Reviewerly/exHarmony huggingface dataset:
Deep Embedding Using Bayesian Risk Minimization with Application to Sketch Recognition

In this paper, we address the problem of hand-drawn sketch recognition. Inspired by the Bayesian decision theory, we present a deep metric learning loss with the objective to minimize the Bayesian risk of misclassification. We estimate this risk for every mini-batch during training, and learn robust deep embeddings by backpropagating it to a deep neural network in an end-to-end trainable paradigm. Our learnt embeddings are discriminative and robust despite of intra-class variations and inter-class similarities naturally present in hand-drawn sketch images. Outperforming the state of the art on sketch recognition, our method achieves 82.2% and 88.7% on TU-Berlin-250 and TU-Berlin-160 benchmarks respectively.
Characterization of $1~\mathrm{k}\Omega$ Metal-Foil Standard Resistors and Continuing Drift-Rate Evaluation of 1$\Omega$ and $10~\Omega$ Standard Resistors

A set of standard resistors is a key component of resistance calibration. We have been developing a series of compact superstable standard resistors that meet calibration laboratory demands. In this paper, we describe manufacturing and evaluation of 1 kΩ standard resistors. All the resistors elements are made with the “stress free bulk metal foil” technology. We found that all standard resistors show extremely small average drift rates, e.g., smaller than 10 nΩ/(Ω year) and small temperature coefficients typically around 35 nΩ/(Ω °C) at 23 °C. Moreover, continuing the evaluation of drift behavior of 1 Ω and 10 Ω standard resistors is discussed.
Why is Eye Tracking an Essential Part of Neuroergonomics?

Eye tracking is a relevant methodology for studying human behavior and performance in neuroergonomics research. Recent progress has made eye tracking less intrusive and easier to use even in the most realistic experimental and natural settings. One benefit of eye tracking is that it enables derivation of both physiological (e.g., pupil diameter) and behavioral (gaze direction) metrics from its users. Pupil diameter is a relevant proxy of internal cognitive and attentional states, while gaze direction reveals visual strategies and patterns of information acquisition. While challenges still exist in terms of signal analysis, eye tracking is an irreplaceable technique with which to design and endow efficient online human–machine interaction systems.
Optimized Portfolio Contracts for Bidding the Cloud

Amazon EC2 provides two most popular pricing schemes–i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">costly</i> on-demand instance where the job is guaranteed to be completed, and ii) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cheap</i> spot instance where a job may be interrupted. We consider a user can select a combination of on-demand and spot instances to finish a task. Thus he needs to find the optimal bidding price for the spot-instance, and the portion of the job to be run on the on-demand instance. We formulate the problem as an optimization problem and seek to find the optimal solution. We consider three bidding strategies: one-time requests with expected guarantee, one-time requests with penalty for incomplete job and violating the deadline, and persistent requests. Even without a penalty on incomplete jobs, the optimization problem turns out to be non-convex. Nevertheless, we show that the portion of the job to be run on the on-demand instance is at most half. If the job has a higher execution time or smaller deadline, the bidding price is higher and vice versa. Additionally, the user never selects the on-demand instance if the execution time is smaller than the deadline. The numerical results illustrate the sensitivity of the effective portfolio to several of the parameters involved in the model. Our empirical analysis on the Amazon EC2 data shows that our strategies can be employed on the real instances, where the expected total cost of the proposed scheme decreases over 45 percent compared to the baseline strategy.
A Unified Security Perspective on Legally Fair Contract Signing Protocols

Inspired by Maurer's universal zero knowledge (UZK) abstract perspective and building on legally fair contract signing protocols without keystones, we propose and analyze the security of the first UZK class of co-signing protocols. We construct our main idea considering the stringent issue of scheme compatibility which characterizes communication systems. Typical examples are the cases of certificates in a public key infrastructure and the general issue of upgrading the version of a system. Thus, working in a general framework may reduce implementation errors and save application development and maintenance time.
Multilogit Prior-Based Gamma Mixture Model for Segmentation of SAR Images

