Reviewerly/exHarmony huggingface dataset:
Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

Abstract With the increasing demand for a personalized product and rapid market response, many companies expect to explore online user-generated content (UGC) for intelligent customer hearing and product redesign strategy. UGC has the advantages of being more unbiased than traditional interviews, yielding in-time response, and widely accessible with a sheer volume. From online resources, customers’ preferences toward various aspects of the product can be exploited by promising sentiment analysis methods. However, due to the complexity of language, state-of-the-art sentiment analysis methods are still not accurate for practice use in product redesign. To tackle this problem, we propose an integrated customer hearing and product redesign system, which combines the robust use of sentiment analysis for customer hearing and coordinated redesign mechanisms. Ontology and expert knowledges are involved to promote the accuracy. Specifically, a fuzzy product ontology that contains domain knowledges is first learned in a semi-supervised way. Then, UGC is exploited with a novel ontology-based fine-grained sentiment analysis approach. Extracted customer preference statistics are transformed into multilevels, for the automatic establishment of opportunity landscapes and house of quality table. Besides, customer preference statistics are interactively visualized, through which representative customer feedbacks are concurrently generated. Through a case study of smartphone, the effectiveness of the proposed system is validated, and applicable redesign strategies for a case product are provided. With this system, information including customer preferences, user experiences, using habits and conditions can be exploited together for reliable product redesign strategy elicitation.
Design Considerations for Interacting and Navigating with 2 Dimensional and 3 Dimensional Medical Images in Virtual, Augmented and Mixed Reality Medical Applications

The extended realities, including virtual, augmented, and mixed realities (VAMR) have recently experienced significant hardware improvement resulting in an expansion in medical applications. These applications can be classified by the target end user (for instance, classifying applications as patient-centric, physician-centric, or both) or by use case (for instance educational, diagnostic tools, therapeutic tools, or some combination). When developing medical applications in VAMR, careful consideration of both the target end user and use case must heavily influence design considerations, particularly methods and tools for interaction and navigation. Medical imaging consists of both 2-dimensional and 3-dimensional medical imaging which impacts design, interaction, and navigation. Additionally, medical applications need to comply with regulatory considerations which will also influence interaction and design considerations. In this manuscript, the authors explore these considerations using three VAMR tools being developed for cardiac electrophysiology procedures.
Modelling and studying the effect of graph errors in graph signal processing

The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors. A little attention has been paid to modelling such errors in the adjacency matrix, and studying their effects on GSP methods. However, modelling errors in the adjacency matrix will enable both to study the graph error effects in GSP and to develop robust GSP algorithms. In this paper, we therefore introduce practically justifiable graph error models. We also study, both analytically when possible and numerically, the graph error effect on the performance of GSP methods in different types of problems such as filtering of graph signals and independent component analysis of graph signals (graph decorrelation).
Graph Constrained Data Representation Learning for Human Motion Segmentation

Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS). These approaches leverage prior knowledge from a source domain to improve clustering performance on a target domain, and currently they represent the state of the art in HMS. Bucking this trend, in this paper, we propose a novel unsupervised model that learns a representation of the data and digs clustering information from the data itself. Our model is reminiscent of temporal subspace clustering, but presents two critical differences. First, we learn an auxiliary data matrix that can deviate from the initial data, hence confers more degrees of freedom to the coding matrix. Second, we introduce a regularization term for this auxiliary data matrix that preserves the local geometrical structure present in the high-dimensional space. The proposed model is efficiently optimized by using an original Alternating Direction Method of Multipliers (ADMM) formulation allowing to learn jointly the auxiliary data representation, a nonnegative dictionary and a coding matrix. Experimental results on four benchmark datasets for HMS demonstrate that our approach achieves significantly better clustering performance then state-of-the-art methods, including both unsupervised and more recent semi-supervised transfer learning approaches <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Automated CNN back-propagation pipeline generation for FPGA online training

