Reviewerly/exHarmony huggingface dataset:
Boxer: Interactive Comparison of Classifier Results

Abstract Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.
Deep learning based Text Emotion Recognition for Chatbot applications

Emotions play a vital role in human interaction. We recognize emotion of a person from their speech, face gesture, body language and sign actions. Since humans use many text devices to make interactions these days, emotion extraction from the text has drawn a lot of importance. It is therefore crucial that emotions in textual conversation need to be well understood by the machines, which ultimately provide users with emotional awareness feedback. This paper investigates the effectiveness of deep learning based Long Short-Term Memory mechanism for identification of textual emotions. The study was carried out on `Emotion classification' dataset with six emotional groups. The experimental results proved that LSTM based text emotion classification provides relatively higher accuracy compared to the existing learning methods.
The NEBULA RPC-Optimized Architecture

Large-scale online services are commonly structured as a network of software tiers, which communicate over the datacenter network using RPCs. Ongoing trends towards software decomposition have led to the prevalence of tiers receiving and generating RPCs with runtimes of only a few microseconds. With such small software runtimes, even the smallest latency overheads in RPC handling have a significant relative performance impact. In particular, we find that growing network bandwidth introduces queuing effects within a server's memory hierarchy, considerably hurting the response latency of fine-grained RPCs. In this work we introduce NEBULA, an architecture optimized to accelerate the most challenging microsecond-scale RPCs, by leveraging two novel mechanisms to drastically improve server throughput under strict tail latency goals. First, NEBULA reduces detrimental queuing at the memory controllers via hardware support for efficient in-LLC network buffer management. Second, NEBULA's network interface steers incoming RPCs into the CPU cores' L1 caches, improving RPC startup latency. Our evaluation shows that NEBULA boosts the throughput of a state-of-the-art key-value store by 1.25- 2.19 x compared to existing proposals, while maintaining strict tail latency goals.
Modelling the Distribution of 3D Brain MRI Using a 2D Slice VAE

Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Autoencoders (VAEs), opened up new avenues for probabilistic modeling. Modelling of volumetric data has remained a challenge, however, because constraints on available computation and training data make it difficult effectively leverage VAEs, which are well-developed for 2D images. We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We do so by estimating the sample mean and covariance in the latent space of the 2D model over the slice direction. This combined model lets us sample new coherent stacks of latent variables to decode into slices of a volume. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy. We demonstrate that our proposed model is competitive in generating high quality volumes at high resolutions according to both traditional metrics and our proposed evaluation. (Code is available at https://github.com/voanna/slices-to-3d-brain-vae/).
Feature extraction based on <scp>deep‐</scp>convolutional neural network for face recognition

Summary Feature extraction is a critical technology that affects the accuracy of face recognition. However, certain features are highly related to changes in face are difficult to extract because of the influences of individual differences and illumination. Therefore, features can accurately describe the changes in face are urgently required. For this reason, this article proposes a feature extraction method based on deep learning. This method combines the features extracted by Local Binary Patterns and by Convolutional Neural Network convolutional layer in the network connection layer, thus obtaining classification features with high representation ability and solving the problem of single feature extraction. The VGG‐16 network proposed in this article has been improved by changing the framework structure. Some experiments based on the Labeled Faces in the Wild dataset are performed, and results show that, in terms of accuracy and the sensitivity to light, the proposed method reaches 99.56% and 80.35% respectively. The recognition results obtained from fused features are superior to which of single feature recognition. Simulation results show that the method is more robust to changes in the illumination condition and more efficient than the existing methods.
Neural Interface Instrumented Virtual Reality Headsets: Toward Next-Generation Immersive Applications

The last decade has seen a strong resurgence of virtual reality (VR) and augmented reality (AR) applications, ranging from entertainment to neurorehabilitation. Users of VR headsets, however, often experience motion sickness symptoms, commonly referred to as cybersickness. For developers of immersive content, it is hard to measure in real time users' perceptions of immersion and the quality of experience (QoE) provided by the application. Commonly, subjective testing via questionnaires is used. Moreover, when applied in clinical applications, such as to treat phobias or in neurorehabilitation, it is hard to quantitatively gauge the success of different treatments other than through the use of subjective outcome measures.
Some theoretical properties of GANS

Generative Adversarial Networks (GANs) are a class of generative algorithms that have been shown to produce state-of-the-art samples, especially in the domain of image creation.The fundamental principle of GANs is to approximate the unknown distribution of a given data set by optimizing an objective function through an adversarial game between a family of generators and a family of discriminators.In this paper, we offer a better theoretical understanding of GANs by analyzing some of their mathematical and statistical properties.We study the deep connection between the adversarial principle underlying GANs and the Jensen-Shannon divergence, together with some optimality characteristics of the problem.An analysis of the role of the discriminator family via approximation arguments is also provided.In addition, taking a statistical point of view, we study the large sample properties of the estimated distribution and prove in particular a central limit theorem.Some of our results are illustrated with simulated examples.
Neural Denoising with Layer Embeddings

