem with change in the illumination of the scene. The proposed method frequently checks whether abrupt change of illumination take place or not and then initialize the background model from the frame that is detected with changed illumination. The illumination change is detected by obtaining the images of two frames that are taken at a suitable interval in HSV color space. Then the mean change value of each channel is calculated to make a decision. This enables the background model to start over with new sample values that are obtained in the current illumination condition and the background subtraction process can successfully detect moving object with greater accuracy even in changing illumination condition. Simulation results indicate that the proposed method gives excellent results in illumination changing condition to detect moving object whereas the conventional background modeling can not detect accurately. Comparison analysis shows that our proposed method outperforms recent methods in terms of detection accuracy.
Formation Transformation Based on Leader-Follower Algorithm

A method based on the leader-follower algorithm is proposed for transformation among the formations. The introduction of the greedy algorithm, behavior-based control and virtual structure help realize the region division and the calculation of the distribution of the leaders and followers in target formation. Collision detection and collision avoidance are proposed to solve path conflicts with error free feedback and effectively maintain the stability of motion. The modeling of transformation is simulated by the shape from a line to a wedged, in which the formation is adjusted by the distance difference obtained by feedback. The experimental results show that it is feasible and effective to implement the formation conversion and formation control, and the system possess a better robustness and stability.
Looking Back to Move Forward: A Retrospective Examination of Research at the Intersection of Cognitive Science and Education and What It Means for the Future

Research at the intersection of cognitive science and education has major benefits for both researchers and education practitioners. Yet, a number of logistical and communication challenges must be carefully negotiated to maximize the benefits of these collaborations. In this article, we trace the investments and major publications that have sought to bridge cognitive science and education over the past 40 years to provide context for the papers in a special issue of the Journal of Cognition and Development “Found in Translation: Cognitive Development Meets Education. We use the Institute of Education Sciences’ investments in research at the intersection of cognitive science and education as a case study for what the field has primarily focused on over the past two decades and, finally, we offer a broad overview of the themes that this special issue touches on and a commentary on how we believe the field can improve.
Robust Least Mean Squares Estimation of Graph Signals

Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph topology. It builds on the fact that graph LMS converges faster when the graph topology is specified correctly. We consider two measures of convergence speed, based on which we develop randomized greedy algorithms for robust interpolation of graph signals. In simulation studies, we show that the randomized greedy robust least mean squares (RGRLMS) outperforms the regular LMS and has even more potential given a robust sampling design.
Learning Semiparametric Regression with Missing Covariates Using Gaussian Process Models

Missing data often appear as a practical problem while applying classical models in the statistical analysis.In this paper, we consider a semiparametric regression model in the presence of missing covariates for nonparametric components under a Bayesian framework.As it is known that Gaussian processes are a popular tool in nonparametric regression because of their flexibility and the fact that much of the ensuing computation is parametric Gaussian computation.However, in the absence of covariates, the most frequently used covariance functions of a Gaussian process will not be well defined.We propose an imputation method to solve this issue and perform our analysis using Bayesian inference, where we specify the objective priors on the parameters of Gaussian process models.Several simulations are conducted to illustrate effectiveness of our proposed method and further, our method is exemplified via two real datasets, one through Langmuir equation, commonly used in pharmacokinetic models, and another through Auto-mpg data taken from the StatLib library.
Automatic Segmentation of Nuclei in Histopathology Images Using Encoding-decoding Convolutional Neural Networks

Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming traditional approaches that exploit color and texture features in combination with shallow classifiers or segmentation algorithms. However, DCNNs need large annotated datasets that require extensive amount of time and expert knowledge. In addition, segmentation results obtained by either traditional or DCNN approaches often require a post-processing step to separate cluttered nuclei. In this paper, we propose a computationally efficient nuclei segmentation framework based on DCNNs exhibiting an encoding-decoding structure. We use a partially-annotated dataset and develop an effective training solution. We also use a weighted background model for network to give more importance to borders of nuclei to overcome the problem of clutters. The abolition of any pre-processing or post-processing step without any compromise on the performance leads to a fast and parameter-free system, which presents important advantages with respect to state-of-the-art.
On p-generalized elliptical random processes

We introduce rank-k-continuous axis-aligned p-generalized elliptically contoured distributions and study their properties such as stochastic representations, moments, and density-like representations. Applying the Kolmogorov existence theorem, we prove the existence of random processes having axis-aligned p-generalized elliptically contoured finite dimensional distributions with arbitrary location and scale functions and a consistent sequence of density generators of p-generalized spherical invariant distributions. Particularly, we consider scale mixtures of rank-k-continuous axis-aligned p-generalized elliptically contoured Gaussian distributions and answer the question when an n-dimensional rank-k-continuous axis-aligned p-generalized elliptically contoured distribution is representable as a scale mixture of n-dimensional rank-k-continuous p-generalized Gaussian distribution for a suitable mixture distribution of a positive random variable. Based on this class of multivariate probability distributions, we introduce scale mixed p-generalized Gaussian processes having axis-aligned finite dimensional distributions being p-generalizations of elliptical random processes. Additionally, some of their characteristic properties are discussed and approximates of trajectories of several examples such as p-generalized Student-t and p-generalized Slash processes having axis-aligned finite dimensional distributions are simulated with the help of algorithms to simulate rank-k-continuous axis-aligned p-generalized elliptically contoured distributions.
Document Context-Aware Social Recommendation Method

