Reviewerly/exHarmony huggingface dataset:
Human and Machine Recognition of Transportation Modes from Body-Worn Camera Images

Computer vision techniques applied on images opportunistically captured from body-worn cameras or mobile phones offer tremendous potential for vision-based context awareness. In this paper, we evaluate the potential to recognise the modes of locomotion and transportation of mobile users, by analysing single images captured by body-worn cameras. We evaluate this with the publicly available Sussex-Huawei Locomotion and Transportation Dataset, which includes 8 transportation and locomotion modes performed over 7 months by 3 users. We present a baseline performance obtained through crowd sourcing using Amazon Mechanical Turk. Humans infered the correct modes of transportations from images with an F1-score of 52%. The performance obtained by five state-of-the-art Deep Neural Networks (VGG16, VGG19, ResNet50, MobileNet and DenseNet169) on the same task was always above 71.3% F1-score. We characterise the effect of partitioning the training data to fine-tune different number of blocks of the deep networks and provide recommendations for mobile implementations.
Development of an Information-Based Online Foreign Language Teaching Platform with ASP.NET

The online teaching platform is a major application of information-based educa-tion. If adopted for foreign language teaching, the platform can greatly facilitate the utilization of network resources and enhance the students’ abilities in listen-ing, speaking, reading and writing. This paper develops an information-based online foreign language teaching platform with the aid of ASP.NET. The SQL was selected as the backend database and accessed via ADO.NET. Taking the browser/server (B/S) structure was adopted as the network architecture, the plat-form was programmed in C# and realized using Microsoft Visual Studio (VS) 2005. Through testing, the proposed platform was proved to achieve all expected functions and satisfy the demand of college English teaching in a safe and stable manner. The research findings promote the quality and informatization of English teaching.
Multi-Modal Classification of Polyp Malignancy using CNN Features with Balanced Class Augmentation

Colorectal polyps are an indicator of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models albeit with limited success. In this paper, a classification approach is proposed to differentiate between polyp malignancy, using features extracted from the Global Average Pooling (GAP) layer of a Convolutional Neural Network (CNNs). Two recent endoscopic modalities are used to improve the algorithm prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle an unbalanced class distribution and to improve the decision of the classifiers. As a result, we increase the performance compared to state-of-the-art methods (0.97 vs 0.90 AUC). Our method for automatic polyp malignancy classification facilitates future advances towards patient safety and may avoid time-consuming and costly histopathological assessment.
Research on Privacy Preserving Data Mining

In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation. The existing problems and directions for future research are also discussed.
FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN.
Efficient Implementation of 2D and 3D Sparse Deconvolutional Neural Networks with a Uniform Architecture on FPGAs

Three-dimensional (3D) deconvolution is widely used in many computer vision applications. However, most previous works have only focused on accelerating two-dimensional (2D) deconvolutional neural networks (DCNNs) on Field-Programmable Gate Arrays (FPGAs), while the acceleration of 3D DCNNs has not been well studied in depth as they have higher computational complexity and sparsity than 2D DCNNs. In this paper, we focus on the acceleration of both 2D and 3D sparse DCNNs on FPGAs by proposing efficient schemes for mapping 2D and 3D sparse DCNNs on a uniform architecture. Firstly, a pruning method is used to prune unimportant network connections and increase the sparsity of weights. After being pruned, the number of parameters of DCNNs is reduced significantly without accuracy loss. Secondly, the remaining non-zero weights are encoded in coordinate (COO) format, reducing the memory demands of parameters. Finally, to demonstrate the effectiveness of our work, we implement our accelerator design on the Xilinx VC709 evaluation platform for four real-life 2D and 3D DCNNs. After the first two steps, the storage required of DCNNs is reduced up to 3.9×. Results show that the performance of our method on the accelerator outperforms that of the our prior work by 2.5× to 3.6× in latency.
AGRM: Attention-Based Graph Representation Model for Telecom Fraud Detection