Abstract We propose a novel approach for denoising Monte Carlo path traced images, which uses data from individual samples rather than relying on pixel aggregates. Samples are partitioned into layers, which are filtered separately, giving the network more freedom to handle outliers and complex visibility. Finally the layers are composited front‐to‐back using alpha blending. The system is trained end‐to‐end, with learned layer partitioning, filter kernels, and compositing. We obtain similar image quality as recent state‐of‐the‐art sample based denoisers at a fraction of the computational cost and memory requirements.
Lessons and tips for designing a machine learning study using EHR data

Machine learning (ML) provides the ability to examine massive datasets and uncover patterns within data without relying on a priori assumptions such as specific variable associations, linearity in relationships, or prespecified statistical interactions. However, the application of ML to healthcare data has been met with mixed results, especially when using administrative datasets such as the electronic health record. The black box nature of many ML algorithms contributes to an erroneous assumption that these algorithms can overcome major data issues inherent in large administrative healthcare data. As with other research endeavors, good data and analytic design is crucial to ML-based studies. In this paper, we will provide an overview of common misconceptions for ML, the corresponding truths, and suggestions for incorporating these methods into healthcare research while maintaining a sound study design.
Unsupervised Heterogeneous Coupling Learning for Categorical Representation

Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. The deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represent heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics.
Complex-Valued Convolutions for Modulation Recognition using Deep Learning

Natural signals are inherently comprised of two components, real and imaginary components. Due to recent successes and progress in Deep Learning, specifically Convolutional Neural Networks (CNNs), this field of machine learning has become extremely popular when handling a wide variety of data, including natural signals. However, deep learning frameworks have been developed to deal with exclusively real-valued data and are unable to compute convolutions for complex-valued data. In this work, we present a linear combination that enables deep learning architectures to compute complex convolutions and learn features across the real and imaginary components of natural signals. When implemented into existing I/Q modulation classification architectures, this small change increases classification accuracy across a range of SNR levels by up to 35%.
Research on personalized recommendation hybrid algorithm for interactive experience equipment

Abstract Interactive calligraphy experience equipment has the characteristics of a large amount of data, various types, and strong homogeneity, which makes it difficult for users to find interesting resources. In this article, a hybrid personalized recommendation algorithm is proposed, which uses collaborative filtering and content‐based recommendation methods in turn to make recommendations. In the initial recommendation, Latent Dirichlet Allocation (LDA) topic model is used to reduce the dimension of high‐dimensional user behavior data and establish a user‐writing theme matrix to reduce inaccurate recommendation caused by high sparsity data in collaborative filtering algorithm. The user interest list is obtained by calculating the similarity between users. Then, on the basis of the preliminary recommendation results, VGG16 model is used to extract the feature vector of the calligraphy image and calculate the similarity between the user's calligraphy words and the primary recommended calligraphy words, thus obtaining the final recommendation results. The experimental results verify the effectiveness and accuracy of the recommendation algorithm, which are better than other recommendation algorithms on the whole, and have important engineering guiding significance.
Value of fuzzy logic for data mining and machine learning: A case study

In this paper, a case study on the role of fuzzy logic (FL) in data mining and machine learning is carried out. It is outlined that, in order to draw more attention of data-mining and machine-learning communities to FL, studies on FL could be more focused not on the activities that fuzzy methods can perform better but rather on the activities that fuzzy methods can perform and the non-fuzzy ones can’t. Such approach takes us away from discussing quantitative differences between fuzzy and non-fuzzy methods to discussing qualitative differences, which are possibly more favorable objects of scientific curiosity. Following the outlined suggestion, a novel speed-up technique is proposed in this paper to support association rule mining (ARM). The proposed technique is a clustering-based one and provides fusion of clustering and ARM. The catchy feature of this technique is that it works well if applied in fuzzy ARM and doesn’t work well if applied in non-fuzzy ARM. The proposed technique is put through experimental verification involving several real-world datasets, and the results substantiate its effectiveness.
Path planning of collision avoidance for unmanned ground vehicles: A nonlinear model predictive control approach

