Reviewerly/exHarmony huggingface dataset:
Deep Probabilistic Matrix Factorization Framework for Online Collaborative Filtering

As living data growing and evolving rapidly, traditional machine learning algorithms are hard to update models when dealing with new training data.When new data arrives, traditional collaborative filtering methods have to train their model from scratch.It is expensive for them to retrain a model and update their parameters.Compared with traditional collaborative filtering, the online collaborative filtering is effective to update the models instantly when new data arrives.But the cold start and data sparsity remain major problems for online collaborative filtering.In this paper, we try to utilize the convolutional neural network to extract user/item features from user/item side information to address these problems.First, we proposed a deep bias probabilistic matrix factorization (DBPMF) model by utilizing the convolutional neural network to extract latent user/item features and adding the bias into probabilistic matrix factorization to track user rating behavior and item popularity.Second, we constrain user-specific and item-specific feature vectors to further improve the performance of the DBPMF.Third, we update two models by an online learning algorithm.The extensive experiments for three datasets (MovieLens100K, MovieLens1M, and HetRec2011) show that our methods have a better performance than baseline approaches.
Reduced variance deep reinforcement learning with temporal logic specifications

In this paper, we propose a model-free reinforcement learning method to synthesize control policies for mobile robots modeled as Markov Decision Process (MDP) with unknown transition probabilities that satisfy Linear Temporal Logic (LTL) specifications. Specifically, we develop a reduced variance deep Q-Learning technique that relies on Neural Networks (NN) to approximate the state-action values of the MDP and employs a reward function that depends on the accepting condition of the Deterministic Rabin Automaton (DRA) that captures the LTL specification. The key idea is to convert the deep Q-Learning problem into a nonconvex max-min optimization problem with a finite-sum structure, and develop an Arrow-Hurwicz-Uzawa type stochastic reduced variance algorithm with constant stepsize to solve it. Unlike Stochastic Gradient Descent (SGD) methods that are often used in deep reinforcement learning, our method can estimate the gradients of an unknown loss function more accurately and can improve the stability of the training process. Moreover, our method does not require learning the transition probabilities in the MDP, constructing a product MDP, or computing Accepting Maximal End Components (AMECs). This allows the robot to learn an optimal policy even if the environment cannot be modeled accurately or if AMECs do not exist. In the latter case, the resulting control policies minimize the frequency with which the system enters bad states in the DRA that violate the task specifications. To the best of our knowledge, this is the first model-free deep reinforcement learning algorithm that can synthesize policies that maximize the probability of satisfying an LTL specification even if AMECs do not exist. Rigorous convergence analysis and rate of convergence are provided for the proposed algorithm as well as numerical experiments that validate our method.
Reversible Data Hiding With Image Enhancement Using Histogram Shifting

Traditional reversible data hiding (RDH) focuses on enlarging the embedding payloads while minimizing the distortion with a criterion of mean square error (MSE).Since imperceptibility can also be achieved via image processing, we propose a novel method of RDH with contrast enhancement (RDH-CE) using histogram shifting.Instead of minimizing the MSE, the proposed method generates marked images with good quality with the sense of structural similarity.The proposed method contains two parts: the baseline embedding and the extensive embedding.In the baseline part, we first merge the least significant bins to reserve spare bins and then embed additional data by a histogram shifting approach using arithmetic encoding.During histogram shifting, we propose to construct the transfer matrix by maximizing the entropy of the histogram.After embedding, the marked image containing additional data has a larger contrast than the original image.In the extensive embedding part, we further propose to concatenate the baseline embedding with an MSE-based embedding.On the recipient side, the additional data can be extracted exactly, and the original image can be recovered losslessly.Comparing with existing RDH-CE approaches, the proposed method can achieve a better embedding payload.
Factors that affect Employees Job Satisfaction and Performance to Increase Customers’ Satisfactions

