Reviewerly/exHarmony huggingface dataset:
Fast Training Methods and Their Experiments for Deep Learning CNN Models

Artificial intelligence and its deep learning have broad application areas, including medical diagnosis, industrial area, data analysis, identification, system control, self-driving, and games. Deep learning methods show the advantage of using neural networks to solve complex tasks. The network training speed is highly dependent on the hardware environments and its training methodology. Training a large dataset on a Convolutional Neural Network (CNN) can last for weeks with a limited number of GPUs. Although cloud training is a good method to train large networks within a short time, it is usually costly. What is more, since a good network needs a lot of parameter tunings to get an optimal result, a network needs to train multiple times with different parameter settings. The training speed and the cost for large networks have become a constraint to verify algorithm results when computing resources are limited. Thus, fast training a large network with a limited number of computing resources is needed and challenging, especially for large datasets. In this paper, we propose new fast training methods to accelerate the CNNs training time from the data sampling level. We introduce a flat reduced random sampling strategy and a bottleneck reduced random sampling strategy for deep learning model training. Furthermore, the theoretical analysis for the proposed fast training methods are derived by four theorems and two corollaries to show the properties and benefits of the proposed new methods. The experiments of these fast training methods are run on three image classification datasets: CIFAR-10, CIFAR-100 and ImageNet. The experimental results show that our proposed new training methods can achieve significant training time percentage reduction with a very small accuracy cost correspondingly, i.e., to well provide the fast training method for deep learning CNN models as our theoretical analysis shows and predicts.
Set-Based State Estimation With Probabilistic Consistency Guarantee Under Epistemic Uncertainty

Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a set-based estimation algorithm, named Gaussian Process-Zonotopic Kalman Filter (GP-ZKF), that produces zonotopic state estimates while respecting both the epistemic uncertainties in the learned models and aleatoric uncertainties. Our method guarantees probabilistic consistency, in the sense that the true states are bounded by sets (zonotopes) across all time steps, with high probability. We formally relate GP-ZKF with the corresponding stochastic approach, GP-EKF, in the case of learned (nonlinear) models. In particular, when linearization errors and aleatoric uncertainties are omitted and epistemic uncertainties are simplified, GP-ZKF reduces to GP-EKF. We empirically demonstrate our method’s efficacy in both a simulated pendulum domain and a real-world robot-assisted dressing domain, where GP-ZKF produced more consistent and less conservative set-based estimates than all baseline stochastic methods.
Explaining the Suspicion: Design of an XAI-Based User-Focused Anti-Phishing Measure

Phishing attacks are the primary cause of data and security breaches in businesses, public institutions, and private life. Due to inherent limitations and users’ high susceptibility to increasingly sophisticated phishing attempts, existing anti-phishing measures cannot realize their full potential. Against this background, we utilize methods from the emerging research field of Explainable Artificial Intelligence (XAI) for the design of a user-focused anti-phishing measure. By leveraging the power of state-of-the-art phishing detectors, our approach uncovers the words and phrases in an e-mail most relevant for identifying phishing attempts. We empirically show that our approach reliably extracts segments of text considered relevant for the discrimination between genuine and phishing e-mails. Our work opens up novel prospects for phishing prevention and demonstrates the tremendous potential of XAI methods beyond applications in AI.
Environment Reconfiguration Planning for Autonomous Robotic Manipulation to overcome Mobility Constraints

This paper presents a novel strategy for intelligent robotic environment reconfiguration applied to overcome mobility constraints with an autonomously exploring mobile manipulation system. A realistic problem arising during exploration of unknown challenging environments is the encountering of untraversable areas –given the robot’s mobility constraints– resulting in the robot getting stuck. We propose that given manipulation capabilities of an autonomous system, it should be possible to leverage loose entities in its surrounding to reconfigure its environment, and therefore potentially restore traversability to an unreachable region. This work’s contribution is two-fold: first, it proposes a mid-range traversability estimation graph-based backend which also allows early detection of terrain gaps, and secondly, it provides an algorithm for focused environment alteration that ensures stable and valid configurations. The plans of this generic policy are evaluated to decide if they resolve the robot’s problem, and are subsequently applied. The effectiveness of the proposed approach is demonstrated via experimental studies using a relevant autonomous system within a mobility-constrained mock-up environment.
Bi-3D: Bi-Manual Pen-and-Touch Interaction for 3D Manipulation on Tablets