Due to the data sparsity problem, social network information is often used to improve the performance of recommendation system. Social recommendation is one of recommendation systems, which has gradually become the hot research field of recommendation because it can better simulate the recommendation process in the real world and better reflect the role of people in the process of recommendation. However, most of the existing methods of social recommendation ignore the impact of the item's contextual information, and only consider the connection between users. In order to take full advantage of contextual information and social relationships into consideration, this paper proposes a novel social recommendation method, named SocialConvMF, which integrates trust relationship and Convolutional Neural Network (CNN) into Probability Matrix Factorization (PMF). First, it uses the trust-aware social recommendation method; then captures contextual information in the document; finally, it utilizes trust-aware and contextual information to do social recommendation. Experimental results on three real-world datasets demonstrate that our proposed method can further improve the accuracy of the rating forecast in social recommendation.
Measuring Systems and Architectures: A Sustainability Perspective

Measurement and Metrics for Green and Sustainable Software Systems Workshop (MeGSuS), the ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS), and the 12th European Conference on Software Architecture (ECSA).Feedback or suggestions are welcome.In addition, if you try or adopt
Introducing Fluctuation into Increasing Order of Symmetric Uncertainty for Consistency-Based Feature Selection

In order to select correlated and relevant features in a feature selection, several filter methods adopt a symmetric uncertainty as one of the feature ranking measures. In this paper, we introduce a fluctuation into the increasing order of the symmetric uncertainty for the consistency-based feature selection algorithms. Here, the fluctuation is an operation of transforming the sorted sequence of features to a new sequence of features. Then, we compare the selected features by the algorithms with a fluctuation with those without fluctuations.
Analysis of Dynamics of Nonlinear Map Optimization

We are developing a swarm intelligence optimization algorithm based on nonlinear dynamical system theory. In this article, we introduce Nonlinear Map Optimization (abbr. NMO) which we proposed. NMO is classified as swarm intelligence (abbr. SI) optimizer and consists of some search individuals whose dynamics is driven by a simple nonlinear map. The simple nonlinear map is regarded as a kind of circle map. For effective optimal solution search, the search point distribution of each search individual is important. The search point distribution is controlled by the simple nonlinear map. The parameters of the simple nonlinear map are controlled so that the search point distribution can be effectively searched for the optimal solution. Also, this map generates a chaotic search point time series while keeping the search range. Such a time series can efficiently search within the search range. As a result, NMO can search along the valley of the evaluation function. Namely, NMO is considered to have a rotation invariance and a scaling invariance. NMO can also be regarded as a system in which one-dimensional map oscillators move while being coupled with each other with a coupling strength according to distance. Therefore, the analysis of the dynamics of NMO gives new knowledge of the nonlinear coupled map.
Blind Super-resolution in Two-dimensional Parameter Space

In this paper, we provide a new mathematical framework for identifying the parameters of a linear system from its response to multiple unknown waveforms. We assume that the system response is given by an unknown number of scaled versions of time-delayed and frequency-shifted unknown waveforms. Then, we develop a blind two-dimensional super-resolution framework that is based on the convex atomic norm frame-work to recover the continuous time-frequency shifts as well as the unknown waveforms. We prove that under a minimum separation condition between the time-frequency shifts and with a certain lower bound on the total number of the observed samples, all the unknowns in the system can be recovered precisely and with high probability. Simulation results that confirm the theoretical findings in the paper are provided.
Blind Image Watermarking Based on Discrete Hilbert Transform and Polar Decomposition

Digital watermarking is a potent technique for digital right management of multimedia data. This paper aims to introduce a blind image watermarking algorithm using discrete Hilbert transform (DHT) and polar decomposition (PD). Initially, Gaussian map is utilized to preprocess the watermark image to enhance its security. Then the original image is segmented into red, green and blue channels. The blue channel of the original image is selected and it is divided into non-overlapping blocks. DHT is conducted on each block to obtain the DHT coefficients represented in matrix form. PD is then carried out to each of these matrices and watermark data is inserted into the positive semi-definite Hermitian matrix obtained from the DHT coefficients of each block by using an embedding operation. Experimental results specify that the proposed algorithm has excellent resistance to numerous attacks. In addition, it also provides good quality watermarked images. Moreover, it shows more excellent results than the recent techniques in respect of robustness and imperceptibility.