Reviewerly/exHarmony huggingface dataset:
Decentralized Knowledge Acquisition for Mobile Internet Applications

Mobile internet applications on smart phones dominate large portions of daily life for many people. Conventional machine learning-based knowledge acquisition methods collect users’ data in a centralized server, then train an intelligent model, such as recommendation and prediction, using all the collected data. This knowledge acquisition method raises serious privacy concerns, and also violates the rules of the newly published General Data Protection Regulation. This paper proposes a new attention-augmented federated learning framework that can conduct decentralized knowledge acquisition for mobile Internet application scenarios, such as mobile keyboard suggestions. In particular, the attention mechanism aggregates the decentralized knowledge which has been acquired from each mobile using its own data locally. The centralized server aggregates knowledge without direct access to personal data. Experiments on three real-world datasets demonstrate that the proposed framework performs better than other baseline methods in terms of perplexity and communication cost.
Incremental and decremental fuzzy bounded twin support vector machine

In this paper, we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with large datasets and to learn from data streams. We combine the TWSVM with a fuzzy membership function, so that each input has a different contribution to each hyperplane in a binary classifier. To solve the pair of quadratic programming problems (QPPs), we use a dual coordinate descent algorithm with a shrinking strategy, and to obtain a robust classification with a fast training we propose the use of a Fourier Gaussian approximation function with our linear FBTWSVM. Inspired by the shrinking technique, the incremental algorithm re-utilizes part of the training method with some heuristics, while the decremental procedure is based on a scoring window. The FBTWSVM is also extended for multi-class problems by combining binary classifiers using a Directed Acyclic Graph (DAG) approach. Moreover, we analyzed the theoretical foundation's properties of the proposed approach and its extension, and the experimental results on benchmark datasets indicate that the FBTWSVM has a fast training and retraining process while maintaining a robust classification performance.
Efficient Neural Interaction Function Search for Collaborative Filtering

In collaborative filtering (CF), interaction function (IFC) play the important role of capturing interactions among items and users. The most popular IFC is the inner product, which has been successfully used in low-rank matrix factorization. However, interactions in real-world applications can be highly complex. Thus, other operations (such as plus and concatenation), which may potentially offer better performance, have been proposed. Nevertheless, it is still hard for existing IFCs to have consistently good performance across different application scenarios. Motivated by the recent success of automated machine learning (AutoML), we propose in this paper the search for simple neural interaction functions (SIF) in CF. By examining and generalizing existing CF approaches, an expressive SIF search space is designed and represented as a structured multi-layer perceptron. We propose an one-shot search algorithm that simultaneously updates both the architecture and learning parameters. Experimental results demonstrate that the proposed method can be much more efficient than popular AutoML approaches, can obtain much better prediction performance than state-of-the-art CF approaches, and can discover distinct IFCs for different data sets and tasks.1
Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation

Visual aesthetics of products plays an important role in the decision process when purchasing appearance-first products, e.g., clothes. Indeed, user’s aesthetic preference, which serves as a personality trait and a basic requirement, is domain independent and could be used as a bridge between domains for knowledge transfer. However, existing work has rarely considered the aesthetic information in product images for cross-domain recommendation. To this end, in this paper, we propose a new deep Aesthetic Cross-Domain Networks (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains. Specifically, we first leverage an aesthetic network to extract aesthetic features. Then, we integrate these features into a cross-domain network to transfer users’ domain independent aesthetic preferences. Moreover, network cross-connections are introduced to enable dual knowledge transfer across domains. Finally, the experimental results on real-world datasets show that our proposed model ACDN outperforms benchmark methods in terms of recommendation accuracy.
Latent Linear Critiquing for Conversational Recommender Systems

