Reviewerly/exHarmony huggingface dataset:
Data Clustering via Uncorrelated Ridge Regression

Ridge regression is frequently utilized by both supervised and semisupervised learnings. However, the trivial solution might occur, when ridge regression is directly applied for clustering. To address this issue, an uncorrelated constraint is introduced to the ridge regression with embedding the manifold structure. In particular, we choose uncorrelated constraint over orthogonal constraint, since the closed-form solution can be obtained correspondingly. In addition to the proposed uncorrelated ridge regression, a soft pseudo label is utilized with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ball constraint for clustering. Moreover, a brand new strategy, i.e., a rescaled technique, is proposed such that optimal scaling within the uncorrelated constraint can be achieved automatically to avoid the inconvenience of tuning it manually. Equipped with the rescaled uncorrelated ridge regression with the soft label, a novel clustering method can be developed based on solving the related clustering model. Consequently, extensive experiments are provided to illustrate the effectiveness of the proposed method.
A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition

This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here the two new techniques described below. (1) Correlation-alignment-based interpolation and (2) covariance regularization. The proposed correlation-alignment-based-interpolation method decreases minC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">primary</sub> up to 30.5% as compared with that from an out-of-domain PLDA model before adaptation, and minC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">primary</sub> is also 5.5% lower than with a conventional linear interpolation method with optimal interpolation weights. Further, the proposed regularization technique ensures robustness in interpolations w.r.t. varying interpolation weights, which in practice is essential.
Gpu-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech Recognition

We present an optimized weighted finite-state transducer (WFST) decoder capable of online streaming and offline batch processing of audio using Graphics Processing Units (GPUs). The decoder is efficient in memory utilization, input/output (I/O) bandwidth, and uses a novel Viterbi implementation designed to maximize parallelism. The reduced memory footprint allows the decoder to process significantly larger graphs than previously possible, while optimizing I/O increases the number of simultaneous streams supported. GPU preprocessing of lattice segments enables intermediate lattice results to be returned to the requestor during streaming inference. Collectively, the proposed algorithm yields up to a 240x speedup over single core CPU decoding, and up to 40x faster decoding than the current state-of-the-art GPU decoder, while returning equivalent results. This decoder design enables deployment of production-grade ASR models on a large spectrum of systems, ranging from large data center servers to low-power edge devices.
Stability of Graph Neural Networks to Relative Perturbations

Graph neural networks (GNNs), consisting of a cascade of layers applying a graph convolution followed by a pointwise nonlinearity, have become a powerful architecture to process signals supported on graphs. Graph convolutions (and thus, GNNs), rely heavily on knowledge of the graph for operation. However, in many practical cases the graph shift operator (GSO) is not known and needs to be estimated, or might change from training time to testing time. In this paper, we are set to study the effect that a change in the underlying graph topology that supports the signal has on the output of a GNN. We prove that graph convolutions with integral Lipschitz filters lead to GNNs whose output change is bounded by the size of the relative change in the topology. Furthermore, we leverage this result to show that the main reason for the success of GNNs is that they are stable architectures capable of discriminating features on high eigenvalues, which is a feat that cannot be achieved by linear graph filters (which are either stable or discriminative, but cannot be both). Finally, we comment on the use of this result to train GNNs with increased stability and run experiments on movie recommendation systems.
Preventive Techniques of Phishing Attacks in Networks

Internet is the most widely used technology in the current era of information technology and it is embedded in daily life activities. Due to its extensive use in everyday life, it has many applications such as social media (Face book, WhatsApp, messenger etc.,) and other online applications such as online businesses, e-counseling, advertisement on websites, e-banking, e-hunting websites, e-doctor appointment and e-doctor opinion. The above mentioned applications of internet technology makes things very easy and accessible for human being in limited time, however, this technology is vulnerable to various security threats. A vital and severe threat associated with this technology or a particular application is “Phishing attack” which is used by attacker to usurp the network security. Phishing attacks includes fake E-mails, fake websites, fake applications which are used to steal their credentials or usurp their security. In this paper, a detailed overview of various phishing attacks, specifically their background knowledge, and solutions proposed in literature to address these issues using various techniques such as anti-phishing, honey pots and firewalls etc. Moreover, installation of intrusion detection systems (IDS) and intrusion detection and prevention system (IPS) in the networks to allow the authentic traffic in an operational network. In this work, we have conducted end use awareness campaign to educate and train the employs in order to minimize the occurrence probability of these attacks. The result analysis observed for this survey was quite excellent by means of its effectiveness to address the aforementioned issues.
confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms

Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.
Gesture Authentication for Smartphones: Evaluation of Gesture Password Selection Policies

Touchscreen gestures are attracting research attention as an authentication method.While studies have showcased their usability, it has proven more complex to determine, let alone enhance, their security.Problems stem both from the small scale of current data sets and the fact that gestures are matched imprecisely -by a distance metric.This makes it challenging to assess entropy with traditional algorithms.To address these problems, we captured a large set of gesture passwords (N=2594) from crowd workers, and developed a security assessment framework that can calculate partial guessing entropy estimates, and generate dictionaries that crack 23.13% or more gestures in online attacks (within 20 guesses).To improve the entropy of gesture passwords, we designed novel blacklist and lexical policies to, respectively, restrict and inspire gesture creation.We close by validating both our security assessment framework and policies in a new crowd-sourced study (N=4000).Our blacklists increase entropy and resistance to dictionary based guessing attacks.
Framework for Quality Ranking of Components in Cloud Computing: Regressive Rank

As the popularity of cloud computing is increasing there is an urgent requirement of developing highly efficient and highly qualitative cloud applications (CA). Hence, it be-comes a big research problem. A recommender system recommends the suitable item to the user and almost all the systems provide a rating score for preference. Traditionally, algorithms predicts the ratings that a user should give to the unrated components to queue the item in recommended list. To select an optimal candidate from a set of function-ally equivalent candidates is crucial through approaches that follow a framework for component quality ranking. More-over, such framework helps in detecting the poor performing candidates from a highly distributed cloud applications. In this paper a novel technique is proposed to provide personalized component ranking for designers by employing the past usage of components by different users. In this approach the similarity between the users is measured based on their rankings for functionally equivalent components set instead of their rating values. In this approach no additional invocation of cloud component is required. Experimental results on real world web-service invocations data set shows that the proposed approach outperforms the previous approaches.
Leveraging Gans to Improve Continuous Path Keyboard Input Models

Continuous path keyboard input has higher inherent ambiguity than standard tapping, because the path trace may exhibit not only local overshoots/undershoots (as in tapping) but also, depending on the user, substantial mid-path excursions. Deploying a robust solution thus requires a large amount of high-quality training data, which is difficult to collect/annotate. In this work, we address this challenge by using GANs to augment our training corpus with user-realistic synthetic data. Experiments show that, even though GAN-generated data does not capture all the characteristics of real user data, it still provides a substantial boost in accuracy at a 5:1 GAN-to-real ratio. GANs therefore inject more robustness in the model through greatly increased word coverage and path diversity.
Dynamic investigations of opto-mechanical properties of iPP/TiO2 nanocomposites fibres using fringe analysis techniques

We present an accurate and fast in-situ investigation of opto-mechanical properties of iPP/TiO2 nanocomposites fibres using a Mach–Zehnder interferometer. The rotation band pass filter approach, autofocusing approach and other approaches are used to overcome the error sources that accompaniments the in-situ investigation of opto-mechanical properties of nanocomposites fibres. The autofocusing approach is applied to overcome the issues which are produced from the heterogeneous distribution of the nanoparticle modifiers throughout the bulk of fibre. According to this approach a meaningful phase information from interference patterns suffered from axially tilted (contains a mix of foci and out of foci parts in the same frame) is extracted. In addition, extract accurate phase information for a fibre sample rotating with an angle, due to the vibrations that is produced by the stretching device, with respect to the interference fringes. Some interference fringe analysis algorithms are combined according to the conditions of the captured interference patterns to perform our task. The extracted phase values are utilized to calculate the birefringence and refractive indices in the parallel and perpendicular polarization direction of iPP/TiO2 nanocomposites fibres at different draw ratios.
Weakly supervised learning for image keypoint matching using graph convolutional networks

