Reviewerly/exHarmony huggingface dataset:
Impulse noise reduction in audio signal through multi-stage technique

The fundamental aim of this study is exclusively focused upon detecting and restoring audio signals corrupted by random-valued impulse noise. This study is stemmed from a process in which the noisy samples are in depth analysed through a cascade of stages. In each stage, a vector of different length is used and the similarity degree between the tested sample and its neighbors are precisely measured in each vector. The tested sample which achieves the minimum degree of similarity with its neighboring samples in each stage is highly regarded as an original sample. Several experiments are carried out on different audio sounds to assess the performance of the proposed algorithm in terms of peak signal to noise ratio (PSNR) and sound quality. The attained results have shown that the new method provides remarkable performance if compared to other well-known algorithm, particularly at low noise rates. The method adopted has turned out to be more successful in removing noisy samples and keeping the original ones intact. Also the method in question highlights low computational complexity and easy to implement.
Toward Efficient Image Representation: Sparse Concept Discriminant Matrix Factorization

The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks.
Voice recognition by neuro-heuristic method

The amount of digital data is increasing every day. At every step of our daily lives, we deal with technologies in which our data are stored (e.g., mobile phones and laptops), and this is one of the main reasons for the design of various types of encryption and user identity verification algorithms. These algorithms are meant not only to fulfill the desire of protecting data but also to address the possibility of granting access of specific digital data to selected individuals. This process brings with it the problem of identity verification. This paper discusses the problem of voice verification and presents a voice verification method based on artificial intelligence methods. Numerous tests are performed herein to demonstrate the effectiveness of the presented solution. The research results are shown and discussed in terms of the advantages and disadvantages of the solution.
Percolated quantum walks with a general shift operator: From trapping to transport

We present an alternative definition of discrete-time coined quantum walks convenient for capturing a rather broad spectrum of a walker's behavior on arbitrary graphs. It includes and covers both the geometry of possible walker's positions with interconnecting links and the prescribed rule in which directions the walker will move at each vertex. While the former allows for the analysis of inhomogeneous quantum walks on graphs with vertices of varying degree, the latter offers us to choose, investigate, and compare quantum walks with different shift operators. The synthesis of both key ingredients constitutes a well-suited playground for analyzing percolated quantum walks on a quite general class of graphs. Analytical treatment of the asymptotic behavior of percolated quantum walks is presented and worked out in details for the Grover walk on graphs with maximal degree 3. We find that for these walks with cyclic shift operators, the existence of an edge-3 coloring of the graph allows for nonstationary asymptotic behavior of the walk. For different shift operators, the general structure of localized attractors is investigated, which determines the overall efficiency of a source-to-sink quantum transport across a dynamically changing medium. As a simple nontrivial example of the theory, we treat a single-excitation transport on a percolated cube.
Artificial neuron based on nonlinear polarization rotation in a semiconductor optical amplifier

We experimentally demonstrated an optoelectronic artificial neuron model, which can perform sigmoid transfer function. The proposed optoelectronic circuit can mimic the behavior of continuous sigmoidal neuron based on the nonlinear polarization rotation (NPR) in a single semiconductor optical amplifier (SOA). Because the current-induced NPR is anti-parallel to the optical power-induced NPR, excitatory and inhibitory stimuli to the proposed artificial neuron can be implemented by optical injection and electrical modulation, respectively. The response of the proposed neuron to excitatory inputs and inhibitory inputs are related to the initial state of polarization (SOP), bias current and power of probe beam. By adjusting the initial SOP, the neuron can be more likely to be excited or inhibited.
MR-NET: Exploiting Mutual Relation for Visual Relationship Detection

