Reviewerly/exHarmony huggingface dataset:
NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks

Traditional recommender systems rely on user profiling based on either user ratings or reviews through bi-sentimental analysis. However, in real-world scenarios, there are two common phenomena: (1) users only provide ratings for items but without detailed review comments. As a result, the historical transaction data available for recommender systems are usually unbalanced and sparse; (2) in many cases, users’ opinions can be better grasped in their reviews than ratings. For the reason that there is always a bias between ratings and reviews, it is really important that users’ ratings and reviews should be mutually reinforced to grasp the users’ true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation. Specifically, we exploit two-step training neural networks, which utilize both reviews and ratings to grasp users’ true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring (SC) method, which employs dual attention vectors to predict the users’ sentiment scores of their reviews rather than using bi-sentiment analysis. Next, a combination function is designed to use the results of SC and user–item rating matrix to catch the opinion bias. It can filter the reviews and users, and build an enhanced user–item matrix. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user–item matrix. Extensive experiments on several real-world datasets (Yelp, Amazon, Taobao and Jingdong) demonstrate that (1) our approach can achieve a superior performance over state-of-the-art baselines; (2) our approach is able to tackle unbalanced data and achieve stable performances.
Priority inheritance with backtracking for iterative multi-agent path finding

In the Multi-Agent Path Finding (MAPF) problem, a set of agents moving on a graph must reach their own respective destinations without inter-agent collisions. In practical MAPF applications such as navigation in automated warehouses, where occasionally there are hundreds or more agents, MAPF must be solved iteratively online on a lifelong basis. Such scenarios rule out simple adaptations of offline compute-intensive optimal approaches; and scalable sub-optimal algorithms are hence appealing for such settings. Ideal algorithms are scalable, applicable to iterative scenarios, and output plausible solutions in predictable computation time. For the aforementioned purpose, this study presents Priority Inheritance with Backtracking (PIBT), a novel sub-optimal algorithm to solve MAPF iteratively. PIBT relies on an adaptive prioritization scheme to focus on the adjacent movements of multiple agents; hence it can be applied to several domains. We prove that, regardless of their number, all agents are guaranteed to reach their destination within finite time when the environment is a graph such that all pairs of adjacent nodes belong to a simple cycle (e.g., biconnected). Experimental results covering various scenarios, including a demonstration with real robots, reveal the benefits of the proposed method. Even with hundreds of agents, PIBT yields acceptable solutions almost immediately and can solve large instances that other established MAPF methods cannot. In addition, PIBT outperforms an existing approach on an iterative scenario of conveying packages in an automated warehouse in both runtime and solution quality.
Restricted-derestricted dynamic Bayesian Network inference of transcriptional regulatory relationships among genes in cancer

Understanding transcriptional regulatory relationships among genes is important for gaining etiological insights into diseases such as cancer. To this end, high-throughput biological data have been generated through advancements in a variety of technologies. These rely on computational approaches to discover underlying structures in such data. Among these computational approaches, Bayesian networks (BNs) stand out because their probabilistic nature enables them to manage randomness in the dynamics of gene regulation and experimental data. Feedback loops inherent in networks of regulatory relationships are more tractable when enhancements to BNs are applied to them. Here, we propose Restricted-Derestricted dynamic BNs with a novel search technique, Restricted-Derestricted Greedy Method, for such tasks. This approach relies on the Restricted-Derestricted Greedy search technique to infer transcriptional regulatory networks in two phases: restricted inference and derestricted inference. An application of this approach to real data sets reveals it performs favourably well compared to other existing well performing dynamic BN approaches in terms of recovering true relationships among genes. In addition, it provides a balance between searching for optimal networks and keeping biologically relevant regulatory interactions among variables.
Constructing 2 × 2 × 4 and 4 × 4 unextendible product bases and positive-partial-transpose entangled states

The 4-qubit unextendible product bases (UPBs) has been recently studied by [Johnston, J. Phys. A: Math. Theor. 47 (2014) 424034]. Based on this study we show that there is only one UPB of size 6 and six UPBs of size 9 in H=C2⊗C2⊗C4, three UPBs of size 9 in K=C4⊗C4, and no UPB of size 7 in H and K. Furthermore we construct a new family of 4-qubit positive-partial-transpose (PPT) entangled states of rank seven, and show that they are also PPT entangled states in H and K, respectively. We analytically derive a geometric measure of a special PPT entangled states.
Human-Centered, Ergonomic Wearable Device with Computer Vision Augmented Intelligence for VR Multimodal Human-Smart Home Object Interaction