Matching between two sets of features from a pair of images is a fundamental and critical step in most computer vision tasks. Existing attempts typically establish a set of putative correspondences with the nearest-neighbor rule in feature spaces and then try to find a subset of reliable matches. However, when there are large camera angles, repetitive structures, and illumination changes existing in the two images of the same scene, recently proposed feature matching approaches do not work well to find good correspondences, especially with a higher proportion of false-positive matches in the putative set. To address these problems, we propose a novel weakly supervised Graph Convolutional Siamese Network Matcher, called GCSNMatcher, to learn the correct correspondences for image feature matching. In particular, GCSNMatcher can directly work on unstructured keypoint sets and further exploit geometric information among sparse interest points by constructing dynamic neighborhood graph structures to enhance the ability of the feature representation of each keypoint. With channel-wised symmetric aggregation operations in our graph convolutional neural networks, the performance of our matcher does not vary under different permutations of unordered keypoint sets. Empirical studies on Yahoo's YFCC100M benchmark dataset demonstrate that our matcher can give a more robust performance for image matching tasks than those state-of-the-art methods, even when it is trained on small datasets.
Height and Weight Estimation from Unconstrained Images

We address the difficult problem of estimating the attributes of weight and height of individuals from pictures taken in completely unconstrained settings. We present a deep learning scheme that relies on simultaneous prediction of human silhouettes and skeletal joints as strong regularizers that improve the prediction of attributes such as height and weight. Apart from imparting robustness to the prediction of attributes, our regularization also allows for better visual interpretability of the attribute prediction. For height estimation, our method shows lower mean average error compared to the state of the art despite using a simpler approach. For weight estimation, which has hardly been addressed in the literature, we set a new benchmark.
Assessing the Impact of Geologic Contact Dilution in Ore/Waste Classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran

Since the Gol-Gohar iron ore mine (GGIOM), which is located in southeastern Iran, is currently one of the biggest iron mines in this region, increasing the accuracy of its mineral resources model has become a challenge for geologists, metallurgists and mining engineers. Given that an accurate classification of the mining blocks into ore or waste is highly significant in strategic mine planning, three approaches for simulating the iron grades were compared against the true grades obtained from production data. The comparison was done by calculating the ratio between the total number of blocks correctly classified as ore and waste and the total number of misclassified blocks, and it was conducted for each approach in three mined benches at the GGIOM. The results reveal that the grade simulation that ignores the geological boundaries and the grade simulation based on a deterministic geological interpretation are much less accurate than the hierarchical approach, which consists of simulating both the geological boundaries and the grades.
Select to Better Learn: Fast and Accurate Deep Learning Using Data Selection From Nonlinear Manifolds

Finding a small subset of data whose linear combination spans other data points, also called column subset selection problem (CSSP), is an important open problem in computer science with many applications in computer vision and deep learning. There are some studies that solve CSSP in a polynomial time complexity w.r.t. the size of the original dataset. A simple and efficient selection algorithm with a linear complexity order, referred to as spectrum pursuit (SP), is proposed that pursuits spectral components of the dataset using available sample points. The proposed non-greedy algorithm aims to iteratively find K data samples whose span is close to that of the first K spectral components of entire data. SP has no parameter to be fine tuned and this desirable property makes it problem-independent. The simplicity of SP enables us to extend the underlying linear model to more complex models such as nonlinear manifolds and graph-based models. The nonlinear extension of SP is introduced as kernel-SP (KSP). The superiority of the proposed algorithms is demonstrated in a wide range of applications.
Cloud Workload Characterization and Profiling for Resource Allocation

Cloud providers aim to efficiently deliver diverse services on demand to users. Recently, they coined the idea of an auction-based market for their resources with the goal of increasing the total revenues. To address the challenge of scheduling and pricing, we build usage profiles for cloud workloads and predict future demands. In this paper, we first present a new methodology to categorize workloads according to their resource usage. We employ a modified hierarchical clustering algorithm that gives us three demand profiles for batch jobs designated as low, medium and high. After that, we extract the number of arrival requests per time for each group. The methodology presented here provides insights to cloud service providers in optimizing resource allocation and improving profits.
Quantum entanglement and violation of Bell’s inequality in dipolar interaction system under Dzyaloshinsky–Moriya interaction