Inferring the interactions between objects, a.k.a visual relationship detection, is a crucial point for vision understanding, which captures more definite concepts than object detection. Most previous work that treats the interaction between a pair of objects as a one way fail to exploit the mutual relation between objects, which is essential to modern visual application. In this work, we propose a mutual relation net, dubbed MR-Net, to explore the mutual relation between paired objects for visual relationship detection. Specifically, we construct a mutual relation space to model the mutual interaction of paired objects, and employ linear constraint to optimize the mutual interaction, which is called mutual relation learning. Our mutual relation learning does not introduce any parameters, and can adapt to improve the performance of other methods. In addition, we devise a semantic ranking loss to discriminatively penalize predicates with semantic similarity, which is ignored by traditional loss function (e.g., cross entropy with softmax). Then, our MR-Net optimizes the mutual relation learning together with semantic ranking loss with a siamese network. The experimental results on two commonly used datasets (VG and VRD) demonstrate the superior performance of the proposed approach.
Multiple Independent Subspace Clusterings

Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it’s still a challenge for users to analyze and understand the distinctive structure of each output clustering. To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering. To this end, we provide a two-stage approach called MISC (Multiple Independent Subspace Clusterings). In the first stage, MISC uses independent subspace analysis to seek multiple and statistical independent (i.e. non-redundant) subspaces, and determines the number of subspaces via the minimum description length principle. In the second stage, to account for the intrinsic geometric structure of samples embedded in each subspace, MISC performs graph regularized semi-nonnegative matrix factorization to explore clusters. It additionally integrates the kernel trick into matrix factorization to handle non-linearly separable clusters. Experimental results on synthetic datasets show that MISC can find different interesting clusterings from the sought independent subspaces, and it also outperforms other related and competitive approaches on real-world datasets.
Safe Policy Improvement with Baseline Bootstrapping in Factored Environments

We present a novel safe reinforcement learning algorithm that exploits the factored dynamics of the environment to become less conservative. We focus on problem settings in which a policy is already running and the interaction with the environment is limited. In order to safely deploy an updated policy, it is necessary to provide a confidence level regarding its expected performance. However, algorithms for safe policy improvement might require a large number of past experiences to become confident enough to change the agent’s behavior. Factored reinforcement learning, on the other hand, is known to make good use of the data provided. It can achieve a better sample complexity by exploiting independence between features of the environment, but it lacks a confidence level. We study how to improve the sample efficiency of the safe policy improvement with baseline bootstrapping algorithm by exploiting the factored structure of the environment. Our main result is a theoretical bound that is linear in the number of parameters of the factored representation instead of the number of states. The empirical analysis shows that our method can improve the policy using a number of samples potentially one order of magnitude smaller than the flat algorithm.
Deep Convolutional Sum-Product Networks

We give conditions under which convolutional neural networks (CNNs) define valid sum-product networks (SPNs). One subclass, called convolutional SPNs (CSPNs), can be implemented using tensors, but also can suffer from being too shallow. Fortunately, tensors can be augmented while maintaining valid SPNs. This yields a larger subclass of CNNs, which we call deep convolutional SPNs (DCSPNs), where the convolutional and sum-pooling layers form rich directed acyclic graph structures. One salient feature of DCSPNs is that they are a rigorous probabilistic model. As such, they can exploit multiple kinds of probabilistic reasoning, including marginal inference and most probable explanation (MPE) inference. This allows an alternative method for learning DCSPNs using vectorized differentiable MPE, which plays a similar role to the generator in generative adversarial networks (GANs). Image sampling is yet another application demonstrating the robustness of DCSPNs. Our preliminary results on image sampling are encouraging, since the DCSPN sampled images exhibit variability. Experiments on image completion show that DCSPNs significantly outperform competing methods by achieving several state-of-the-art mean squared error (MSE) scores in both left-completion and bottom-completion in benchmark datasets.
MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.
Adversarial Binary Collaborative Filtering for Implicit Feedback