Synthetic Aperture Radar (SAR) has the capability of working in all weather conditions during day and night that make it attractive to be used for target detection and recognition purposes. However, it has the problem of speckling that is structured as multiplicative noise which makes the SAR data a complex image. The algorithms need to be sufficiently robust to speckle noise for the achievement of reliable segmentation from such complex images. In this letter, the first contribution is the development of a robust multilogit spatial interactive model as a categorical distribution. The categorical property of this approach makes it ideally suited to be used as a pixel-based prior to any finite mixture model. Second, multilogit spatial interactive gamma mixture model is developed which is based on this prior. Experimental results with synthetic and real images indicate that the proposed mixture model is highly effective in segmenting SAR images.
SVM and HMM Classifier Combination Based Approach for Online Handwritten Indic Character Recognition

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.
Modeling Bitcoin Price and Bubbles

The goal of this chapter is to present recent developments about Bitcoin 1 price modeling and related applications.Precisely, we consider a bivariate model in continuous time to describe the behavior of Bitcoin price and of the investors' attention on the overall network.The attention index affects Bitcoin price through a suitable dependence on the drift and diffusion coefficients and a possible correlation between the sources of randomness represented by the driving Brownian motions.The model is fitted on historical data of Bitcoin prices, by considering the total trading volume and the Google Search Volume Index as proxies for the attention measure.Moreover, a closed formula is computed for European-style derivatives on Bitcoin.Finally, we discuss two possible extensions of the model.Precisely, we investigate the relation between the correlation parameter and possible bubble effects in the asset price; further, we consider a multivariate framework to represent the special feature of Bitcoin being traded on several exchanges and we discuss conditions to rule out arbitrage opportunities in this setting.
Confluence and convergence modulo equivalence in probabilistically terminating reduction systems

Convergence of an abstract reduction system is the property that the possible derivations from a given initial state all end in the same final state. Relaxing this by "modulo equivalence" means that these final states need not be identical, only equivalent wrt. a specified equivalence relation. We generalize this notion for probabilistic abstract reduction systems, naming it almost-sure convergence modulo equivalence, such that the final states are reached with probability 1. We relate it to the well-studied properties of almost-sure termination and confluence/convergence of probabilistic and non-probabilistic systems. In addition, we provide a transformational approach for proving – or disproving – almost-sure convergence modulo equivalence of given systems.
Comparative analysis of nucleus segmentation techniques for malignancy detection of pap smear cervical cells

Pap smear is a microscopic screening process of cervical smear used to detect pre-cancerous and cancerous cells in the cervix region. Cell nuclei segmentation is one of the most significant task in cervical cell image analysis which aids in identifying the different stages of cervical cancer like m ild, moderate, severe and carcinoma. Nucleus is the bio-marker which assist in identifying the malignancy of the cervical cell and the numerous literature clearly revealed the importance of nucleus shape, size and texture which enormously assist in identifying the normal and abnormal cells as the appearance of the nucleus of normal cell vary in size, shape and texture as it progresses from benign to malignancy. The main objective of this paper is to compare the different standard segmentation techniques like fuzzy C-means clustering, region based active contours and particle swarm optimization to extract nucleus feature. The statistical measures like precision, sensitivity (recall), specificity and accuracy are calculated and the performance of the extracted nuclei feature is compared with the standard benchmark Herlev database of Pap smear images.
Numerical solution of Burgers’ equation using Biorthogonal wavelet-based full approximation scheme

Recently, wavelet-based numerical methods have been newly developed in the areas of science and engineering. In this paper, we proposed a full-approximation scheme for the numerical solution of Burgers’ equation using biorthogonal wavelet filter coefficients as prolongation and restriction operators. The presented scheme gives higher accuracy in terms of higher convergence in less CPU time, which has been illustrated through the test problem.
On a conjecture of Fernando, Hou and Lappano concerning permutation polynomials over finite fields

Let Fq be the finite field with q elements and let p=charFq. It was conjectured that for integers e≥2 and 1≤a≤pe−2, the polynomial Xq−2+Xq2−2+⋯+Xqa−2 is a permutation polynomial of Fqe if and only if (i) a=2 and q=2, or (ii) a=1 and gcd(q−2,qe−1)=1. In the present paper we confirm this conjecture.
Revisiting explicit adaptive two-probe schemes

