Reviewerly/exHarmony huggingface dataset:
Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations

Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights into the structure of problem landscapes and to use in selecting an effective algorithm for a given optimization problem. While there has been some success, evaluating the utility of problem features in practice presents some significant challenges. Machine learning models have been employed as part of the evaluation process, but they may require additional information about the problems as well as having their own hyper-parameters, biases and experimental variability. As a result, extra layers of uncertainty and complexity are added into the experimental evaluation process, making it difficult to clearly assess the effect of the problem features. In this article, we propose a novel method for the evaluation of problem features which can be applied directly to individual or groups of features and does not require additional machine learning techniques or confounding experimental factors. The method is based on the feature's ability to detect a prior ranking of similarity in a set of problems. Analysis of Variance (ANOVA) significance tests are used to determine if the feature has successfully distinguished the successive problems in the set. Based on ANOVA test results, a percentage score is assigned to each feature for different landscape characteristics. Experimental results for twelve different features on four problem transformations demonstrate the method and provide quantitative evidence about the ability of different problem features to detect specific properties of problem landscapes.
An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection

Today illegal activities regarding online financial transactions have become increasingly complex and borderless, resulting in huge financial losses for both sides, customers and organizations. Many techniques have been proposed to fraud prevention and detection in the online environment. However, all of these techniques besides having the same goal of identifying and combating fraudulent online transactions, they come with their own characteristics, advantages and disadvantages. In this context, this paper reviews the existing research done in fraud detection with the aim of identifying algorithms used and analyze each of these algorithms based on certain criteria. To analyze the research studies in the field of fraud detection, the systematic quantitative literature review methodology was applied. Based on the most called machine-learning algorithms in scientific articles and their characteristics, a hierarchical typology is made. Therefore, our paper highlights, in a new way, the most suitable techniques for detecting fraud by combining three selection criteria: accuracy, coverage and costs.
Relative efficiency of Gafchromic EBT3 and MD-V3 films exposed to low-energy photons and its influence on the energy dependence

Energy-dependence of Gafchromic films exposed to low-energy photons has been reported to be a function of absorbed-dose. However, these studies are based on a relative-response, R, which considers the absorbed-dose in water and not within the film sensitive-volume. This work investigated the relative-efficiency, REfilm, (ratio of absorbed-dose required to produce the same net optical density (netOD) by 60Co gamma and by x-ray) of Gafchromic EBT3 and MD-V3 films exposed to five x-ray beams from 20 kV to 160 kV and 60Co gamma rays. A factor that accounts for the energy-dependence, f x , Q , m e d , based on REfilm, phantom-material and depth at which the films are placed during irradiation was used to remove the influence of absorbed dose. Values of REfilm indicated that the absorbed dose from 60Co gamma rays needs to be 4 and 3 times larger than those from 20 kV x-rays to produce the same netOD within the EBT3 and MD-V3 sensitive volumes, respectively. Thus, saturation could help explain why Gafchromic films show under-response to very low doses from low-energy photon beams, regardless of film model. Furthermore, REfilm, was found to be nearly independent of netOD and colour-channels. Consequently, f x , Q , m e d is independent of the absorbed dose and colour-channels. In contrast, besides the variation with the photon energy, f x , Q , m e d varied with film model, depth and phantom material used during the irradiation. Thus, the results suggest that f x , Q , m e d is a more reliable wide-ranging parameter for evaluating the degree of energy-dependence of the film rather than the relative-response method commonly considered.
Exploring the Reasons Why University Students Prefer Print over Digital Texts: An Australian Perspective

This paper presents findings from an Australian survey that provides an analysis of the reasons why university students prefer to read print or digital course materials. This survey was undertaken as part of the Academic Reading Format International Study (ARFIS), which is investigating print versus digital reading preferences in 31 countries (Mizrachi et al., 2018). A total of 582 students completed the survey with 471 students providing comments. This article provides an in-depth analysis of the comments from the survey. The comments provided in this survey provide rich data on the reasons why students prefer reading in print or electronic formats, an area of importance due to the increasing prevalence of digital only collections in university libraries. Based on comments, students preferred print due to reasons such as accessibility, eyestrain, tactile features, better focus and ability to highlight and take notes. Although students prefer print, they acknowledge the convenience and cheaper costs of e-texts. Students who were e-preferring were influenced by issues such as cost, environment and convenience. In light of library collections prioritising digital content, tertiary institutions and libraries should carefully consider the role that print textbooks and collections, as well as printing services, may play in supporting student learning.
Domain adaptation for part-of-speech tagging of noisy user-generated text

Luisa März, Dietrich Trautmann, Benjamin Roth. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer

Abstract A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.
Rolling Force Prediction of Hot Rolling Based on GA-MELM

