Reviewerly/exHarmony huggingface dataset:
Generative Latent Flow: A Framework for Non-adversarial Image Generation.

In this work, we propose the Generative Latent Flow (GLF), an algorithm for generative modeling of the data distribution. GLF uses an Auto-encoder (AE) to learn latent representations of the data, and a normalizing flow to map the distribution of the latent variables to that of simple i.i.d noise. In contrast to some other Auto-encoder based generative models, which use various regularizers that encourage the encoded latent distribution to match the prior distribution, our model explicitly constructs a mapping between these two distributions, leading to better density matching while avoiding over regularizing the latent variables. We compare our model with several related techniques, and show that it has many relative advantages including fast convergence, single stage training and minimal reconstruction trade-off. We also study the relationship between our model and its stochastic counterpart, and show that our model can be viewed as a vanishing noise limit of VAEs with flow prior. Quantitatively, under standardized evaluations, our method achieves state-of-the-art sample quality among AE based models on commonly used datasets, and is competitive with GANs' benchmarks.
Parallel Correlation Filters for Real-Time Visual Tracking

Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.
The advantages of multiple classes for reducing overfitting from test set reuse

Excessive reuse of holdout data can lead to overfitting. However, there is little concrete evidence of significant overfitting due to holdout reuse in popular multiclass benchmarks today. Known results show that, in the worst-case, revealing the accuracy of $k$ adaptively chosen classifiers on a data set of size $n$ allows to create a classifier with bias of $\Theta(\sqrt{k/n})$ for any binary prediction problem. We show a new upper bound of $\tilde O(\max\{\sqrt{k\log(n)/(mn)},k/n\})$ on the worst-case bias that any attack can achieve in a prediction problem with $m$ classes. Moreover, we present an efficient attack that achieve a bias of $\Omega(\sqrt{k/(m^2 n)})$ and improves on previous work for the binary setting ($m=2$). We also present an inefficient attack that achieves a bias of $\tilde\Omega(k/n)$. Complementing our theoretical work, we give new practical attacks to stress-test multiclass benchmarks by aiming to create as large a bias as possible with a given number of queries. Our experiments show that the additional uncertainty of prediction with a large number of classes indeed mitigates the effect of our best attacks. Our work extends developments in understanding overfitting due to adaptive data analysis to multiclass prediction problems. It also bears out the surprising fact that multiclass prediction problems are significantly more robust to overfitting when reusing a test (or holdout) dataset. This offers an explanation as to why popular multiclass prediction benchmarks, such as ImageNet, may enjoy a longer lifespan than what intuition from literature on binary classification suggests.
A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter (KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multi-sensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Square-root Cubature Information Filter (MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate information filter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment. Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.
A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities

Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales.
Generalized Scheme Based on Octagon-Shaped Shell for Data Hiding in Steganographic Applications

The exploiting modification direction scheme is a well-known data hiding method because of its high payload and low distortion. Its most criticized drawback is that the secret data must be converted to a non-binary numeral system before the embedding procedure. To overcome this drawback, one study proposed a turtle shell-based scheme for data hiding, in which a reference matrix was constructed based on a hexagon-shaped shell to embed three secret bits into each pixel group of a pair of pixels of the cover image. In a subsequent work, the embedding capacity was increased by using an octagon-shaped shell instead of a hexagon-shaped shell in the reference matrix. The above scheme was extended by utilizing a regular octagon-shaped shell in the reference matrix to reduce the distortion of the worst case. In another approach, the payload of the octagon-shaped-shell-based data hiding scheme was maximized by searching for the appropriate width and height of the octagon. In this study, we generalize the data hiding scheme based on an octagon-shaped shell so that the above-mentioned four schemes can be regarded as specific cases of the proposed method.
Effect of Corruption on Foreign Direct Investment Inflows in Nigeria

Abstract This study investigates the effect of corruption on foreign direct investment inflows in Nigeria, by using some control variables. The study covers a period from 1996 to 2017 and employs Ordinary Least Squares method to perform the multiple regression analysis with the aid of SPSS version 20. The findings indicate that corruption has a significant positive influence on FDI. Though the influence of inflation is significantly negative but exchange rate and Nigeria’s corruption ranking position have insignificant positive impact on FDI. The implication is that the poor legal framework and institutional qualities in Nigeria are helping corruption to thrive in all areas of Nigeria’s economy and might ruin the young generation if nothing is done urgently. The study finds support for helping hand theory of corruption and FDI and also establishes that inflation has a significant negative influence on FDI inflows in the country. Therefore, the study recommends establishment of strong institutional and legal system to curtail the prevailing situation in order to save the future of the country.
Effect of Real-time Physics Organizer Based Smartphone and Indigenous Technology to Students’ Scientific Literacy Viewed from Gender Differences