Fraud detection is an increasingly important and difficult task in the modern technological environment. Existing classification methods for fraud detection are based on the call behavior attributes, such as duration and frequency of callings. With the evolution of the fraud mode, the methods based on self-attributes cannot detect the fraudsters who camouflage themselves by behaving like normal accounts. So we propose an Attention-based Graph Representation learning Model(AGRM), which takes into account the node's self-attributes and its context information. In our model, an attention architecture is introduced to learn the discriminative representation by focusing on the most informative context. On this basis, we design a self-attention mechanism to adjust the contribution of the node's self-attributes and its neighbors'. Extensive experiments show that the proposed method achieved significant accuracy improvement compared with existing fraud detection methods.
The perfect marriage and much more: Combining dimension reduction, distance measures and covariance

We develop a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions of random variables, and the Johnson–Lindenstrauss Lemma, a technique for dimension reduction. The resulting technique is a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. The degree to which different entities, (markets, universities, hospitals, cities, groups of securities, etc.), have different distance measures of their corresponding distributions tells us the extent to which they are different, aiding participants looking for diversification or looking for more of the same thing. We demonstrate a relationship between covariance and distance measures based on a generic extension of Stein's Lemma. We consider an asset pricing application and then briefly discuss how this methodology lends itself to numerous market–structure studies and even applications outside the realm of finance / social sciences by illustrating a biological application. We provide numerical illustrations using security prices, volumes and volatilities of both these variables from six different countries.
Iterative Classroom Teaching

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using O(min{d,N}log 1/ɛ) exam-ples, where d is the ambient dimension of the problem, N is the number of learners, and ɛ is the accuracy parameter. We show the robustness of our teaching strategy when the teacher has limited knowledge of the learners’ internal dynamics as provided by a noisy oracle. Further, we study the trade-off between the learners’ workload and the teacher’s cost in teaching the target concept. Our experiments validate our theoretical results and suggest that appropriately partitioning the classroom into homogenous groups provides a balance between these two objectives.
Sequential Gating Ensemble Network for Noise Robust Multiscale Face Restoration

Face restoration from low resolution and noise is important for applications of face analysis recognition. However, most existing face restoration models omit the multiple scale issues in the face restoration problem, which is still not well solved in the research area. In this paper, we propose a sequential gating ensemble network (SGEN) for a multiscale noise robust face restoration issue. To endow the network with multiscale representation ability, we first employ the principle of ensemble learning for SGEN network architecture design. The SGEN aggregates multilevel base-encoders and base-decoders into the network, which enables the network to contain multiple scales of receptive field. Instead of combining these base-en/decoders directly with nonsequential operations, the SGEN takes base-en/decoders from different levels as sequential data. Specifically, it is visualized that SGEN learns to sequentially extract high-level information from base-encoders in a bottom-up manner and restore low-level information from base-decoders in a top-down manner. Besides, we propose realizing bottom-up and top-down information combination and selection with a sequential gating unit (SGU). The SGU sequentially takes information from two different levels as inputs and decides the output based on one active input. Experimental results on the benchmark dataset demonstrate that our SGEN is more effective at multiscale human face restoration with more image details and less noise than state-of-the-art image restoration models. Further utilizing an adversarial training scheme, SGEN also produces more visually preferred results than other models under subjective evaluation.
A classification of semi-fragile watermarking systems for JPEG images

The article discusses semi-fragile digital watermarking systems designed to protect JPEG images against unauthorized changes. These systems allow detecting and locating changes, and some of them are able to restore the original content. Formal schemes describing the procedures for watermark embedding and authentication are given. We consider more than a dozen systems of this type and classify them according to various criteria. We also consider the results of experimental studies of these systems estimating their level of distortions arising from watermark embedding. In addition, we test the systems whether they are able to operate in the semi-fragile way.
Natural Option Critic

The recently proposed option-critic architecture (Bacon, Harb, and Precup 2017) provides a stochastic policy gradient approach to hierarchical reinforcement learning. Specifically, it provides a way to estimate the gradient of the expected discounted return with respect to parameters that define a finite number of temporally extended actions, called options. In this paper we show how the option-critic architecture can be extended to estimate the natural gradient (Amari 1998) of the expected discounted return. To this end, the central questions that we consider in this paper are: 1) what is the definition of the natural gradient in this context, 2) what is the Fisher information matrix associated with an option’s parameterized policy, 3) what is the Fisher information matrix associated with an option’s parameterized termination function, and 4) how can a compatible function approximation approach be leveraged to obtain natural gradient estimates for both the parameterized policy and parameterized termination functions of an option with per-time-step time and space complexity linear in the total number of parameters. Based on answers to these questions we introduce the natural option critic algorithm. Experimental results showcase improvement over the vanilla gradient approach.
Reviewer assignment based on sentence pair modeling

Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is an important task in the academic community. Recent researches have focused mainly on text processing methods based on natural language processing, such as topic model, word embedding and so on. However, it is difficult for the computer to understand the research fields of reviewers and manuscripts. In this paper, a novel supervisory information that is expressed as sentence pairs constructed by titles and abstracts is adopted to solve reviewer assignment problem. We propose a sentence pair modeling-based reviewer assignment (SPM-RA) method, which models the relationship of sentence pairs by supervising information. The supervisory information makes the model accurately learn the field features of reviewers and manuscripts. Firstly, we construct the training set by the field relationship between title and abstract. We use TF-IDF sampling to solve the problem of unbalanced data set. Then, we use neural network models, such as the BERT (bidirectional encoder representations from transformers), CNN (convolution neural network), biLSTM (bidirectional long short term memory), or various combinations of them to do modeling sentence pair and learn the field features of the paper through the training set. Finally, we predict the similarity between reviewers and manuscripts by training model. We evaluate SPM-RA on two real datasets and compare its performance to that of seven existing methods. The experimental results show that SPM-RA improves the recommendation precision by at least 18% on the public dataset.
Combining active learning and local patch alignment for data-driven facial animation with fine-grained local detail

Data-driven facial animation has attracted considerable attention from both academic and industrial communities in recent years. Typically, the motion data used to animate the faces are derived from either 3D or 2D facial features whose positions on the face are determined according to experience. There still lacks an automatic approach to determine the optimal positions of the features to face deformation, and current face deformation methods are incapable of providing fine-grained local geometric characteristics. This paper proposes a novel scheme for face animation in which an active learning method based on Locally Linear Reconstruction algorithm is exploited to determine the optimal feature positions on the face for face deformation, and the Semi-Supervised Local Patch Alignment algorithm is subsequently used to deform the face with the selected features according to the optimal feature positions. The active learning model can be solved by a sequential approach, and the Semi-Supervised Local Patch Alignment model can be addressed by a least-square method. Experimental results on various types of faces demonstrate the superiority of the proposed scheme to existing approaches in both feature points selection and fine-grained local characteristics preservation.
Adversarial Label Learning

We consider the task of training classifiers without labels. We propose a weakly supervised method—adversarial label learning—that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose. The method therefore minimizes an upper bound of the classifier’s error rate using projected primal-dual subgradient descent. Minimizing this bound protects against bias and dependencies in the weak supervision. Experiments on real datasets show that our method can train without labels and outperforms other approaches for weakly supervised learning.
Online estimation of the asymptotic variance for averaged stochastic gradient algorithms

Stochastic gradient algorithms are more and more studied since they can deal efficiently and online with large samples in high dimensional spaces. In this paper, we first establish a Central Limit Theorem for these estimates as well as for their averaged version in general Hilbert spaces. Moreover, since having the asymptotic normality of estimates is often unusable without an estimation of the asymptotic variance, we introduce a new recursive algorithm for estimating this last one, and we establish its almost sure rate of convergence as well as its rate of convergence in quadratic mean. Finally, two examples consisting in estimating the parameters of the logistic regression and estimating geometric quantiles are given.
Designing digital filter banks using wavelets

In digital filters theory, filtering techniques generally deal with pole-zero structures. In this context, filtering schemes, such as infinite impulse response (IIR) filters, are described by linear differential equations or linear transformations, in which the impulse response of each filter provides its complete characterization, under filter design specifications. On the other hand, finite impulse response (FIR) digital filters are more flexible than the analog ones, yielding higher quality factors. Since many approaches to the circuit synthesis using the wavelet transform have been recently proposed, here we present a digital filter design algorithm, based on signal wavelet decomposition, which explores the energy partitioning among frequency sub-bands. Exploring such motivation, the method involves the design of a perfect reconstruction wavelet filter bank, of a suitable choice of roots in the Z-plane, through a spectral factorization, exploring the orthogonality and localization property of the wavelet functions. This approach resulted in an energy partitioning across scales of the wavelet transform that enabled a superior filtering performance, in terms of its behavior on the pass and stop bands. This algorithm presented superior results when compared to windowed FIR digital filter design, in terms of the intended behavior in its transition band. Simulations of the filter impulse response for the proposed method are presented, displaying the good behavior of the method with respect to the transition bandwidth of the involved filters.
On DNA Signatures, Their Dual-Use Potential for GMO Counterfeiting, and a Cyber-Based Security Solution