In the hot continuous rolling process, the main factor affecting the actual thickness of strip is the rolling force. The precision of rolling force calculation is the key to realize accurate on-line control. However, because of the complexity and nonlinearity of the rolling process, as well as many influencing factors, the theoretical analysis of the traditional rolling force prediction model often needs to be simplified and hypothesized. This leads to the incompleteness of the mathematical model and the deviation between the calculated results and the actual working conditions. In this paper, a rolling force prediction method based on genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multiple hidden layer extreme learning machine (MELM) is proposed, namely, PSO-GA-MELM algorithm, which takes MELM as the basic model for rolling force prediction. In the modeling process, GA is used to determine the optimal number of hidden layers and the optimal number of hidden nodes, and PSO is used to search for the optimal input weights and biases. This method avoids the influence of human intervention on the model and saves the modeling time. This paper takes the actual production data of BaoSteel 2050 production line as experimental data, and the experimental results indicate that the algorithm can be effectively used to determine the optimal network structure of MELM. The rolling force prediction model trained by the algorithm has excellent performance in prediction accuracy, computational stability, and the number of hidden nodes and is applicable to the prediction of rolling force in hot continuous rolling process.
Synthesizing Depth Hand Images with GANs and Style Transfer for Hand Pose Estimation

Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.
State-Regularized Recurrent Neural Networks

Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We evaluate state-regularized RNNs on (1) regular languages for the purpose of automata extraction; (2) nonregular languages such as balanced parentheses, palindromes, and the copy task where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition, and language modeling. We show that state-regularization (a) simplifies the extraction of finite state automata modeling an RNN's state transition dynamics; (b) forces RNNs to operate more like automata with external memory and less like finite state machines; (c) makes RNNs have better interpretability and explainability.
Quantum isomer search

Isomer search or molecule enumeration refers to the problem of finding all the isomers for a given molecule. Many classical search methods have been developed in order to tackle this problem. However, the availability of quantum computing architectures has given us the opportunity to address this problem with new (quantum) techniques. This paper describes a quantum isomer search procedure for determining all the structural isomers of alkanes. We first formulate the structural isomer search problem as a quadratic unconstrained binary optimization (QUBO) problem. The QUBO formulation is for general use on either annealing or gate-based quantum computers. We use the D-Wave quantum annealer to enumerate all structural isomers of all alkanes with fewer carbon atoms (n < 10) than Decane (C10H22). The number of isomer solutions increases with the number of carbon atoms. We find that the sampling time needed to identify all solutions scales linearly with the number of carbon atoms in the alkane. We probe the problem further by employing reverse annealing as well as a perturbed QUBO Hamiltonian and find that the combination of these two methods significantly reduces the number of samples required to find all isomers.
Multimodal Camera-Based Gender Recognition Using Human-Body Image With Two-Step Reconstruction Network

With the recent development of intelligent surveillance systems, the importance of research study on gender recognition of people at a distance is also on the rise. The existing gender recognition technologies studies have used high-resolution facial images captured from the front at a short distance, which showed high performance. However, intelligent surveillance systems in actual real environments have difficulty in detecting the faces of people because they use images captured from a distance. Moreover, in the case of back-view images, gender recognition based on the facial image is impossible because the face cannot be detected. Thus, gender recognition using the full-body human-body images of people is being studied but its performance is low owing to problems such as low resolution, motion blur, and optical blur. Furthermore, the performance of gender recognition using only visible-light cameras is limited owing to illumination variations, shadow, and the type of clothes and accessories. To solve these problems, remote body-shape-based recognition was performed by sequentially using two convolutional neural networks which improved the resolution of visible-light images. In addition, the degradation of recognition performance owing to various factors (e.g., illumination, shadow, and the type of clothes and accessories) was prevented by combining a visible-light camera with an infrared camera, and the scalability was enhanced using various heterogeneous cameras. The higher performance of the proposed method compared with that of other methods was verified through a comparative experiment using the open database of Sun Yat-sen University multiple modality Re-ID (SYSU-MM01) and the Dongguk body-based gender database (DBGender-DB2) that has been built by us.
Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm

As an effective way to learn a distance metric between pairs of samples, deep metric learning (DML) has drawn significant attention in recent years. The key idea of DML is to learn a set of hierarchical nonlinear mappings using deep neural networks, and then project the data samples into a new feature space for comparing or matching. Although DML has achieved practical success in many applications, there is no existing work that theoretically analyzes the generalization error bound for DML, which can measure how good a learned DML model is able to perform on unseen data. In this paper, we try to fill up this research gap and derive the generalization error bound for DML. Additionally, based on the derived generalization bound, we propose a novel DML method (called ADroDML), which can adaptively learn the retention rates for the DML models with dropout in a theoretically justified way. Compared with existing DML works that require predefined retention rates, ADroDML can learn the retention rates in an optimal way and achieve better performance. We also conduct experiments on real-world datasets to verify the findings derived from the generalization error bound and demonstrate the effectiveness of the proposed adaptive DML method.
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.
Value formation with immersive technologies: an activity perspective