In order to help the Jordanian industrial sector to minimize the amount of time and money currently spent on recruiting and training new employees due to the rapid employee turnover rate, this study aims to identify the factors which encourage employees to remain in their jobs for a long-term as opposed to those that create negative sentiments thus, leading employees to quit. The factors under focus are wages, organizational culture, benefits, job satisfaction, stress, training and development, promotion prospects, and job security. The study measures the impact of each factor on employee satisfaction. The research population is the body of employees in Jordan’s industrial sector, with a random sample of industrial employees representing the population. The quantitative method is used to examine the research question. The study found that Jordanian employees care the most about their salaries and position more than any other factors. Therefore, we recommend that Jordanian manufactures consider studying the range of salary for each position, so they do not miss out on talented people or lose a good employee.
Non-Ergodic Convergence Analysis of Heavy-Ball Algorithms

In this paper, we revisit the convergence of the Heavy-ball method, and present improved convergence complexity results in the convex setting. We provide the first non-ergodic O(1/k) rate result of the Heavy-ball algorithm with constant step size for coercive objective functions. For objective functions satisfying a relaxed strongly convex condition, the linear convergence is established under weaker assumptions on the step size and inertial parameter than made in the existing literature. We extend our results to multi-block version of the algorithm with both the cyclic and stochastic update rules. In addition, our results can also be extended to decentralized optimization, where the ergodic analysis is not applicable.
DeRPN: Taking a Further Step toward More General Object Detection

Most current detection methods have adopted anchor boxes as regression references. However, the detection performance is sensitive to the setting of the anchor boxes. A proper setting of anchor boxes may vary significantly across different datasets, which severely limits the universality of the detectors. To improve the adaptivity of the detectors, in this paper, we present a novel dimension-decomposition region proposal network (DeRPN) that can perfectly displace the traditional Region Proposal Network (RPN). DeRPN utilizes an anchor string mechanism to independently match object widths and heights, which is conducive to treating variant object shapes. In addition, a novel scale-sensitive loss is designed to address the imbalanced loss computations of different scaled objects, which can avoid the small objects being overwhelmed by larger ones. Comprehensive experiments conducted on both general object detection datasets (Pascal VOC 2007, 2012 and MS COCO) and scene text detection datasets (ICDAR 2013 and COCO-Text) all prove that our DeRPN can significantly outperform RPN. It is worth mentioning that the proposed DeRPN can be employed directly on different models, tasks, and datasets without any modifications of hyperparameters or specialized optimization, which further demonstrates its adaptivity. The code has been released at https://github.com/HCIILAB/DeRPN.
Scalable Recollections for Continual Lifelong Learning

Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings. Perhaps the most difficult of these settings is that of continual lifelong learning, where the model must learn online over a continuous stream of non-stationary data. A successful continual lifelong learning system must have three key capabilities: it must learn and adapt over time, it must not forget what it has learned, and it must be efficient in both training time and memory. Recent techniques have focused their efforts primarily on the first two capabilities while questions of efficiency remain largely unexplored. In this paper, we consider the problem of efficient and effective storage of experiences over very large time-frames. In particular we consider the case where typical experiences are O(n) bits and memories are limited to O(k) bits for k
Temporal Bilinear Networks for Video Action Recognition

Temporal modeling in videos is a fundamental yet challenging problem in computer vision. In this paper, we propose a novel Temporal Bilinear (TB) model to capture the temporal pairwise feature interactions between adjacent frames. Compared with some existing temporal methods which are limited in linear transformations, our TB model considers explicit quadratic bilinear transformations in the temporal domain for motion evolution and sequential relation modeling. We further leverage the factorized bilinear model in linear complexity and a bottleneck network design to build our TB blocks, which also constrains the parameters and computation cost. We consider two schemes in terms of the incorporation of TB blocks and the original 2D spatial convolutions, namely wide and deep Temporal Bilinear Networks (TBN). Finally, we perform experiments on several widely adopted datasets including Kinetics, UCF101 and HMDB51. The effectiveness of our TBNs is validated by comprehensive ablation analyses and comparisons with various state-of-the-art methods.
DANE: Domain Adaptive Network Embedding