Tablets are attractive for design work anywhere, but 3D manipulations are notoriously difficult. We explore how engaging the stylus and multi-touch in concert can render such tasks easier. We introduce Bi-3D, an interaction concept where touch gestures are combined with 2D pen commands for 3D manipulation. For example, for a fast and intuitive 3D drag & drop technique: the pen drags the object on-screen, and parallel pinch-to-zoom moves it in the third dimension. In this paper, we describe the Bi-3D design space, crossing two-handed input and the degrees-of-freedom (DOF) of 3D manipulation and navigation tasks. We demonstrate sketching and manipulation tools in a prototype 3D design application, where users can fluidly combine 3D operations through alternating and parallel use of the modalities. We evaluate the core technique, bi-manual 3DOF input, against widget and mid-air baselines in an object movement task. We find that Bi-3D is a fast and practical way for multi-dimensional manipulation of graphical objects, promising to facilitate 3D design on stylus and tablet devices.
Localizing Human Keypoints beyond the Bounding Box

Since human pose is one of the most effective and popular sources for understanding human in various applications, there have been numerous researches on detecting keypoints of human body from the image source. However, when a human body is shown partially in the source image, estimation range is also restricted causing performance degradation in locating keypoints of human body. In this paper, we propose ‘Position Puzzle’ network and augmentation to leverage the performance of detecting keypoints including those outside the bounding box. Specifically, Position Puzzle Network expands the spatial range of keypoint localization by refining the position and the scale of the target’s bounding box, and Position Puzzle Augmentation improves the performance of keypoint detector using the partial image in training. We prepare data by cropping COCO dataset and utilize them in training and evaluation. Under the prepared dataset, the proposed method enhances the performance of baseline network up to 37.6% and 30.6% in mAP and mAR, respectively, and effectively localizes keypoints positioned not only inside but also outside the bounding box. We also verify that the proposed method can localize keypoints beyond the bounding box in the original COCO dataset.
An Evaluation Approach of Multi-person Movement Synchronization Level using OpenPose

In dance, broadcast gymnastics and other activities, participants are required to keep their action in synchronization, which is very important to show their uniformity. In this paper, we present an approach to quantitatively evaluate the synchronization level of multi-person movement. Firstly, Kinect is used to obtain color image and depth image, and use OpenPose to process color image to acquire 2D joint point coordinates of all participants in the image; Secondly, color image and depth image are fused into point cloud array, and retrieve 3D joint point coordinates corresponding to 2D from point cloud array; Then, according to 3D joint point coordinates, define three joint angles that can represent the human pose; Finally, after smoothing, calculate the evaluation index to achieve the quantitative evaluation. The experimental results demonstrate that when there are fewer than 5 participants, this method can be applied to evaluate the synchronization level of their movements, it has good accuracy and reliability.
Gradually structured data

Dynamically-typed languages offer easy interaction with ad hoc data such as JSON and S-expressions; statically-typed languages offer powerful tools for working with structured data, notably algebraic datatypes , which are a core feature of typed languages both functional and otherwise. Gradual typing aims to reconcile dynamic and static typing smoothly. The gradual typing literature has extensively focused on the computational aspect of types, such as type safety, effects, noninterference, or parametricity, but the application of graduality to data structuring mechanisms has been much less explored. While row polymorphism and set-theoretic types have been studied in the context of gradual typing, algebraic datatypes in particular have not, which is surprising considering their wide use in practice. We develop, formalize, and prototype a novel approach to gradually structured data with algebraic datatypes. Gradually structured data bridges the gap between traditional algebraic datatypes and flexible data management mechanisms such as tagged data in dynamic languages, or polymorphic variants in OCaml. We illustrate the key ideas of gradual algebraic datatypes through the evolution of a small server application from dynamic to progressively more static checking, formalize a core functional language with gradually structured data, and establish its metatheory, including the gradual guarantees.
JavaDL: automatically incrementalizing Java bug pattern detection

Static checker frameworks support software developers by automatically discovering bugs that fit general-purpose bug patterns. These frameworks ship with hundreds of detectors for such patterns and allow developers to add custom detectors for their own projects. However, existing frameworks generally encode detectors in imperative specifications, with extensive details of not only what to detect but also how . These details complicate detector maintenance and evolution, and also interfere with the framework’s ability to change how detection is done, for instance, to make the detectors incremental. In this paper, we present JavaDL, a Datalog-based declarative specification language for bug pattern detection in Java code. JavaDL seamlessly supports both exhaustive and incremental evaluation from the same detector specification. This specification allows developers to describe local detector components via syntactic pattern matching , and nonlocal (e.g., interprocedural) reasoning via Datalog-style logical rules . We compare our approach against the well-established SpotBugs and Error Prone tools by re-implementing several of their detectors in JavaDL. We find that our implementations are substantially smaller and similarly effective at detecting bugs on the Defects4J benchmark suite, and run with competitive runtime performance. In our experiments, neither incremental nor exhaustive analysis can consistently outperform the other, which highlights the value of our ability to transparently switch execution modes. We argue that our approach showcases the potential of clear-box static checker frameworks that constrain the bug detector specification language to enable the framework to adapt and enhance the detectors.
Effects of Domain Randomization on Simulation-to-Reality Transfer of Reinforcement Learning Policies for Industrial Robots