In the future, Human-Robot Interaction should be enabled by a compact, human-centered and ergonomic wearable device that can merge human and machine altogether seamlessly by constantly identifying each other's intentions. In this paper, we will showcase the use of an ergonomic and lightweight wearable device that can identify human's eye/facial gestures with physiological signal measurements. Since human's intentions are usually coupled with eye movements and facial expressions, through proper design of interactions using these gestures, we can let people interact with the robots or smart home objects naturally. Combined with Computer Vision object recognition algorithms, we can allow people use very simple and straightforward communication strategies to operate telepresence robot and control smart home objects remotely, totally “Hands-Free”. People can wear a VR head-mounted display and see through the robot's eyes (the remote camera attached on the robot) and interact with the smart home devices intuitively by simple facial gestures or blink of the eyes. It is tremendous beneficial for the people with motor impairment as an assistive tool. For the normal people without disabilities, they can also free their hands to do other tasks and operate the smart home devices at the same time as multimodal control strategies.
Foundations for Parallel Information Flow Control Runtime Systems

We present the foundations for a new dynamic information flow control (IFC) parallel runtime system, LIO $$_{\mathrm {PAR}}$$ . To our knowledge, LIO $$_{\mathrm {PAR}}$$ is the first dynamic language-level IFC system to (1) support deterministic parallel thread execution and (2) eliminate both internal- and external-timing covert channels that exploit the runtime system. Most existing IFC systems are vulnerable to external timing attacks because they are built atop vanilla runtime systems that do not account for security—these runtime systems allocate and reclaim shared resources (e.g., CPU-time and memory) fairly between threads at different security levels. While such attacks have largely been ignored—or, at best, mitigated—we demonstrate that extending IFC systems with parallelism leads to the internalization of these attacks. Our IFC runtime system design addresses these concerns by hierarchically managing resources—both CPU-time and memory—and making resource allocation and reclamation explicit at the language-level. We prove that LIO $$_{\mathrm {PAR}}$$ is secure, i.e., it satisfies progress- and timing-sensitive non-interference, even when exposing clock and heap-statistics APIs.
Deep reinforcement learning for quantum Szilard engine optimization

Machine learning techniques based on artificial neural networks have been successfully applied to solve many problems in science. One of the most interesting domains of machine learning, reinforcement learning, has natural applicability for optimization problems in physics. In this work we use deep reinforcement learning and Chopped Random Basis optimization, to solve an optimization problem based on the insertion of an off-center barrier in a quantum Szilard engine. We show that using designed protocols for the time dependence of the barrier strength, we can achieve an equal splitting of the wave function (1/2 probability to find the particle on either side of the barrier) even for an asymmetric Szilard engine in such a way that no information is lost when measuring which side the particle is found. This implies that the asymmetric non-adiabatic Szilard engine can operate with the same efficiency as the traditional Szilard engine, with adiabatic insertion of a central barrier. We compare the two optimization methods, and demonstrate the advantage of reinforcement learning when it comes to constructing robust and noise-resistant protocols.
A Behavior Based Trustworthy Service Composition Discovery Approach in Cloud Environment

With the development of cloud computing, more and more individuals and organizations have moved local application resources into the cloud computing resource pool in the form of Web services so that users can choose to invoke. This paper proposes a trustworthy service composition discovery approach to satisfy user requirements including behavioral constraints in a cloud environment. First, we build a global service composition discovery model based on sematic relationships between attributes and present a service composition discovery method. Then, we propose a method for detecting behavioral consistency. We validate the feasibility and effectiveness of the proposed solutions through a case study.
An Out-of-Sample Extension to Manifold Learning via Meta-Modeling

Unsupervised manifold learning has become accepted as an important tool for reducing dimensionality of a dataset by finding its meaningful low-dimensional representation lying on an unknown nonlinear subspace. Most manifold learning methods only embed an existing dataset, but do not provide an explicit mapping function for novel out-of-sample data, thereby potentially resulting in an ineffective tool for classification purposes, particularly for iterative methods such as active learning. To address this issue, out-of-sample extension methods have been introduced to generalize an existing embedding of new samples. In this work, a novel out-of-sample method is introduced by utilizing High Dimensional Model Representation (HDMR) as a nonlinear multivariate regression with the Tikhonov regularizer for unsupervised manifold learning algorithms. The proposed method was extensively analyzed using illustrative datasets sampled from known manifolds. Several experiments with 3D synthetic datasets and face recognition datasets were also conducted, and the performance of the proposed method was compared to several well-known out-of-sample methods. The results obtained with Locally Linear Embedding (LLE), Laplacian Eigenmaps (LE), and t-Distributed Stochastic Neighbor Embedding (t-SNE) showed that the proposed method achieves competitive even better performance than the other out-of-sample methods.
Preliminary Investigation of Alleviating User Cold-Start Problem in E-commerce with Deep Cross-Domain Recommender System