This study investigates the role and functionality of special nucleotide sequences ("DNA signatures") to detect the presence of an organism and to distinguish it from all others. After highlighting vulnerabilities of the prevalent DNA signature paradigm for the identification of agricultural genetically modified (GM) organisms it will be argued that these so-called signatures really are no signatures at all - when compared to the notion of traditional (handwritten) signatures and their generalizations in the modern (digital) world. It is suggested that a recent contamination event of an unauthorized GM Bacillus subtilis strain (Paracchini et al., 2017) in Europe could have been-or the same way could be - the consequence of exploiting gaps of prevailing DNA signatures. Moreover, a recent study (Mueller, 2019) proposes that such DNA signatures may intentionally be exploited to support the counterfeiting or even weaponization of GM organisms (GMOs). These concerns mandate a re-conceptualization of how DNA signatures need to be realized. After identifying central issues of the new vulnerabilities and overlying them with practical challenges that bio-cyber hackers would be facing, recommendations are made how DNA signatures may be enhanced. To overcome the core problem of signature transferability in bioengineered mediums, it is necessary that the identifier needs to remain secret during the entire verification process. On the other hand, however, the goal of DNA signatures is to enable public verifiability, leading to a paradoxical dilemma. It is shown that this can be addressed with ideas that underlie special cryptographic signatures, in particular those of "zero-knowledge" and "invisibility." This means more than mere signature hiding, but relies on a knowledge-based proof and differentiation of a secret (here, as assigned to specific clones) which can be realized without explicit demonstration of that secret. A re-conceptualization of these principles can be used in form of a combined (digital and physical) method to establish confidentiality and prevent un-impersonation of the manufacturer. As a result, this helps mitigate the circulation of possibly hazardous GMO counterfeits and also addresses the situation whereby attackers try to blame producers for deliberately implanting illicit adulterations hidden within authorized GMOs.
Print vs Digital Reading Comprehension in EFL: A Literature Review

Printed texts have long been used as the prime medium of learning to read and reading to learn. However, the ubiquity of technology has emerged digital texts, and the accelerating influx of digital texts requires new comprehension skills and strategies. This article reviews and synthesizes current ideas and research findings on digital reading in EFL context to provide a more solid theoretical basis for digital texts use in reading comprehension programs. Discussion in this article begins with the comparison of the nature of conventional or print reading versus digital reading and the characteristics of printed texts versus digital texts. After that, the discussion proceeds to the findings of relevant studies concerning the effect of digital reading to comprehension, students and instructors' perception of digital texts, and strategies for reading digital texts.
Tree-Structured Neural Networks With Topic Attention for Social Emotion Classification

Social emotion classification studies the emotion distribution evoked by an article among numerous readers. Although recently neural network-based methods can improve the classification performance compared with the previous word-emotion and topic-emotion approaches, they have not fully utilized some important sentence language features and document topic features. In this paper, we propose a new neural network architecture exploiting both the syntactic information of a sentence and topic distribution of a document. The proposed architecture first constructs a tree-structured long short-term memory (Tree-LSTM) network based on the sentence syntactic dependency tree to obtain a sentence vector representation. For a multi-sentence document, we then use a Chain-LSTM network to obtain the document representation from its sentences’ hidden states. Furthermore, we design a topic-based attention mechanism with two attention levels. The word-level attention is used for weighting words of a single-sentence document and the sentence-level attention for weighting sentences of a multi-sentence document. The experiments on three public datasets show that the proposed scheme outperforms the state-of-the-art ones in terms of higher average Pearson correlation coefficient and MicroF1 performance.