Critiquing is a method for conversational recommendation that iteratively adapts recommendations in response to user preference feedback. In this setting, a user is iteratively provided with an item recommendation and attribute description for that item; a user may either accept the recommendation, or critique the attributes in the item description to generate a new recommendation. Historical critiquing methods were largely based on explicit constraint- and utility-based methods for modifying recommendations w.r.t. critiqued item attributes. In this paper, we revisit the critiquing approach in the era of recommendation methods based on latent embeddings with subjective item descriptions (i.e., keyphrases from user reviews). Two critical research problems arise: (1) how to co-embed keyphrase critiques with user preference embeddings to update recommendations, and (2) how to modulate the strength of multi-step critiquing feedback, where critiques are not necessarily independent, nor of equal importance. To address (1), we build on an existing state-of-the-art linear embedding recommendation algorithm to align review-based keyphrase attributes with user preference embeddings. To address (2), we exploit the linear structure of the embeddings and recommendation prediction to formulate a linear program (LP) based optimization problem to determine optimal weights for incorporating critique feedback. We evaluate the proposed framework on two recommendation datasets containing user reviews with simulated users. Empirical results compared to a standard approach of averaging critique feedback show that our approach reduces the number of interactions required to find a satisfactory item and increases the overall success rate.
Culture in the design of mHealth UI

Purpose Designers of mobile applications have long understood the importance of users’ preferences in making the user experience easier, convenient and therefore valuable. The cultural aspects of groups of users are among the key features of users’ design preferences, because each group’s preferences depend on various features that are culturally compatible. The process of integrating culture into the design of a system has always been an important ingredient for effective and interactive human computer interface. This study aims to investigate the design of a mobile health (mHealth) application user interface (UI) based on Arabic culture. It was argued that integrating certain cultural values of specific groups of users into the design of UI would increase their acceptance of the technology. Design/methodology/approach A total of 135 users responded to an online survey about their acceptance of a culturally designed mHealth. Findings The findings showed that culturally based language, colours, layout and images had a significant relationship with users’ behavioural intention to use the culturally based mHealth UI. Research limitations/implications First, the sample and the data collected of this study were restricted to Arab users and Arab culture; therefore, the results cannot be generalized to other cultures and users. Second, the adapted unified theory of acceptance and use of technology model was used in this study instead of the new version, which may expose new perceptions. Third, the cultural aspects of UI design in this study were limited to the images, colours, language and layout. Practical implications It encourages UI designers to implement the relevant cultural aspects while developing mobile applications. Originality/value Embedding Arab cultural aspects in designing UI for mobile applications to satisfy Arab users and enhance their acceptance toward using mobile applications, which will reflect positively on their lives.
Multi-way backpropagation for training compact deep neural networks

Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet (He et al., 2016), we are able to train very deep CNNs as the gradient vanishing issue has been largely addressed by the introduction of skip connections. However, we observe that, when the depth is very large, the intermediate layers (especially shallow layers) may fail to receive sufficient supervision from the loss due to severe transformation through long backpropagation path. As a result, the representation power of intermediate layers can be very weak and the model becomes very redundant with limited performance. In this paper, we first investigate the supervision vanishing issue in existing backpropagation (BP) methods. And then, we propose to address it via an effective method, called Multi-way BP (MW-BP), which relies on multiple auxiliary losses added to the intermediate layers of the network. The proposed MW-BP method can be applied to most deep architectures with slight modifications, such as ResNet and MobileNet. Our method often gives rise to much more compact models (denoted by "Mw+Architecture") than existing methods. For example, MwResNet-44 with 44 layers performs better than ResNet-110 with 110 layers on CIFAR-10 and CIFAR-100. More critically, the resultant models even outperform the light models obtained by state-of-the-art model compression methods. Last, our method inherently produces multiple compact models with different depths at the same time, which is helpful for model selection. Extensive experiments on both image classification and face recognition demonstrate the superiority of the proposed method.
On Minimum Discrepancy Estimation for Deep Domain Adaptation

In the presence of large sets of labeled data, Deep LearningDeep learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classificationClassification and recognition tasks. However, DL cannot always perform well when the training and testing images come from different distributions or in the presence of domain shift between training and testing images. They also suffer in the absence of labeled input data. Domain adaptationDomain adaptation (DA) methods have been proposed to make up the poor performance due to domain shift. In this chapter, we present a new unsupervised deep domain adaptationDomain adaptation method based on the alignment of second-order statistics (covariances) as well as maximum mean discrepancy of the source and target data with a two-stream Convolutional Neural Network (CNN). We demonstrate the ability of the proposed approach to achieve state-of-the-art performance for imageImage classification classificationClassification on three benchmark domain adaptationDomain adaptation datasets: Office-31 [27], Office-Home [37] and Office-Caltech [8].
Digital literacy of EFL students in a junior high school in Iran: voices of teachers, students and Ministry Directors