Abstract Training of convolutional neural networks (CNNs) on embedded platforms to support on-device learning has become essential for the future deployment of CNNs on autonomous systems. In this work, we present an automated CNN training pipeline compilation tool for Xilinx FPGAs. We automatically generate multiple hardware designs from high-level CNN descriptions using a multi-objective optimization algorithm that explores the design space by exploiting CNN parallelism. These designs that trade-off resources for throughput allow users to tailor implementations to their hardware and applications. The training pipeline is generated based on the backpropagation (BP) equations of convolution which highlight an overlap in computation. We translate the overlap into hardware by reusing most of the forward pass (FP) pipeline reducing the resources overhead. The implementation uses a streaming interface that lends itself well to data streams and live feeds instead of static data reads from memory. Meaning, we do not use the standard array of processing elements (PEs) approach, which is efficient for offline inference, instead we translate the architecture into a pipeline where data is streamed through allowing for new samples to be read as they become available. We validate the results using the Zynq-7100 on three datasets and varying size architectures against CPU and GPU implementations. GPUs consistently outperform FPGAs in training times in batch processing scenarios, but in data stream scenarios, FPGA designs achieve a significant speedup compared to GPU and CPU when enough resources are dedicated to the learning task. A 2.8×, 5.8×, and 3× speed up over GPU was achieved on three architectures trained on MNIST, SVHN, and CIFAR-10 respectively.
Similar Supergraph Search Based on Graph Edit Distance

Subgraph and supergraph search methods are promising techniques for the development of new drugs. For example, the chemical structure of favipiravir—an antiviral treatment for influenza—resembles the structure of some components of RNA. Represented as graphs, such compounds are similar to a subgraph of favipiravir. However, the existing supergraph search methods can only discover compounds that match exactly. We propose a novel problem, called similar supergraph search, and design an efficient algorithm to solve it. The problem is to identify all graphs in a database that are similar to any subgraph of a query graph, where similarity is defined as edit distance. Our algorithm represents the set of candidate subgraphs by a code tree, which it uses to efficiently compute edit distance. With a distance threshold of zero, our algorithm is equivalent to an existing efficient algorithm for exact supergraph search. Our experiments show that the computation time increased exponentially as the distance threshold increased, but increased sublinearly with the number of graphs in the database.
Vector Quantization for Adaptive State Aggregation in Reinforcement Learning

We propose an adaptive state aggregation scheme to be used along with temporal-difference reinforcement learning and value function approximation algorithms. The resulting algorithm constitutes a two-timescale stochastic approximation algorithm with: (a) a fast component that executes a temporal-difference reinforcement learning algorithm, and (b) a slow component, based on online vector quantization, that adaptively partitions the state space of a Markov Decision Process according to an appropriately defined dissimilarity measure. We study the convergence of the proposed methodology using Bregman Divergences as dissimilarity measures that can increase the efficiency and reduce the computational complexity of vector quantization algorithms. Finally, we quantify its performance on the Cart-pole (inverted pendulum) optimal control problem using Q-learning with adaptive state aggregation based on the Self-Organizing Map (SOM) algorithm.
Visualization of Medical Volume Data Based on Improved K-Means Clustering and Segmentation Rules

Accurate extraction and visualization of the target features are important in medical visualization, allowing the user to get a meaningful view of their targets.The traditional way to generate visualization results is 1D TF-based volume visualization, which uses a 1D -TF to determine the optical properties of each voxel.However, it is difficult to distinguish multiple targets by using the 1D TF-based volume visualization.It is also challenging to distinguish accurate targets when the objects' intensity values are similar in volume.Using the traditional transfer function usually fails to extract important target features and generates less accurate results.In this paper, we proposed extracting and segmentation techniques based on K-means algorithm that allows the users to segment and enhances single or multiple targets by a single point to view the features in 3D view.We have applied it to various univariate or multivariate volume datasets from the medical field to demonstrate its effectiveness.Moreover, we have performed both qualitative and quantitative experiments to compare its results against the results from two state-of-the-art techniques and the ground truths.The experimental results showed that our method is able to generate the closest results to the ground truth.
Feature Mining: A Novel Training Strategy for Convolutional Neural Network

In this paper, we propose a novel training strategy named Feature Mining for convolutional neural networks (CNNs) that aims to strengthen the network’s learning of the local features. Through experiments, we found that different parts of the feature contain different semantics, while the network will inevitably lose a large amount of local information during feedforward propagation. In order to enhance the learning of the local features, Feature Mining divides the complete feature into two complementary parts and reuses this divided feature to make the network capture different local information; we call the two steps Feature Segmentation and Feature Reusing. Feature Mining is a parameter-free method with a plug-and-play nature and can be applied to any CNN model. Extensive experiments demonstrated the wide applicability, versatility, and compatibility of our method.
Measuring affective states from technical debt