The Dzyaloshinsky–Moriya (DM) interaction contributes to some unusual and interesting magnetic properties in real materials, thus playing an important role in the degree of quantum entanglement in Heisenberg quantum spin models. In [C. S. Castro, O. S. Duarte, D. P. Pires, D. O. Soares-Pinto and M. S. Reis, Phys. Lett. A 380, 1571 (2016)], it has been investigated about the non-locality and the thermal entanglement in a dipolar spin thermal system without DM interaction. In this work, we study the entanglement in the thermal state of inhomogeneous Heisenberg coupling under the presence of the DM interaction along the [Formula: see text]-axis. More precisely, we analyze the effect of the DM interaction on non-locality phenomena and quantum entanglement as measured by negativity and Von Neumann entropy. We show that by comparing with [C. S. Castro, O. S. Duarte, D. P. Pires, D. O. Soares-Pinto and M. S. Reis, Phys. Lett. A 380, 1571 (2016)], the local quantum states become more pronounced, when the DM interaction is taken into account. This fact is well confirmed by noting that Von Neumann entropy is destroyed in the presence of the DM interaction. It can be deduced that the Dzyaloshinsky–Moriya (DM) interaction makes the thermal states less correlated.
Rgb-D Based Multi-Modal Deep Learning for Face Identification

In recent years, the rapid development of depth cameras and wide application scenarios. The depth image information becomes more influential in face identification. In the proposed architecture, we implement the networks in dual CNN paths for color and depth images separately. Moreover, we design innovative loss functions to strengthen the discrimination and the complementary features between color and depth modalities. To preserve the strengthened color and depth features, we fuse both features by concatenation before classification. The experimental results show that our multi-modal learning method achieve 4.3381% EER, 0.27 FMR1000, and 0.33 ZeroFMR on IIIT-D Kinect RGB-D Face dataset for face verification and 99.7% classification accuracy, which exceeds the most state-of-the-art methods. Moreover, the global descriptors of model output are designed to be binarized. Our method requires less memory and computation time.
PHISHER CRUSH: A Mobile Persuasive Game for Promoting Online Security

Phishing has become a major security threat in this Internet age. Dubious computer geeks and malicious hackers tend to make use of this mode of cyberattack due to the ability of phishes to deceive unsuspecting users without being prevented by various system security measures. The major reason why people fall for phishing attacks is that they are mostly unaware of how to detect them. This paper presents the design and implementation of a mobile persuasive game for promoting online security by teaching people how to detect and avoid phishing links. We also present the results and insights from the playability and persuasiveness evaluation of the game. The evaluators reported an overall high level of playability according to the Heuristic Evaluation for Playability (HEP). Above all, the results show that the game is highly persuasive, hence is expected to effectively motivate the desired behaviour of promoting online security. Based on our results, we provided some design consideration and insights from the evaluators' comments.
Exposure Interpolation Via Hybrid Learning

Deep learning based methods have become dominant solutions to many image processing problems. A natural question would be "Is there any space for conventional methods on these problems?" In this paper, exposure interpolation is taken as an example to answer this question and the answer is "Yes". A new hybrid learning framework is introduced to interpolate a medium exposure image for two large-exposure-ratio images from an emerging high dynamic range (HDR) video capturing device. The framework is set up by fusing conventional and deep learning methods. Experimental results indicate that the deep learning method can be used to improve the quality of interpolated image via the conventional method significantly. The conventional method can be adopted to increase the convergence speed of the deep learning method and to reduce the number of samples which is required by the deep learning method. They compensate each other.
Clinical ultrasound image standardization using histogram specification

This article presents a novel ultrasound image standardization approach. This method aims to preserve the non-linear relationship in the echo-textures while ensuring the endurance in the transformed image. This is achieved by utilizing the concept of histogram specification. A reference cumulative distribution function (CDF) of a considered distribution is used to process the test images. Initially, the shape and scale parameter of the distribution are estimated for each type of echo-texture from the reference ultrasound images of a particular organ. These parameters are used to estimate the prototype parameter set. The obtained prototype parameter set, along with a distribution function, is then used to construct a reference CDF. This CDF, in turn, is used as a transfer function in the histogram specification technique for standardizing the given input image. The efficiency and stability of the proposed approach are investigated and compared with the linear scaling technique. Four measures are used to evaluate the algorithms on three data sets. The results show that the proposed approach provides better standardization of images and is invariant to the gain of the scanning device as opposed to linear scaling.