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.
Baidu Neural Machine Translation Systems for WMT19

In this paper we introduce the systems Baidu submitted for the WMT19 shared task on Chinese↔English news translation.Our systems are based on the Transformer architecture with some effective improvements.Data selection, back translation, data augmentation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments.Our Chinese→English system achieved the highest case-sensitive BLEU score among all constrained submissions, and our English→Chinese system ranked the second in all submissions.
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank

Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge.The basic idea is to map dependency arcs from a source treebank to its target translation according to word alignments.This method, however, can suffer from imperfect alignment between source and target words.To address this problem, we investigate syntactic transfer by code mixing, translating only confident words in a source treebank.Cross-lingual word embeddings are leveraged for transferring syntactic knowledge to the target from the resulting code-mixed treebank.Experiments on University Dependency Treebanks show that code-mixed treebanks are more effective than translated treebanks, giving highly competitive performances among cross-lingual parsing methods.
Improving Zero-shot Translation with Language-Independent Constraints

An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation.Improving this ability kills two birds with one stone by providing an alternative to pivot translation which also allows us to better understand how the model captures information between languages.In this work, we carried out an investigation on this capability of the multilingual NMT models.First, we intentionally create an encoder architecture which is independent with respect to the source language.Such experiments shed light on the ability of NMT encoders to learn multilingual representations, in general.Based on such proof of concept, we were able to design regularization methods into the standard Transformer model, so that the whole architecture becomes more robust in zero-shot conditions.We investigated the behaviour of such models on the standard IWSLT 2017 multilingual dataset.We achieved an average improvement of 2.23 BLEU points across 12 language pairs compared to the zero-shot performance of a state-of-the-art multilingual system.Additionally, we carry out further experiments in which the effect is confirmed even for language pairs with multiple intermediate pivots.
Image Transformation Technique Using Steganography Methods Using LWT Technique

Digital image watermarking is a technique adopted to get rid of the increasing piracies in digital images.Computerized information can be effectively duplicated, altered and falsifications be made by anybody having a PC.Most inclined to such vindictive assaults are the watermarked pictures distributed in the Internet.Advanced Watermarking can be utilized as a device for finding unapproved information reuse and furthermore for copyright security.In the existing method, texturization dependant image watermarking methodology is performed which involves the embedding and extraction of a logo image to and from an original image respectively.After finding out the texture regions of host image, the logo image is embedded into the identified texture regions by Discrete Wavelet Transform.Before embedding, according to the textual characteristics of the host image analyzed, texturization of a logo is done by using Arnold transform and a rotation.It is effective for attaining a similar texture for both logo image and host image.Later the logo image is extracted back.Discrete Wavelet Transform results in degradation of quality and robustness of watermarked image.Also it is not a difficult task for an attacker to compromise the Arnold transform and rotation performed.In this work, Lifting Wavelet Transform technique is used instead of the Discrete Wavelet Transform as it overcome the above mentioned drawback.In addition, Arnold transform and rotation is replaced with circular shift method for enhancing security.
Multimodal Emotion Recognition from Eye Image, Eye Movement and EEG Using Deep Neural Networks

In consideration of the complexity of recording electroencephalography(EEG), some researchers are trying to find new features of emotion recognition. In order to investigate the potential of eye tracking glasses for multimodal emotion recognition, we collect and use eye images to classify five emotions along with eye movements and EEG. We compare four combinations of the three different types of data and two kinds of fusion methods, feature level fusion and Bimodal Deep AutoEncoder (BDAE). According to the three-modality fusion features generated by BDAE, the best mean accuracy of 79.63% is achieved. By analyzing the confusion matrices, we find that the three modalities can provide complementary information for recognizing five emotions. Meanwhile, the experimental results indicate that the classifiers with eye image and eye movement fusion features can achieve a comparable classification accuracy of 71.99%.
Decentralized E-Voting Portal Using Blockchain