This study aims to obtain information regarding the effect of the RPO on students' scientific literacy using global scientific literacy questionnaire (GSLQ) in terms of GSLQ dimension and gender differences.The quantitative research approach is used with experimental methods.The U Mann Whitney, Wilcoxon, T-Test, and descriptive statistics were used to determine whether there were differences in students' global scientific literacy between the control group and the experiment group.The results show that there are no significant differences in students' global scientific literacy between the control and experiment groups, nevertheless, the results in the experiment group show that RPO appears to have significant effects, especially in the habits of mind dimension and across students' gender.Male students in the experiment group are able to show better results on habits of mind dimension than female students.However, female students in the experiment group are able to show a higher increase than the students in the control group.
Text Categorization by Learning Predominant Sense of Words as Auxiliary Task

Distributions of the senses of words are often highly skewed and give a strong influence of the domain in a document.This paper follows the assumption and presents a method for text categorization by leveraging the predominant sense of words depending on the domain, i.e., domain-specific senses.The key idea is that the features learned from predominant senses are possible to discriminate the domain of the document and thus improve the overall performance of text categorization.We propose a multi-task learning framework based on the neural network model, transformer, which trains a model to simultaneously categorize documents and predicts a predominant sense for each word.The experimental results using four benchmark datasets including RCV1 show that our method is comparable to the state-of-the-art categorization approach, especially our model works well for categorization of multi-label documents.
An Efficient and Highly Secure Technique to Encrypt and Decrypt Color Images

Digital color images are considered as the most widely used data. They are exchanged frequently on the internet and via email, so an efficient and highly secure method of color image encryption and decryption is needed. Different methods of encryption-decryption are used, but most of them suffer from low efficiency or low-security level or both. In this paper, an efficient and highly secure method of encryption-decryption will be proposed, tested, and implemented. The efficiency parameters will be calculated and compared with other methods’ parameters to prove the efficiency of the proposed method.
A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pruning starts by training a model and then removing redundant parameters while minimizing the impact on what is learned. Alternatively, a recent approach shows that pruning can be done at initialization prior to training, based on a saliency criterion called connection sensitivity. However, it remains unclear exactly why pruning an untrained, randomly initialized neural network is effective. In this work, by noting connection sensitivity as a form of gradient, we formally characterize initialization conditions to ensure reliable connection sensitivity measurements, which in turn yields effective pruning results. Moreover, we analyze the signal propagation properties of the resulting pruned networks and introduce a simple, data-free method to improve their trainability. Our modifications to the existing pruning at initialization method lead to improved results on all tested network models for image classification tasks. Furthermore, we empirically study the effect of supervision for pruning and demonstrate that our signal propagation perspective, combined with unsupervised pruning, can be useful in various scenarios where pruning is applied to non-standard arbitrarily-designed architectures.
A Multilayered Clustering Framework to build a Service Portfolio using Swarm-based algorithms

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Pólya Urn Latent Dirichlet Allocation: A Doubly Sparse Massively Parallel Sampler

Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big corpora that are best analyzed in parallel and distributed computational environments. Indeed, current approaches to parallel inference either don't converge to the correct posterior or require storage of large dense matrices in memory. We present a novel sampler that overcomes both problems, and we show that this sampler is faster, both empirically and theoretically, than previous Gibbs samplers for LDA. We do so by employing a novel Pólya-urn-based approximation in the sparse partially collapsed sampler for LDA. We prove that the approximation error vanishes with data size, making our algorithm asymptotically exact, a property of importance for large-scale topic models. In addition, we show, via an explicit example, that-contrary to popular belief in the topic modeling literature-partially collapsed samplers can be more efficient than fully collapsed samplers. We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.
Centrality-Based Paper Citation Recommender System

Researchers cite papers in order to connect the new research ideas with previous research.For the purpose of finding suitable papers to cite, researchers spend a considerable amount of time and effort.To help researchers in finding relevant/important papers, we evaluated textual and topological similarity measures for citation recommendations.This work analyzes textual and topological similarity measures (i.e., Jaccard and Cosine) to evaluate which one performs well in finding similar papers?To find the importance of papers, we compute centrality measures (i.e., Betweeness, Closeness, Degree and PageRank).After evaluation, it is found that topological-based similarity via Cosine achieved 85.2% and using Jaccard obtained 61.9% whereas textualbased similarity via Cosine on abstract obtained 68.9% and using Cosine on title achieved 37.4% citation links.Likewise, textual-based similarity via Jaccard on abstract obtained 35.4% and using Jaccard on title achieved 28.3% citation links.
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?

Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored. In this paper, we ask the fundamental question of whether Chinese word segmentation (CWS) is necessary for deep learning-based Chinese Natural Language Processing. We benchmark neural word-based models which rely on word segmentation against neural char-based models which do not involve word segmentation in four end-to-end NLP benchmark tasks: language modeling, machine translation, sentence matching/paraphrase and text classification. Through direct comparisons between these two types of models, we find that char-based models consistently outperform word-based models. Based on these observations, we conduct comprehensive experiments to study why word-based models underperform char-based models in these deep learning-based NLP tasks. We show that it is because word-based models are more vulnerable to data sparsity and the presence of out-of-vocabulary (OOV) words, and thus more prone to overfitting. We hope this paper could encourage researchers in the community to rethink the necessity of word segmentation in deep learning-based Chinese Natural Language Processing. \footnote{Yuxian Meng and Xiaoya Li contributed equally to this paper.}
Natural Language Understanding with the Quora Question Pairs Dataset

This paper explores the task Natural Language Understanding (NLU) by looking at duplicate question detection in the Quora dataset. We conducted extensive exploration of the dataset and used various machine learning models, including linear and tree-based models. Our final finding was that a simple Continuous Bag of Words neural network model had the best performance, outdoing more complicated recurrent and attention based models. We also conducted error analysis and found some subjectivity in the labeling of the dataset.
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning

Goal-conditioned policies are used in order to break down complex reinforcement learning (RL) problems by using subgoals, which can be defined either in state space or in a latent feature space. This can increase the efficiency of learning by using a curriculum, and also enables simultaneous learning and generalization across goals. A crucial requirement of goal-conditioned policies is to be able to determine whether the goal has been achieved. Having a notion of distance to a goal is thus a crucial component of this approach. However, it is not straightforward to come up with an appropriate distance, and in some tasks, the goal space may not even be known a priori. In this work we learn a distance-to-goal estimate which is computed in terms of the number of actions that would need to be carried out in a self-supervised approach. Our method solves complex tasks without prior domain knowledge in the online setting in three different scenarios in the context of goal-conditioned policies a) the goal space is the same as the state space b) the goal space is given but an appropriate distance is unknown and c) the state space is accessible, but only a subset of the state space represents desired goals, and this subset is known a priori. We also propose a goal-generation mechanism as a secondary contribution.
Sarcasm Detection on Twitter: User Behavior Approach

Nowadays, people often use social mediaMalave, Nitin platformsDhage, Sudhir N. to express their feelings and opinions of a certain event in a very informal language. Complex ways of expressing opinions by different people make it difficult to determine the actual sentiments. Different elements that influence these sentiments are briefly discussed in this paper. To do this, only the content of that tweet is not enough; there is an emerging need to find some generic approach for sarcasm detection on Twitter. Our proposed framework concentrate not only on insights of tweets but also shades light on important factor of user behavior and its influence on other users. This factor is studied enormously in the field of psychology, but very few have worked on this in the domain of recognizing sarcasm in texts. This suggested method also considers context-based evaluation, based on data acquired from past experience. Context plays important role in determining user behavior, and it should not be ignored while detecting mockery. Our framework proposes to record user behavior pattern and personality traits along with context information. Accessing this information along with existing sarcasm-detection mechanism would help us to achieve generic approach to detect sarcasm on Twitter.
Multiple-copy state discrimination of noisy qubits

Multiple-copy state discrimination is a fundamental task in quantum information processing. If there are two, pure, nonorthogonal states then both local and collective schemes are known to reach the Helstrom bound, the maximum probability of successful discrimination allowed by quantum theory. For mixed states, it is known that only collective schemes can perform optimally, so it might be expected that these schemes are more resilient to preparation noise. We calculate the probability of success for two schemes, a local scheme based on Bayesian updating and quantum data gathering, the simplest possible collective measurement, and consider imperfect preparation fidelity. We find two surprising results. First, both schemes converge upon the same many-copy limit, which is less than unity. Second, the local scheme performs better in all cases. This highlights the point that one should take into account noise when designing state discrimination schemes.
Transferring Adaptive Bit Rate Streaming Quality Models from H.264/HD to H.265/4K-UHD

In this paper the quality of adaptive bit rate video streaming is investigated and two state-of-the-art models, i.e., the NTT audiovisual quality-estimation and ITU-T P.1203 models, are considered. This paper shows how these models can be applied to new conditions, e.g., 4K ultra high definition (4K-UHD) videos encoded using H.265, considering that they were originally designed and trained for HD videos encoded with H.264. Six subjective evaluations involving up to 192 participants and a large variety of test conditions, e.g., durations from 10sec to 3min, coding-quality variation, and stalling events, were conducted on both TV and mobile devices. Using the subjective data, this paper addresses how models and coefficients can be transferred to new conditions. A comparison between state-of-the-art models is conducted, showing the performance of transferred and retrained models. It is found that other video-quality estimation models, such as VMAF, can be used as input of the NTT and ITU-T P.1203 long-term pooling modules, allowing these other video-quality-estimation models to support the specificities of adaptive bit-rate-streaming scenarios. Finally, all retrained coefficients are detailed in this paper allowing future work to directly reuse the results of this study.