Purpose The purpose of this paper is to contribute a framework that explains how value is formed during the usage of immersive technologies in industrial contexts. Design/methodology/approach Drawing on activity theory and a customer-dominant logic, the authors tentatively develop an activity-centric framework for value formation enabled by physical and mental activities conducted by users of immersive technologies. The authors evaluate the framework through a case study focusing on the use of virtual reality (VR) in an industrial setting. Findings The findings from the case study illustrate the tentative framework and specify how it is enacted by users in the studied context through three physical activities constituted by a set of actions and reflected in five emotional responses. Research limitations/implications Both researchers and practitioners may use the framework presented in this paper as a guide for further academic and practical developments concerning the value of immersive technologies such as VR and augmented reality. Originality/value The activity-centric framework contributes a novel perspective to the literature on value formation enabled by immersive technologies.
SPRING: A Sparsity-Aware Reduced-Precision Monolithic 3D CNN Accelerator Architecture for Training and Inference

Convolutional neural networks (CNNs) outperform traditional machine learning algorithms across a wide range of applications, such as object recognition, image segmentation, and autonomous driving. However, their ever-growing computational complexity makes it necessary to design efficient hardware accelerators. Most CNN accelerators focus on exploring various dataflow styles and designs that exploit computational parallelism. However, potential performance improvement (speedup) from sparsity has not been adequately addressed. The computation and memory footprint of CNNs can be significantly reduced if sparsity is exploited in network evaluations. To further improve performance and energy efficiency, some accelerators evaluate CNNs with limited precision. However, this is limited to the inference phase since reduced precision sacrifices network accuracy if used in training. In addition, CNN evaluation is usually memory-intensive, especially during training. The performance bottleneck arises from the fact that the memory cannot feed the computational units enough data, resulting in idling of these computational units and thus low utilization ratios. In this article, we propose SPRING, a SParsity-aware Reduced-precision Monolithic 3D CNN accelerator for trainING and inference. SPRING supports both CNN training and inference. It uses a binary mask scheme to encode sparsities in activations and weights. It uses the stochastic rounding algorithm to train CNNs with reduced precision without accuracy loss. To alleviate the memory bottleneck in CNN evaluation, especially during training, SPRING uses an efficient monolithic 3D nonvolatile memory interface to increase memory bandwidth. Compared to Nvidia GeForce GTX 1080 Ti, SPRING achieves 15.6×, 4.2×, and 66.0× improvements in performance, power reduction, and energy efficiency, respectively, for CNN training, and 15.5×, 4.5×, and 69.1× improvements in performance, power reduction, and energy efficiency, respectively, for inference.
A Game Theoretic Approach for Dynamic Information Flow Tracking with Conditional Branching

In this paper, we study system security against Advanced Persistent Threats (APTs). APTs are stealthy and persistent but APTs interact with system and introduce information flows in the system as data-flow and control-flow commands. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism against APTs which taints suspicious input sources in the system and performs online security analysis when a tainted information is used in unauthorized manner. Our objective in this paper is to model DIFT that handle data-flow and conditional branches in the program that arise from control-flow commands. We use game theoretic framework and provide the first analytical model of DIFT with data-flow and conditional-branch tracking. Our game model which is an undiscounted infinite-horizon stochastic game captures the interaction between APTs and DIFT and the notion of conditional branching. We prove that the best response of the APT is a maximal reachability probability problem and provide a polynomial-time algorithm to find the best response by solving a linear optimization problem. We formulate the best response of the defense as a linear optimization problem and show that an optimal solution to the linear program returns a deterministic optimal policy for the defense. Since finding Nash equilibrium for infinite-horizon undiscounted stochastic games is computationally difficult, we present a nonlinear programming based polynomial-time algorithm to find an E-Nash equilibrium. Finally, we perform experimental analysis of our algorithm on real-world data for NetRecon attack augmented with conditional branching.
Conversion of Knill–Laflamme–Milburn Entanglement to Greenberger–Horne–Zeilinger Entanglement

Abstract Schemes for converting photonic polarized‐entangled Knill–Laflamme–Milburn (KLM) states to Greenberger–Horne–Zeilinger (GHZ) states are proposed using weak cross‐Kerr nonlinearity and X‐quadrature homodyne measurement. Analyses show that the two‐qubit (Bell state) and three‐qubit conversion cases have very high fidelities and close‐to‐unity probabilities. The conversion processes are robust against photon loss. The schemes linking these two entangled states may be helpful to the study of quantum information processing based on them.