Online voting is an alternative to age old paper ballot system and the currently popular electronic voting machines (EVM). An electronic voting portal should offer security and integrity along with the transparency of votes and privacy of voters. This paper proposes an e-voting system based on blockchain that eliminates some of the limitations in existing voting systems. The paper also presents state of art of some blockchain frameworks for e-voting. The presented implementation is suitable for small scale elections like inside corporate houses, board rooms etc. The implementation uses smart contract from Ethereum. Truffle framework is used in this paper for development, testing and deploying smart contracts. Ganache is used as Ethereum client for testing. Here Meta-mask is used as browser wallet.
Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines. 1
Designing a Blockchain Model for the Paris Agreement’s Carbon Market Mechanism

This paper examines the benefits and constraints of applying blockchain technology for the Paris Agreement carbon market mechanism and develops a list of technical requirements and soft factors as selection criteria to test the feasibility of two different blockchain platforms. The carbon market mechanism, as outlined in Article 6.2 of the Paris Agreement, can accelerate climate action by enabling cooperation between national Parties. However, in the past, carbon markets were limited by several constraints. Our research investigates these constraints and translates them into selection criteria to design a blockchain platform to overcome these past limitations. The developed selection criteria and assumptions developed in this paper provide an orientation for blockchain assessments. Using the selection criteria, we examine the feasibility of two distinct blockchains, Ethereum and Hyperledger Fabric, for the specific use case of Article 6.2. These two blockchain systems represent contrary forms of design and governance; Ethereum constitutes a public and permissionless blockchain governance system, while Hyperledger Fabric represents a private and permissioned governance system. Our results show that both blockchain systems can address present carbon market constraints by enhancing market transparency, increasing process automation, and preventing double counting. The final selection and blockchain system implementation will first be possible, when the Article 6 negotiations are concluded, and governance preferences of national Parties are established. Our paper informs about the viability of different blockchain systems, offers insights into governance options, and provides a valuable framework for a concrete blockchain selection in the future.
A Discriminative Deep Model With Feature Fusion and Temporal Attention for Human Action Recognition

Activity recognition which aims to accurately distinguish human actions in complex environments plays a key role in human-robot/computer interaction.However, long-lasting and similar actions will cause poor feature sequence extraction and thus lead to a reduction of the recognition accuracy.We propose a novel discriminative deep model (D3D-LSTM) based on 3D-CNN and LSTM for both single-target and interaction action recognition to improve the spatiotemporal processing performance.Our models have several notable properties: 1) A real-time feature fusion method is used to obtain a more representative feature sequence through composition of local mixtures for enhancing the performance of discriminating similar actions; 2) We introduce an improved attention mechanism that focuses on each frame individually by assigning different weights in real-time; 3) An alternating optimization strategy is proposed for our model to obtain parameters with the best performance.Because the proposed D3D-LSTM model is efficient enough to be used as a detector that recognizes various activities, a Real-set database is collected to evaluate action recognition in complex real-world scenarios.For long-term relations, we update the present memory state via the weight-controlled attention module that enables the memory cell to store better long-term features.The densely connected bimodal modal makes local perceptrons of 3D-Conv motion-aware and stores better short-term features.The proposed D3D-LSTM model has been evaluated through a series of experiments on the Real-set and open-source datasets, i.e.SBU-Kinect and MSR-action-3D.Experimental results show that the proposed D3D-LSTM model achieves new state-of-the-art results, including pushing the average rate of the SBU-Kinect to 92.40% and the average rate of the MSR-action-3D to 95.40%.
Chain Rule for the Quantum Relative Entropy

The chain rule for the classical relative entropy ensures that the relative entropy between probability distributions on multipartite systems can be decomposed into a sum of relative entropies of suitably chosen conditional distributions on the individual systems. Here, we prove a chain rule inequality for the quantum relative entropy. The new chain rule allows us to solve an open problem in the context of asymptotic quantum channel discrimination: surprisingly, adaptive protocols cannot improve the error rate for asymmetric channel discrimination compared to nonadaptive strategies.