In the bitprobe model the primary concern is the number of bits that must be probed in a data structure in order to answer queries, and how constraining the number probes affects the storage requirements. In this note we revisit an explicit membership scheme of Radhakrishnan, Raman, and Rao [ESA 2001], which stores an n=2 element subset of a universe [1,m], occupies O(m2/3) bits, and supports membership queries in t=2 bit probes. We show that, with a small modification to their storage scheme, their query scheme also works for n=3 elements.
Semantics Based Web Ranking Using a Robust Weight Scheme

In this paper, HTML tags and attributes are used to determine different structural position of text in a web page. Tags- attributes based models are used to assign a weight to a text that exist in different structural position of web page. Genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms are used to select the informative terms using a novel tags-attributes and term frequency weighting scheme. These informative terms with heuristic weight give emphasis to important terms, qualifying how well they semantically explain a webpage and distinguish them from each other. The proposed approach is developed by customizing Terrier and tested over the Clueweb09B, WT10g, .GOV2 and uncontrolled data collections. The performance of the proposed approach is found to be encouraging against five baseline ranking models. The percentage gain of approach achieved is 75-90%, 70-83% and 43-60% in P@5, P@10 and MAP, respectively.
On decomposable correlation matrices

A correlation matrix is a positive semidefinite matrix with ones on the diagonal. In this work, we introduce and study r-decomposable correlation matrices: those that can be written as the Schur product of correlation matrices of rank at most r. We find that for all r≥ 2, every (r+1)×(r+1) correlation matrix is r-decomposable, and we construct (2r+1)×(2r+1) correlation matrices that are not r-decomposable. One question this leaves open is whether every 4×4 correlation matrix is 2-decomposable, which we make partial progress towards resolving. Motivations in quantum information are discussed.
Tile Pair-Based Adaptive Multi-Rate Stereo Shading

This work proposes a new stereo shading architecture that enables adaptive shading rates and automatic shading reuse among triangles and between two views. The proposed pipeline presents several novel features. First, the present sort-middle/bin shading is extended to tile pair-based shading to rasterize and shade pixels at two views simultaneously. A new rasterization algorithm utilizing epipolar geometry is then proposed to schedule tile pairs and perform rasterization at stereo views efficiently. Second, this work presents an adaptive multi-rate shading framework to compute shading on pixels at different rates. A novel tile-based screen space cache and a new cache reuse shader are proposed to perform such multi-rate shading across triangles and views. The results show that the newly proposed method outperforms the standard sort-middle shading and the state-of-the-art multi-rate shading by achieving considerably lower shading cost and memory bandwidth.
Self-Supervised Mixture-of-Experts by Uncertainty Estimation

Learning related tasks in various domains and transferring exploited knowledge to new situations is a significant challenge in Reinforcement Learning (RL). However, most RL algorithms are data inefficient and fail to generalize in complex environments, limiting their adaptability and applicability in multi-task scenarios. In this paper, we propose SelfSupervised Mixture-of-Experts (SUM), an effective algorithm driven by predictive uncertainty estimation for multitask RL. SUM utilizes a multi-head agent with shared parameters as experts to learn a series of related tasks simultaneously by Deep Deterministic Policy Gradient (DDPG). Each expert is extended by predictive uncertainty estimation on known and unknown states to enhance the Q-value evaluation capacity against overfitting and the overall generalization ability. These enable the agent to capture and diffuse the common knowledge across different tasks improving sample efficiency in each task and the effectiveness of expert scheduling across multiple tasks. Instead of task-specific design as common MoEs, a self-supervised gating network is adopted to determine a potential expert to handle each interaction from unseen environments and calibrated completely by the uncertainty feedback from the experts without explicit supervision. To alleviate the imbalanced expert utilization as the crux of MoE, optimization is accomplished via decayedmasked experience replay, which encourages both diversification and specialization of experts during different periods. We demonstrate that our approach learns faster and achieves better performance by efficient transfer and robust generalization, outperforming several related methods on extended OpenAI Gym’s MuJoCo multi-task environments.
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents.
Super-resolution using neighbourhood regression with local structure prior