Abstract Context Software engineering is a human activity. Despite this, human aspects are under-represented in technical debt research, perhaps because they are challenging to evaluate. Objective This study’s objective was to investigate the relationship between technical debt and affective states (feelings, emotions, and moods) from software practitioners. Method Forty participants ( N = 40) from twelve companies took part in a mixed-methods approach, consisting of a repeated-measures ( r = 5) experiment ( n = 200), a survey, and semi-structured interviews. From the qualitative data, it is clear that technical debt activates a substantial portion of the emotional spectrum and is psychologically taxing. Further, the practitioners’ reactions to technical debt appear to fall in different levels of maturity. Results The statistical analysis shows that different design smells (strong indicators of technical debt) negatively or positively impact affective states. Conclusions We argue that human aspects in technical debt are important factors to consider, as they may result in, e.g., procrastination, apprehension, and burnout.
PupilTAN: A Few-Shot Adversarial Pupil Localizer

The eye center localization is a challenging problem faced by many computer vision applications. The challenges typically stem from the scene variability, such as, the wide range of shapes, the lighting conditions, the view angles and the occlusions. Nowadays, the increasing interest on deep neural networks requires a large volume of training data. However, a significant issue is the dependency on labeled data, which are expensive to obtain and susceptible to errors. To address these issues, we propose a deep network, dubbed PupilTAN, that performs image-to-heatmap Translation and an Adversarial training framework that solves the eye localization problem in a few-shot unsupervised way. The key idea is to estimate, by using only a few ground-truth shots, the heatmaps centers’ pdf and use it as a generator to create random heatmaps that follow the same probability distribution of the real ones. We showcase that training the deep network with these artificial heatmaps in an adversarial framework not only makes us less dependent on labeled data, but also leads to a significant accuracy improvement. The proposed network achieves real-time performance in a general-purpose computer environment and improves the state-of-the-art accuracy for both MUCT and BioID datasets, even compared with supervised techniques. Furthermore, our model is robust even in the case of reducing its size of up to 1/16 of the original network (0.2M parameters), demonstrating comparable accuracy to the state-of-the-art with high practical value to real-time applications.
Comfort Evaluation of Stereo Images Based on Machine Learning

Aiming at the difficult problem that how to evaluate comfort of stereo images effectively, this paper proposes a visual comfort evaluation model of stereo images based on machine learning. The job is conducted based on a focus area in images. The disparity map is calculated from a pair of stereo images. A primary salient area is obtained by the GBVS algorithm from right eye image, and the final stereo salient area is generated from both parallax information and primary salient area. Next, multiple types of features in the region are extracted. At last, the support vector regression model is used to predict the visual discomfort score. The experimental result shows that the proposed objective evaluation model is close to the subjective evaluation value, and it has achieved better results.
Understanding the Last Line of Defense: Human Response to Cybersecurity Events

Cybersecurity in consumer, corporate, and military settings, continues to be a growing concern in the modern and technologically driven world. As Wiederhold (2014) puts it, “the human factor remains the security’s weakest link in cyberspace.” A literature review related to human response to cybersecurity events reveals three phases involved in the cybersecurity response process, including: (1) Susceptibility, the phase preceding an event, which primarily encompasses behaviors that impact vulnerability to a cybersecurity event; (2) Detection of the event when it occurs; and (3) Response to the event after it occurs. In order for an individual to effectively protect themselves and their organizations from cybersecurity breaches, they must understand and be sensitive to the susceptibility of their devices, and when a potential breach occurs, must exhibit rapid and effective response. The goal of this effort was to examine the human factors surrounding non-expert response to a cybersecurity vulnerability or event and create a framework based on the literature. Recommendations for what steps can be taken to better prepare individuals to respond to cyber events is provided.
Accelerating Optimization and Reinforcement Learning with Quasi Stochastic Approximation