In addition to the safety of collision avoidance, the safety of lateral stability is another critical issue for unmanned ground vehicles in the high-speed condition. This article presents an integrated path planning algorithm for unmanned ground vehicles to address the aforementioned two issues. Since visibility graph method is a very practical and effective path planning algorithm, it is used to plan the global collision avoidance path, which can generate the shortest path across the static obstacles from the start point to the final point. To improve the quality of the planned path and avoid uncertain moving obstacles, nonlinear model predictive control is used to optimize the path and conduct second path planning with the consideration of lateral stability. Considering that the moving trajectories of moving obstacles are uncertain, multivariate Gaussian distribution and polynomial fitting are utilized to predict the moving trajectories of moving obstacles. In the collision avoidance algorithm design, a series of constraints are taken into consideration, including the minimum turning radius, safe distance, control constraint, tracking error, etc. Four simulation conditions are carried out to verify the feasibility and accuracy of the comprehensive collision avoidance algorithm. Simulation results indicate that the algorithm can deal with both static and dynamic collision avoidance, and lateral stability.
Security Requirements Engineering Framework with BPMN 2.0.2 Extension Model for Development of Information Systems

With recent advancements of technologies such as Internet of Things and cloud computing, security of information systems has emerged as a critical issue. This has created a need for elicitation and analysis of the security requirements at an early stage of system development. These requirements should also be expressed using visual notations that can encapsulate the vision of different stakeholders related to security. While business process management notation (version 2.0.2) is a widely used graphical representation for business requirements and makes it easier to define and communicate business processes between different stakeholders of the system. Moreover, extension mechanisms are available to model the specific needs of an organization. Due to its flexible structure for defining new extensions, it can be adapted to model security requirements in the information system (IS). Towards this, we propose a threat profile security framework to define the security requirements of manufacturing systems for businesses, which are at a stage of infancy to adapt or evolve the IS with the changing needs of a business environment. In particular, the framework is modeled by extending Business Process Management Notation and is applied in a manufacturing industry process at the shop floor level. We show through a case study example that the threat goal-based framework is broader and, hence, covers a majority of security concerns of organizations.
Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics.Classifying these views, opinions and comments is known as sentiment analysis which is an active research area.Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business.Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays.In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation.In this paper, we explore diverse feature sets and classifiers for sentiment quantification.In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-ofthe-art deep learning algorithms on basis of features set, is performed.The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification.The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.
ADeCNN: An Improved Model for Aspect-Level Sentiment Analysis Based on Deformable CNN and Attention

Aspect-level sentiment analysis aims at identifying the sentiment polarity of target in the context.In most of the previous sentiment analysis models, there usually exists the problem of insufficient extraction capability of local features and long-distance dependency features.To solve the above problem, in this paper, we propose an improved model (called ADeCNN) for aspect-level sentiment analysis, by incorporating the attention mechanism into the deformable CNN model.In ADeCNN, we use deformable convolutional layers and bi-directional long short-term memory network (Bi-LSTM), combined with sentence-level attention, to extract sentiment features, and to break through the limitations of the model's long-distance dependency feature extraction capability.We then use a gated end-to-end memory network (GMemN2N) to integrate the target into the sentiment feature extraction process, so as to obtain sentiment features.And finally, we obtain the corresponding sentiment analysis results through the classifier.In addition, in order to solve the problem that the same words have large differences in the polarity of sentiments expressed in different targets, the model is also constructed with the ability to generate different attention weights based on target to assist sentiment analysis, with the aim of further enhancing the correlation between the target and the words in the sentence.We setup experiments to demonstrate the functionality effectiveness and performance gains of ADeCNN, based on the SemEval 2014 Task4 and SemEval 2017 Task4 datasets.Extensive experimental results show that ADeCNN outperforms its competitors, producing an arresting increase of the classification accuracy on all the three datasets of Laptop, Restaurant, and Twitter.
Detection and Tracking of Moving Objects at Road Intersections Using a 360-Degree Camera for Driver Assistance and Automated Driving

Many complicated road intersections are seen while driving.In some, blind spots make it difficult for drivers or automated vehicles to discern moving objects coming from certain directions, possibly confusing drivers or autonomous vehicles wishing to cross or to turn at the intersection.To address this problem, we investigate detection and tracking of all moving objects at an intersection using a single 360-degree-view camera (3DVC).Through experiments, we develop methods allowing a 3DVC to capture the entirety of a four-way intersection when installed at one corner.This paper also presents image processing algorithms for detecting and tracking moving objects at intersections by processing images from the installed 3DVC.Experiments under varied conditions demonstrate that the proposed detection algorithm has a very high detection rate.We also confirm the tracking ability for moving objects detected using the proposed algorithm.
Towards Differentially Private Text Representations

Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server who has access to user information is ill-suited in many applications. To tackle this problem, we develop a new deep learning framework under an untrusted server setting, which includes three modules: (1) embedding module, (2) randomization module, and (3) classifier module. For the randomization module, we propose a novel local differentially private (LDP) protocol to reduce the impact of privacy parameter ε on accuracy, and provide enhanced flexibility in choosing randomization probabilities for LDP. Analysis and experiments show that our framework delivers comparable or even better performance than the non-private framework and existing LDP protocols, demonstrating the advantages of our LDP protocol.