Learning-based super-resolution (SR) imaging has been extensively studied. In this study, a novel reconstruction method is proposed for SR that employs the geometrical structure of an image as well as the statistical priors based on clustering and local structure priors. In this approach, the training samples are divided into numerous overlapping sub-patches, and the sub-patches are then grouped into different clusters, where anchored centres for different subspaces are defined by training joint dictionaries. Variable low-resolution to high-resolution mappings are then learned according to the local similarities. Finally, the desired high resolution patch can be reconstructed by using the multiple learned local relationships corresponding to its sub-patches. Experimental results demonstrated the superior performance of the proposed method.
Deep Upright Adjustment of 360 Panoramas Using Multiple Roll Estimations

Misalignment of the orientations between a 360 camera and the scene results in a wavy and distorted spherical panorama image, which may look unstable and have poor perceptual quality. To automatically correct such mis-oriented 360 panoramas, this paper proposes a novel upright adjustment framework based on a convolutional neural network. Instead of directly predicting the 3D rotation of the camera on a given panorama image, our method estimates the rotation by analyzing the projected 2D rotations of multiple images sampled from the panorama. To accurately estimate the rotations of 2D sampled images, we train a 2D roll estimation network using a large-scale labeled image dataset generated by cropping 360 spherical panoramas with various view orientations. Experimental results demonstrate that the proposed method accurately and robustly handles upright adjustment of rotated panoramas while outperforming the previous methods on test datasets that consist of a variety of scenes.
Voice Liveness Detection for Medical Devices

Many healthcare providers integrate biometric recognition/verification schemes into patient identification or other information security systems. While overcoming the disadvantages of using passwords, PINs, and tokens which may be forgotten, or stolen, biometric systems are susceptible to spoofing attacks, or presentation attacks. Liveness detection is an effective mechanism used to defeat a presentation attack. This chapter focuses on voice liveness detection in automatic speaker verification (ASV) systems. The authors explain the spoofing attacks to ASV systems comprising impersonation, voice conversion, speech synthesis, and replay and then present four types of liveness detection (anti-spoofing) methods used to mitigate ASV spoofing attacks: challenge-response-based methods, acoustic feature-based methods, hardware-based methods, and multi-modal biometric-based methods. This chapter analyzes the advantages and disadvantages of each kind of liveness detection method and proposes the possible application of voiceprint-based liveness detection schemes in the insulin pump system.
E-Learning and Augmented Reality (AR) for Chronic Wound Assessment

This investigation aimed to verify if augmented reality (AR) increases students' motivation and enhances the development of clinical decision-making skills in wound diagnosis and treatment. A quasi-experimental study was carried out with a sample of 54 nursing students. A comparative analysis of the progress of the control and experimental group was made using the data extracted from the simulator and a questionnaire. First, both groups used e-FER, a virtual simulator that simulates the diagnosis and treatment of virtual clinical cases. In the second moment, the control group continued using only e-FER, while the experimental group used e-FER combined with an AR application to observe the chronic wounds. The results showed that AR enhanced student performance, particularly in wound diagnostic parameters, with highly statistically significant differences (p&amp;lt;0,001), and registering high levels of motivation and simulator usability, even with the introduction of an additional device in the activity.
Development of Human Speech Signal-Based Intelligent Human-Computer Interface for Driving a Wheelchair in Enhancing the Quality-of-Life of the Persons

For the past few decades, an increase in experimental research has been carried out in enhancing the quality-of-life of the persons with different levels of disabilities. To enhance the lifestyle of differently disabled in terms of their mobility or movement or transportation, a proper aid with appropriate human-computer interface system is needed. So, in this chapter, a hybrid classification model is proposed, which combines and uses hM-GM and ANN models, for classifying human speech signal, especially the word for driving a wheelchair for helping the people, who seek transportation. For classifying the correct word from the phase of sentence (i.e., the human speech signal) to corresponding trigger command for an electrically powered wheelchair prototype, under the certain experimental condition, the hM-GM model yields good recognition of words, but they suffer major limitations as it relies on strong statistical properties and probability. Hence, by combining hM-GM and ANN model-based classifier for enhancing the accuracy of classifying the word to corresponding trigger command.