A requirement for a significant amount of training as well as the exploration of potentially expensive or safety-critical states limits the applicability of reinforcement learning for real-world robotics. One potential solution is given by pretraining models in simulations before transferring them to the real world. In this chapter, we investigate the concept of domain randomization to train robust agents in simulation to control an industrial robot. We examine the effects of different degrees of randomization with respect to the transferability to the real world. In addition, we use attention maps to gain insights into the agents’ decision-making processes. We find that attention maps enable a qualitative assessment for the data-efficiency of a pretrained agent when transferred to the real-world setup.
Isotopic exchange of oxygen, sulfur, hydrogen and copper between aqueous phase and the copper minerals brochantite, libethenite and olivenite

Abstract Fractionation factors for the isotopes of O, H, S, or Cu (as appropriate) were determined for the minerals brochantite [Cu 4 (SO 4 )(OH) 6 ], libethenite [Cu 2 (PO 4 )(OH)] and olivenite [Cu 2 (AsO 4 )(OH)] and corresponding aqueous solutions at temperatures between 30 and 70°C. All samples used for this determination were synthetic and the degree of fractionation was expressed as 1000 ln α = ( A × 10 6 / T 2 ) + B , where A and B are empirical parameters. A few natural libethenite samples from its type locality Ľubietová-Podlipa were also analysed and compared to the prediction based on the isotopic composition of meteoric water and our fractionation factors. The hydrogen fractionation factors agreed with the prediction well, whereas those for oxygen did not. A possible explanation is the disequilibrium of aqueous phosphate (and also arsenate) species and the solution in our experiments or the interaction of meteoric fluids with the isotopically heavy (in terms of oxygen) country rocks. Because the effects of isotopic disequilibrium in our experiments cannot be ruled out, the oxygen fractionation factors should be used with caution. The determined fractionation factors can be used as an isotope geothermometer, given that it can be proven that the phases of interest precipitated from the same fluid in equilibrium. Libethenite is predicted to have slightly lower δ 65 Cu values than its parental solution, but brochantite slightly higher δ 65 Cu values than its parental solution. Simple forward models, simulating neutralisation or reduction of mine drainage, show that precipitation of these minerals and removal of the co-existing fluid, could cause isotopic variations (in δ 65 Cu) on the order of 1‰ or more.
Image Style Transfer with Generative Adversarial Networks

Image style transfer is a recently popular research field, which aims to learn the mapping between different domains and involves different computer vision techniques. Recently, Generative Adversarial Networks (GAN) have demonstrated their potentials of translating images from source domain X to target domain Y in the absence of paired examples. However, such a translation cannot guarantee to generate high perceptual quality results. Existing style transfer methods work well with relatively uniform content, they often fail to capture geometric or structural patterns that reflect the quality of generated images. The goal of this doctoral research is to investigate the image style transfer approaches, and design advanced and useful methods to solve existing problems. Though preliminary experiments conducted so far, we demonstrate our insights on the image style translation approaches, and present the directions to be pursued in the future.
Migrating to Hosted EZproxy: Our Library’s Experience

The West Virginia University Libraries migrated to OCLC’s Hosted EZproxy service in 2020. The Libraries’ main goals with the migration were to reduce staff time spent on proxy maintenance, customize user access to resources, and increase analytics about resource usage. The migration process involved sending configuration and user data to OCLC, updating vendors with a new IP address and proxy URL, and creating specialized user groups. The following details the Libraries’ experience during this project.
Machine Learning Target Count Prediction in Electromagnetics Using Neural Networks