Fast item recommendation based on implicit feedback is vital in practical scenarios due to data-abundance, but challenging because of the lack of negative samples and the large number of recommended items. Recent adversarial methods unifying generative and discriminative models are promising, since the generative model, as a negative sampler, gradually improves as iteration continues. However, binary-valued generative model is still unexplored within the min-max framework, but important for accelerating item recommendation. Optimizing binary-valued models is difficult due to non-smooth and nondifferentiable. To this end, we propose two novel methods to relax the binarization based on the error function and Gumbel trick so that the generative model can be optimized by many popular solvers, such as SGD and ADMM. The binary-valued generative model is then evaluated within the min-max framework on four real-world datasets and shown its superiority to competing hashing-based recommendation algorithms. In addition, our proposed framework can approximate discrete variables precisely and be applied to solve other discrete optimization problems.
Attentive Temporal Pyramid Network for Dynamic Scene Classification

Dynamic scene classification is an important yet challenging problem especially with the presence of defected or irrelevant frames due to unconstrained imaging conditions such as illumination, camera motion and irrelevant background. In this paper, we propose the attentive temporal pyramid network (ATP-Net) to establish effective representations of dynamic scenes by extracting and aggregating the most informative and discriminative features. The proposed ATP-Net detects informative features of frames that contain the most relevant information to scenes by a temporal pyramid structure with the incorporated attention mechanism. These frame features are effectively fused by a newly designed kernel aggregation layer based on kernel approximation into a discriminative holistic representations of dynamic scenes. The proposed ATP-Net leverages the strength of attention mechanism to select the most relevant frame features and the ability of kernels to achieve optimal feature fusion for discriminative representations of dynamic scenes. Extensive experiments and comparisons are conducted on three benchmark datasets and the results show our superiority over the state-of-the-art methods on all these three benchmark datasets.
Hybrid spatiotemporal architectures for universal linear optics

We present two hybrid linear-optical architectures that simultaneously exploit spatial and temporal degrees of freedom of light to effect arbitrary discrete unitary transformations. Our architectures combine the benefits of spatial implementations of linear optics, namely, low loss and parallel operation, with those of temporal implementations, namely, modest resource requirements and access to transformations of potentially unbounded size. We arrive at our architectures by devising and employing decompositions of large discrete unitary transformations into smaller ones, decompositions we expect to have broad utility beyond spatiotemporal linear optics. We show that hybrid architectures promise important advantages over both spatial-only and temporal-only architectures.
Distributed PageRank Computation: An Improved Theoretical Study

PageRank is a classic measure that effectively evaluates the node importance in large graphs, and has been applied in numerous applications ranging from data mining, Web algorithms, recommendation systems, load balancing, search, and identifying connectivity structures. Computing PageRank for large graphs is challenging and this has motivated the studies of distributed algorithms to compute PageRank. Previously, little works have been spent on the distributed PageRank algorithms with provably desired complexity and accuracy. Given a graph with n nodes and if we model the distributed computation model as the well-known congested clique model, the state-of-the-art algorithm takes O(√logn) communication rounds to approximate the PageRank value of each node in G, with a probability at least 1−1/n. In this paper, we present improved distributed algorithms for computing PageRank. Particularly, our algorithm performs O(log log√n) rounds (a significant improvement compared with O(√logn) rounds) to approximate the PageRank values with a probability at least 1−1/n. Moreover, under a reasonable assumption, our algorithm also reduces the edge bandwidth (i.e., the maximum communication message size that can be exchanged through an edge during a communication round) by a O(logn) factor compared with the state-of-the-art algorithm. Finally, we show that our algorithm can be adapted to efficiently compute another variant of PageRank, i.e., the batch one-hop Personalized PageRanks, in O(log logn) communication rounds.
LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition

Activity recognition is central to many motion analysis applications ranging from health assessment to gaming. However, the need for obtaining sufficiently large amounts of labeled data has limited the development of personalized activity recognition models. Semi-supervised learning has traditionally been a promising approach in many application domains to alleviate reliance on large amounts of labeled data by learning the label information from a small set of seed labels. Nonetheless, existing approaches perform poorly in highly dynamic settings, such as wearable systems, because some algorithms rely on predefined hyper-parameters or distribution models that needs to be tuned for each user or context. To address these challenges, we introduce LabelForest 1, a novel non-parametric semi-supervised learning framework for activity recognition. LabelForest has two algorithms at its core: (1) a spanning forest algorithm for sample selection and label inference; and (2) a silhouette-based filtering method to finalize label augmentation for machine learning model training. Our thorough analysis on three human activity datasets demonstrate that LabelForest achieves a labeling accuracy of 90.1% in presence of a skewed label distribution in the seed data. Compared to self-training and other sequential learning algorithms, LabelForest achieves up to 56.9% and 175.3% improvement in the accuracy on balanced and unbalanced seed data, respectively.
Learning Multi-Task Communication with Message Passing for Sequence Learning

We present two architectures for multi-task learning with neural sequence models. Our approach allows the relationships between different tasks to be learned dynamically, rather than using an ad-hoc pre-defined structure as in previous work. We adopt the idea from message-passing graph neural networks, and propose a general graph multi-task learning framework in which different tasks can communicate with each other in an effective and interpretable way. We conduct extensive experiments in text classification and sequence labelling to evaluate our approach on multi-task learning and transfer learning. The empirical results show that our models not only outperform competitive baselines, but also learn interpretable and transferable patterns across tasks.
The Pure Price of Anarchy of Pool Block Withholding Attacks in Bitcoin Mining

Bitcoin, a cryptocurrency built on the blockchain data structure, has generated significant academic and commercial interest. Contrary to prior expectations, recent research has shown that participants of the protocol (the so-called “miners”) are not always incentivized to follow the protocol. We study the game induced by one such attack – the pool block withholding attack – in which mining pools (groups of miners) attack other mining pools. We focus on the case of two pools attacking each other, with potentially other mining power in the system.We show that this game always admits a pure Nash equilibrium, and its pure price of anarchy, which intuitively measures how much computational power can be wasted due to attacks in an equilibrium, is at most 3. We conjecture, and prove in special cases, that it is in fact at most 2. Our simulations provide compelling evidence for this conjecture, and show that players can quickly converge to the equilibrium by following best response strategies.
A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability

The cervical cancer developing from the precancerous lesions caused by the Human Papilloma Virus (HPV) has been one of the preventable cancers with the help of periodic screening. There are two types of grading conventions widely accepted among pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is introduced. The papillae of the cervical epithelium and overlapping cell problems are handled and a tissue classification method with a novel morphological feature exploiting the relative orientation between the BM and the major axis of all nuclei is developed and its performance is evaluated. Besides, the inter-observer variability is also revealed by a thorough comparison among pathologists' decisions, as well as, the final diagnosis.
Provable Data Integrity of Cloud Storage Service With Enhanced Security in the Internet of Things

Utilizing the cloud storage services to replace the conventional storage paradigms in the Internet of Things is becoming popular due to the huge amount of data produced by users. However, guaranteeing data integrity on the cloud is challenging since data owners would lose control of the outsourced data when subscribing the cloud storage services. There have been a lot of Provable Data Possession and Proof of Retrievability schemes proposed to check the data integrity on the cloud. These schemes additionally address the data updating or retrievability problems. Nevertheless, the majority of existing schemes handle only one data block at a time when dealing with the data updates, which is inefficient for the real-time and highly secure services. In order to overcome the efficiency limitation, we propose a new scheme to support the simultaneous updates of multiple data blocks. Specifically, a balanced data structure—SGMHT—an extension of the Merkle Hash Tree (MHT) based on scapegoat tree, is constructed for the proposed goal of the scheme. The scheme also employs an erasure-coded hierarchical log structure to support the delayed update of multiple blocks and the data retrievability. In addition, homomorphic tags are used to reduce the amount of data transmission and enhance the efficiency of data updates. We formally analyze the security of the proposed scheme. The results of the experiments show that our scheme outperforms the existing schemes that are addressing the single block updates in terms of efficiency.