The paper sets out to obtain precise convergence rates for quasi-stochastic approximation (QSA), with applications to optimization and reinforcement learning. The main contributions are obtained for general nonlinear algorithms, under the assumption that there is a well defined linearization near the optimal parameter θ*, with Hurwitz linearization matrix A*. Subject to stability of the algorithm (general conditions are surveyed in the paper): (i)If the algorithm gain is chosen as a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> =g/(1+t) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ρ</sup> with g > 0 and ρ ∈ (0,1), then a “finite-t” approximation is obtained a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> {Θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> - θ*} = Y̅ + Ξ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sup> + o(1) where Θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> is the parameter estimate, Y ∈ ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> is a vector identified in the paper, and {Ξ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sup> } is bounded with zero mean. (ii)The approximation continues to hold with a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> =g/(1+t) under the stronger assumption that I+gA* is Hurwitz. (iii)The Ruppert-Polyak averaging technique is extended to this setting, in which the estimates {Θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> } are obtained using the gain in (i), and Θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RP</b> is defined to be the running average. The convergence rate is 1/t if and only if Y=0. (iv)The theory is illustrated with applications to gradient-free optimization, and policy gradient algorithms for reinforcement learning.
Improving Supervised Outlier Detection by Unsupervised Representation Learning and Generative Adversarial Networks

In this paper, a semi-supervised outlier detection algorithm is presented. The proposed method is an extension of XGBOD (Extreme Gradient Boosting Outlier Detection) developed by Zhao and Hryniewicki and combines the benefits of both unsupervised representation learning and generative adversarial networks. The previous studies show that multiple unsupervised outlier detection algorithms extract useful representations from the underlying data. The results presented herein show that the augmentation of their extracted representations by GANs (Generative Adversarial Networks) further improves the performance of supervised outlier detection on several datasets.
Quantum Asymmetry and Noisy Multimode Interferometry

Quantum asymmetry is a physical resource that coincides with the amount of coherence between the eigenspaces of a generator responsible for phase encoding in interferometric experiments. We highlight an apparently counterintuitive behavior that the asymmetry may increase as a result of a decrease of coherence inside a degenerate subspace. We intuitively explain and illustrate the phenomena by performing a three-mode single-photon interferometric experiment, where one arm carries the signal and two noisy reference arms have fluctuating phases. We show that the source of the observed sensitivity improvement is the reduction of correlations between these fluctuations and comment on the impact of the effect when moving from the single-photon quantum level to the classical regime. Finally, we also establish the analogy of the effect in the case of entanglement resource theory.
<i>Break, Perturb, Build</i>: Automatic Perturbation of Reasoning Paths Through Question Decomposition

Abstract Recent efforts to create challenge benchmarks that test the abilities of natural language understanding models have largely depended on human annotations. In this work, we introduce the “Break, Perturb, Build” (BPB) framework for automatic reasoning-oriented perturbation of question-answer pairs. BPB represents a question by decomposing it into the reasoning steps that are required to answer it, symbolically perturbs the decomposition, and then generates new question-answer pairs. We demonstrate the effectiveness of BPB by creating evaluation sets for three reading comprehension (RC) benchmarks, generating thousands of high-quality examples without human intervention. We evaluate a range of RC models on our evaluation sets, which reveals large performance gaps on generated examples compared to the original data. Moreover, symbolic perturbations enable fine-grained analysis of the strengths and limitations of models. Last, augmenting the training data with examples generated by BPB helps close the performance gaps, without any drop on the original data distribution.
Software Process Improvement During the Last Decade: A Theoretical Mapping and Future Avenues

Studies have been conducted in Software process improvement (SPI) for decades. These studies discussed different aspects related to SPI like small and medium enterprise (SME), critical success factors, and gamification. Some researchers conjectured that typical SPI is progressively put into the backdrop. Nevertheless, no methodical study was found utilizing bibliometric analysis of the SPI studies to further explore the related research in this domain. This paper presents a summary of the SPI recent studies collected from "Web of Science" database. VOSviewer was used with bibliometric analysis technique to visualize the literature contained in our study scope. As a result, six clusters were found converged on these fundamental thematic sections: SME, Capability Maturity Model Integration (CMMI), critical success factors, Agile, gamification, and knowledge management. Recommendations are provided in this paper for future research on SPI based on the present gaps. Serving as R&D guidance to the SPI research community.