In this article, we showcase an application of neural networks (NNs) to solve an inverse problem in electromagnetics (EMs). Wires are randomly distributed into an area of known dimensions. The wires are then illuminated with a monochromatic plane wave (PW) at a certain angle of incidence, and the EM field measured at a finite number of uniformly spaced points along the perimeter of the area is then fed into a convolutional neural network (CNN) designed to predict the number of wires. Counting the wires is posed as a supervised classification problem with a known upper limit to the number of wires, and accuracy of 96% has been achieved for the case where the number of the wires is known to be ten or less. A number of approaches have been taken to improve the network performance including frequency variation analysis and illuminating the wire distributions with additional PW angles of incidence. We conclude with an analysis of the network capability to resolve objects based on its performance on known wire distributions, which suggests the existence of a characteristic resolution limit corresponding to the CNN topology.
Effects of colored noise on the dynamics of quantum entanglement of a one-parameter qubit–qutrit system

We analyzed the effect of colored noise on the negativity dynamics of a hybrid system consisting of a qubit–qutrit and not interacting, prepared from the start in an entangled one-parameter state and acting with noise in local and non-local environments. In this pink and brown noise we investigated two different situations: in the first situation, the noise is produced by a bistable oscillator with an unknown exchange rate; however, in the second situation, the noise is generated by a set of bistable oscillators. We found that entanglement decreases with time to zero, and undergoes the phenomenon of sudden death and rebirth. The pink noise is more prone to entanglement than the brown noise and the non-local environment is more prone to entanglement than the local one. When the number of fluctuators is increased, entanglement decays faster and finally, for certain parameters of the initial state, the subsystems are not affected by the noise.
Hierarchy of continuous-variable quantum resource theories

Connections between the resource theories of coherence and purity (or non-uniformity) are well known for discrete-variable, finite-dimensional, quantum systems. We establish analogous results for continuous-variable systems, in particular Gaussian systems. To this end, we define the concept of maximal coherence at fixed energy, which is achievable with energy-preserving unitaries. We show that the maximal Gaussian coherence (where states and operations are required to be Gaussian) can be quantified analytically by the relative entropy. We then propose a resource theory of non-uniformity, by considering the purity of a quantum state at fixed energy as resource, and by defining non-uniformity monotones. In the Gaussian case, we prove the equality of Gaussian non-uniformity and maximal Gaussian coherence. Finally, we show a hierarchy for non-uniformity, coherence, discord and entanglement in continuous-variable systems.
Face Identification System Based on Synthesizing Realistic Image using Edge-Aided GANs

Abstract Presently, facial image recognition via a thermal camera is a critical phase in numerous fields. Systems using thermal facial images suffer from numerous problems in face identification. In this paper, a model Edge-Aided Generative Adversarial Network (EA-GAN) is introduced to overcome the difficulties of thermal face identification by synthesizing a visible faces image from the thermal version. To enhance the performance of the Conditional Generative Adversarial Network (CGAN) model for the create realistic face images, the edge information extracted from the thermal image has been used as input, thus lead to improving overall the system's achievement. Moreover, a new model is presented in the present work for face identification by integrating two Convolutional Neural Networks (CNN) to achieve high and rapid accuracy rates. Based on the experiments on the Carl dataset for faces, it is indicated that EA-GAN can synthesize visually comfortable and identity-preserving faces; thus, better performance is achieved in comparison with the state-of-the-art approaches for thermal facial identification.
Learning Topic Models: Identifiability and Finite-Sample Analysis

Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of methods have been proposed for topic modeling, lacking in the literature is a formal theoretical investigation of the statistical identifiability and accuracy of latent topic estimation. In this article, we propose a maximum likelihood estimator (MLE) of latent topics based on a specific integrated likelihood that is naturally connected to the concept, in computational geometry, of volume minimization. Our theory introduces a new set of geometric conditions for topic model identifiability, conditions that are weaker than conventional separability conditions, which typically rely on the existence of pure topic documents or of anchor words. Weaker conditions allow a wider and thus potentially more fruitful investigation. We conduct finite-sample error analysis for the proposed estimator and discuss connections between our results and those of previous investigations. We conclude with empirical studies employing both simulated and real datasets. Supplementary materials for this article are available online.
Exclusion statistics for particles with a discrete spectrum

We formulate and study the microscopic statistical mechanics of systems of particles with exclusion statistics in a discrete one-body spectrum. The statistical mechanics of these systems can be expressed in terms of effective single-level grand partition functions obeying a generalization of the standard thermodynamic exclusion statistics equation of state. We derive explicit expressions for the thermodynamic potential in terms of microscopic cluster coefficients and show that the mean occupation numbers of levels satisfy a nesting relation involving a number of adjacent levels determined by the exclusion parameter. We apply the formalism to the harmonic Calogero model and point out a relation with the Ramanujan continued fraction identity and appropriate generalizations.