Promoting students’ digital literacy has become a significant challenge for educational authorities and course designers. However, very limited attention has been directed toward junior high school students’ digital literacy in the literature of computer-assisted language learning (CALL). Thus, this mixed-methods study explored junior high school teachers’ and students’ perspectives on the students’ digital literacy level and issues pertaining to it. A total of 364 junior high school students, 20 junior high school teachers, and three Directors of the Ministry of Education of Iran participated in the study. Exploratory factor analysis was run to ensure the construct validity of the questionnaire. The results of the Mann–Whitney U test showed significant differences between the perceptions of the teachers and students. While the students and teachers reported that students had an acceptable level of digital literacy in the interviews, the questionnaire results showed that the students had a low to moderate level of digital literacy. The teachers and students believed that the junior high school students used technology for recreational and non-educational purposes. The junior high school students did not use a wide range of computer applications and software tools. The interview with the Directors of the Ministry of Education depicted that they did not have consensus on the issues related to junior high school students’ digital literacy and that the Ministry did not have clear plans for promoting students’ digital literacy levels. The study proposes implications for the renewal of the English as a foreign language (EFL) curriculum of junior high schools and the integration of CALL in EFL programs in Iran and other similar contexts.
Optical Flow Estimation in the Deep Learning Age

Akin to many subareas of computer vision, the recent advances in deep learningDeep learning have also significantly influenced the literature on optical flowOptical flow. Previously, the literature had been dominated by classical energy-based models, which formulate optical flowOptical flow estimation as an energy minimization problem. However, as the practical benefits of Convolutional Neural Networks (CNNs)Convolutional Neural Networks (CNNs) over conventional methods have become apparent in numerous areas of computer vision and beyond, they have also seen increased adoption in the contextContext of motion estimation to the point where the current state of the art in terms of accuracy is set by CNN approaches. We first review this transition as well as the developments from early work to the current state of CNNsConvolutional Neural Networks (CNNs) for optical flowOptical flow estimation. Alongside, we discuss some of their technical details and compare them to recapitulate which technical contribution led to the most significant accuracy improvements. Then we provide an overview of the various optical flowOptical flow approaches introduced in the deep learningDeep learning age, including those based on alternative learning paradigms (e.g., unsupervised and semi-supervised methods) as well as the extension to the multi-frame case, which is able to yield further accuracy improvements.
A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO)

Gradient-based algorithms have been widely used in optimizing parameters of deep neural networks' (DNNs) architectures. However, the vanishing gradient remains as one of the common issues in the parameter optimization of such networks. To cope with the vanishing gradient problem, in this article, we propose a novel algorithm, evolved gradient direction optimizer (EVGO), updating the weights of DNNs based on the first-order gradient and a novel hyperplane we introduce. We compare the EVGO algorithm with other gradient-based algorithms, such as gradient descent, RMSProp, Adagrad, momentum, and Adam on the well-known Modified National Institute of Standards and Technology (MNIST) data set for handwritten digit recognition by implementing deep convolutional neural networks. Furthermore, we present empirical evaluations of EVGO on the CIFAR-10 and CIFAR-100 data sets by using the well-known AlexNet and ResNet architectures. Finally, we implement an empirical analysis for EVGO and other algorithms to investigate the behavior of the loss functions. The results show that EVGO outperforms all the algorithms in comparison for all experiments. We conclude that EVGO can be used effectively in the optimization of DNNs, and also, the proposed hyperplane may provide a basis for future optimization algorithms.
Lessons Learned From Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework

This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint. Since the delay and the inference accuracy are incurred by wireless communications and computing, an optimization problem is formulated to maximize the inference accuracy subject to the offloading probability, the pre-braking probability, and data quality. Simulations demonstrate the superiority of the proposed offloading framework.
Training Keyword Spotters with Limited and Synthesized Speech Data

With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tasks, one of the most challenging parts in the model creation process is obtaining a sufficient amount of training data. In this paper, we explore the effectiveness of synthesized speech data in training small, spoken term detection models of around 400k parameters. Instead of training such models directly on the audio or low level features such as MFCCs, we use a pre-trained speech embedding model trained to extract useful features for keyword spotting models. Using this speech embedding, we show that a model which detects 10 keywords when trained on only synthetic speech is equivalent to a model trained on over 500 real examples. We also show that a model without our speech embeddings would need to be trained on over 4000 real examples to reach the same accuracy.
SNDCNN: Self-Normalizing Deep CNNs with Scaled Exponential Linear Units for Speech Recognition