Many current applications use recommender systems to predict user preferences, aiming at improving user experience and increasing the amount of sales and the usage time that users spent on the application. However, it is not an easy task to recommend items to new users accurately because of the user cold-start problem, which means that recommendation performance will degrade on users with little interaction, particularly for latent users who have never used the service before. In this work, we combine an online shopping domain with information from an ad platform, and then apply deep learning to build a cross-domain recommender system based on shared users of these two domains, to alleviate the user cold-start problem. Experimental results show the effectiveness of our deep cross-domain recommender system on handling user cold-start problem. By our framework, it is possible to recommend products to users of other domain through ad distribution in a more accurate level, and to increase sales amount of online shopping.
A Robust Deep Ensemble Classifier for Figurative Language Detection

Recognition and classification of Figurative Language (FL) is an open problem of Sentiment Analysis in the broader field of Natural Language Processing (NLP) due to the contradictory meaning contained in phrases with metaphorical content. The problem itself contains three interrelated FL recognition tasks: sarcasm, irony and metaphor which, in the present paper, are dealt with advanced Deep Learning (DL) techniques. First, we introduce a data prepossessing framework towards efficient data representation formats so that to optimize the respective inputs to the DL models. In addition, special features are extracted in order to characterize the syntactic, expressive, emotional and temper content reflected in the respective social media text references. These features aim to capture aspects of the social network user's writing method. Finally, features are fed to a robust, Deep Ensemble Soft Classifier (DESC) which is based on the combination of different DL techniques. Using three different benchmark datasets (one of them containing various FL forms) we conclude that the DESC model achieves a very good performance, worthy of comparison with relevant methodologies and state-of-the-art technologies in the challenging field of FL recognition.
The Effect of Cycling Through a Projection-Based Virtual Environment System on Generalized Anxiety Disorder

Virtual reality (VR) has the potential to help clinical medicine manage generalized anxiety disorder (GAD). However, patients with GAD who use traditional head-mounted VR to cycle may cause them to feel motion sickness and fatigue. To solve this problem, a projection-based virtual environment (VE) system was built to provide GAD patients with a sense of immersion while they are cycling. This projection-based VE system allows patients with GAD to interact with the virtual environment and produce experiences similar to cycling in the outdoors. Sixty GAD patients met several screening criteria and were selected as participants. All participants were randomly assigned to one of the two 20-min conditions: (1) Observing watercolor paintings projected by the projector while engaged in cycling with a stationary bicycle; or (2) observing the scenes (i.e., forest or park) projected by the VE system and engaging in cycling with a stationary bicycle. Finally, this study confirmed that patients with GAD in the projection-based VE group exhibited higher alpha values and lower galvanic skin responses (GSR) after cycling than those cycling in the control group. These results showed that cycling in the projection-based VE group allowed the patient with GAD to achieve higher exercise intensity and lower perceived emotional stress.
Camera Heights in Cinematic Virtual Reality

When watching a 360° movie with Head Mounted Displays (HMD) the viewer feels to be inside the movie and can experience it in an immersive way. The head of the viewer is exactly in the same place as the camera was when the scene was recorded. In traditional movies, the viewer is watching the movie from outside and a distance between eye height and camera height does not create a problem. However, viewing a movie from the perspective of the camera by HMDs can raise some challenges, e.g. heights of well-known objects can irritate the viewer in the case the camera height does not correspond to the physical eye height. The aim of this work is to study how the position of the camera influences presence, sickness and the user experience of the viewer. We have considered several watching postures as well as various eye heights. The results of our experiments suggest that differences between camera and eye heights are more accepted if the camera position is lower than the own body height. Additionally, sitting postures are preferred and can be adapted easier than standing postures. These results can be applied to improve guidelines for 360° filmmakers.
Improving Classification of Breast Cancer by Utilizing the Image Pyramids of Whole-Slide Imaging and Multi-scale Convolutional Neural Networks

Whole-slide imaging (WSI) is the digitization of conventional glass slides. Automatic computer-aided diagnosis (CAD) based on WSI enables digital pathology and the integration of pathology with other data like genomic biomarkers. Numerous computational algorithms have been developed for WSI, with most of them taking the image patches cropped from the highest resolution as the input. However, these models exploit only the local information within each patch and lost the connections between the neighboring patches, which may contain important context information. In this paper, we propose a novel multi-scale convolutional network (ConvNet) to utilize the built-in image pyramids of WSI. For the concentric image patches cropped at the same location of different resolution levels, we hypothesize the extra input images from lower magnifications will provide context information to enhance the prediction of patch images. We build corresponding ConvNets for feature representation and then combine the extracted features by 1) late fusion: concatenation or averaging the feature vectors before performing classification, 2) early fusion: merge the ConvNet feature maps. We have applied the multi-scale networks to a benchmark breast cancer WSI dataset. Extensive experiments have demonstrated that our multi-scale networks utilizing the WSI image pyramids can achieve higher accuracy for the classification of breast cancer. The late fusion method by taking the average of feature vectors reaches the highest accuracy (81.50%), which is promising for the application of multi-scale analysis of WSI.
Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction

We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent (SGD): a novel generalization of the classical Brown clustering objective and a recently proposed variational lower bound. While both objectives are subject to noise in gradient updates, we show through analysis and experiments that the variational lower bound is robust whereas the generalized Brown objective is vulnerable. We obtain strong performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context.
On Connecting Stochastic Gradient MCMC and Differential Privacy

Concerns related to data security and confidentiality have been raised when applying machine learning to real-world applications. Differential privacy provides a principled and rigorous privacy guarantee for machine learning models. While it is common to inject noise to design a model satisfying a required differential-privacy property, it is generally hard to balance the trade-off between privacy and utility. We show that stochastic gradient Markov chain Monte Carlo (SG-MCMC) -- a class of scalable Bayesian posterior sampling algorithms -- satisfies strong differential privacy, when carefully chosen stepsizes are employed. We develop theory on the performance of the proposed differentially-private SG-MCMC method. We conduct experiments to support our analysis, and show that a standard SG-MCMC sampler with minor modification can reach state-of-the-art performance in terms of both privacy and utility on Bayesian learning.
Siamese-Network-Based Learning to Rank for No-Reference 2D and 3D Image Quality Assessment

2D image quality assessment (IQA) and stereoscopic 3D IQA are considered as two different tasks in the literature. In this paper, we present an index for both no-reference 2D and 3D IQA. We propose to transform the IQA task into a task of quality comparison between images. By generating image pairs, the amount of training data reaches the square of the original amount of data, effectively solving the lacking of training samples. We also propose a learning to rank model using Siamese convolutional neural networks (LRSN) for quality comparison. The presented LRSN has two branches that have the same structure, share weights with each other, and take two image patches as inputs. The goal of LRSN is learning to rank the quality scores between the two input image patches. The relative quality score of a test image is obtained by first comparing its image patches with many image patches of other images and counts the number of times that its image patches are ranked superior to other patches. The experimental results on three 2D (LIVE, CSIQ, and TID2013) and three 3D (LIVE 3D Phase-I, LIVE 3D Phase-II, and NBU) IQA databases demonstrate that the proposed LRSN model works well for both 2D and 3D IQA and outperforms the state-of-the-art no-reference 2D and 3D IQA metrics.
Performance evaluation of solar radiation equations for estimating reference evapotranspiration (ETo) in a humid tropical environment

Abstract Solar radiation ( Rs ) is an essential input for estimating reference crop evapotranspiration, ETo . An accurate estimate of ETo is the first step involved in determining water demand of field crops. The objective of this study was to assess the accuracy of fifteen empirical solar radiations ( Rs ) models and determine its effects on ETo estimates for three sites in humid tropical environment (Abakaliki, Nsukka, and Awka). Meteorological data from the archives of NASA (from 1983 to 2005) was used to derive empirical constants (calibration) for the different models at each location while data from 2006 to 2015 was used for validation. The results showed an overall improvement when comparing measured Rs with Rs determined using original constants and Rs using the new constants. After calibration, the Swartman–Ogunlade ( R 2 = 0.97) and Chen 2 models ( RMSE = 0.665 MJ∙m −2 ∙day −1 ) performed best while Chen 1 ( R 2 = 0.66) and Bristow–Campbell models ( RMSE = 1.58 MJ∙m −2 ∙day −1 ) performed least in estimating Rs in Abakaliki. At the Nsukka station, Swartman–Ogunlade ( R 2 = 0.96) and Adeala models ( RMSE = 0.785 MJ∙m −2 ∙day −1 ) performed best while Hargreaves–Samani ( R 2 = 0.64) and Chen 1 models ( RMSE = 1.96 MJ∙m −2 ∙day −1 ) performed least in estimating Rs . Chen 2 ( R 2 = 0.98) and Swartman–Ogunlade models ( RMSE = 0.43 MJ∙m −2 ∙day −1 ) performed best while Hargreaves–Samani ( R 2 = 0.68) and Chen 1 models ( RMSE = 1.64 MJ∙m −2 ∙day −1 ) performed least in estimating Rs in Awka. For estimating ETo , Adeala ( R 2 =0.98) and Swartman–Ogunlade models ( RMSE = 0.064 MJ∙m −2 ∙day −1 ) performed best at the Awka station and Swartman–Ogunlade ( R 2 = 0.98) and Chen 2 models ( RMSE = 0.43 MJ∙m −2 ∙day −1 ) performed best at Abakaliki while Angstrom–Prescott–Page ( R 2 = 0.96) and El-Sebaii models ( RMSE = 0.0908 mm∙day −1 ) performed best at the Nsukka station.