Very deep CNNs achieve state-of-the-art results in both computer vision and speech recognition, but are difficult to train. The most popular way to train very deep CNNs is to use shortcut connections (SC) together with batch normalization (BN). Inspired by Self-Normalizing Neural Networks, we propose the self-normalizing deep CNN (SNDCNN) based acoustic model topology, by removing the SC/BN and replacing the typical RELU activations with scaled exponential linear unit (SELU) in ResNet-50. SELU activations make the network self-normalizing and remove the need for both shortcut connections and batch normalization. Compared to ResNet50, we can achieve the same or lower (up to 4.5% relative) word error rate (WER) while boosting both training and inference speed by 60%-80%. We also explore other model inference optimization schemes to further reduce latency for production use.
The Use of Duolingo Apps to Improve English Vocabulary Learning

Education is a process of learning knowledge and skills. Learning English is skill-based learning that requires diverse approaches and techniques. Current education is much affected by technological advances. The use of technology is the latest solution to answer the challenges of learning English. As with advancement of English language learning means, duolingo completes to innovate way of learning language on mobile phone basis. The focus of this research is to explore the use of Duolingo apps in improving students vocabulary in Borneo University of Tarakan. The research design of this research is pre-experimental research with pre-test and post-test. This research will involve 10 students as sample with 30 days practicing duolingo apps using “regular” intensity of practice (20 xp per day). The Research instruments were tests and questionnaire to answer the research questions. How is the use of duolingo in improving students’ vocabulary? Based on the quantitative data, the students’ score is increased. The result is expected to provide information for teachers and students in having extensive language learning practice in handy form
A TfidfVectorizer and SVM based sentiment analysis framework for text data corpus

E-commerce and social networking sites are very much dependent on the available data which can be analyzed in real time to predict their future business strategies. However, analyzing huge amount of data manually is not possible in time context of business. Therefore, automated sentimental analysis, which can automatically determine the sentiments from the text data corpus plays an important role in today's world. Many sentimental analysis frameworks with state of the art results have been proposed in the literature. However, many of these frameworks have low accuracy on the textual data corpus contains emoticons and special texts. In addition, many of these frameworks are also energy and computation intensive with which puts limitation in their real time deployment. In this paper, we aim to address these issues by proposing a novel sentimental analysis framework based on Support Vector Machine (SVM). The proposed framework uses a novel technique to tokenize the text documents, wherein stop words, special characters, emoticons present in the text documents are eliminated. In addition, words with similar meanings and annotations are clubbed together into one type, using the concept of stemming. The pre-processed tokenized documents are then vectorized into n-gram integers vectors using the ‘TfidfVectorizer’ for use as input to the SVM based machine learning classifier model. Experimental results on the Amazon's electronics item review dataset and IMDB's movie review data corpus show that the proposed sentimental analysis framework achieves high performance compared to other similar frameworks proposed in the literature.
An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network

The shipping industry is developing towards intelligence rapidly. An accurate and fast method for ship image/video detection and classification is of great significance for not only the port management, but also the safe driving of Unmanned Surface Vehicle (USV). Thus, this paper makes a self-built dataset for the ship image/video detection and classification, and its method based on an improved regressive deep convolutional neural network is presented. This method promotes the regressive convolutional neural network from four aspects. First, the feature extraction layer is lightweighted by referring to YOLOv2. Second, a new feature pyramid network layer is designed by improving its structure in YOLOv3. Third, a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60% anchors. Last, the activation function is verified and optimized. Then, the detecting experiment on 7 types of ships shows that the proposed method has advantage compared with the YOLO series networks and other intelligent methods. This method can solve the problem of low recognition rate and real-time performance for ship image/video detection and classification with a small dataset. On the testing-set, the final mAP is 0.9209, the Recall is 0.9818, the AIOU is 0.7991, and the FPS is 78–80 in video detection. Thus, this method provides a highly accurate and real-time ship detection method for the intelligent port management and visual processing of the USV. In addition, the proposed regressive deep convolutional network also has a better comprehensive performance than that of YOLOv2/v3.