Reviewerly/exHarmony huggingface dataset:
Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation

In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.
Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection

Although the introduction of fuzzy theory into a portfolio selection model can help improve the model's practicality, it would increase the difficulty of solving the model. To tackle the issue, this paper proposes a novel mechanism based artificial bee colony algorithm (ABC) consisting of two new proposed learning strategies—direction learning and elite learning. The direction learning strategy has a great potential to guide the search toward the promising areas. The elite learning strategy can gradually pick up the convergence rate without loss of the population diversity. The cooperation of the two approaches forms a mechanism, complementing each other to improve the performance of the algorithms. The proposed mechanism, named LL-mechanism, is introduced into three ABC variants-ABC, gbest-guided ABC (GABC), and CABC, generating LL-ABC, LL-GABC, and LL-CABC, respectively. The experimental results demonstrate the superior performance of the LL-mechanism and LL-CABC outperforms other methods in terms of solution quality, convergence rate, robustness, and numerical stability. Finally, the proposed LL-CABC approach is employed to solve the portfolio selection with fuzzy security return. The experiments on two portfolio selection models illustrate that LL-CABC is effective and promising for a fuzzy portfolio selection.
Continuous Authentication of Smartphones Based on Application Usage

An empirical investigation of active/continuous authentication for smartphones is presented by exploiting users' unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Specifically, variations of hidden Markov models (HMMs) are evaluated for continuous user verification, and challenges due to the sparsity of session-wise data, an explosion of states, and handling unforeseen events in the test data are tackled. Unlike traditional approaches, the proposed formulation utilizes the complete app-usage information to achieve low latency. Through experimentation, empirical assessment of the impact of unforeseen events, i.e., unknown applications and unforeseen observations, on user verification is done via a modified edit-distance algorithm for sequence matching. It is found that for enhanced verification performance, unforeseen events should be considered. For validation, extensive experiments on two distinct datasets, namely, UMDAA-02 and Securacy, are performed. Using the marginally smoothed HMM a low equal error rate (EER) of 16.16% is reached for the Securacy dataset and the same method is found to be able to detect an intrusion within ~2.5 min of application use.
A fractional order derivative based active contour model for inhomogeneous image segmentation

Segmenting intensity inhomogeneous images is a challenging task for both local and global methods. Some hybrid methods have great advantages over the traditional methods in inhomogeneous image segmentation. In this paper, a new hybrid method is presented, which incorporates image gradient, local environment and global information into a framework, called adaptive-weighting active contour model. The energy or level set functions in the framework mainly include two parts: a global term and local term. The global term aims to enhance the image contrast, and it can also accelerate the convergence rate when minimizing the energy function. The local term integrates fractional order differentiation, fractional order gradient magnitude, and difference image information into the well-known local Chan–Vese model, which has been shown to be effective and efficient in modeling the local information. The local term can also enhance low frequency information and improve the inhomogeneous image segmentation. An adaptive weighting strategy is proposed to balance the actions of the global and local terms automatically. When minimizing the level set functions, regularization can be imposed by applying Gaussian filtering to ensure smoothness in the evolution process. In addition, a corresponding stopping criterion is proposed to ensure the evolving curve automatically stops on true boundaries of objects. Dice similarity coefficient is employed as the comparative quantitative measures for the segmented results. Experiments on synthetic images as well as real images are performed to demonstrate the segmentation accuracy and computational efficiency of the presented hybrid method.
A Lightweight And privacy-preserving public cloud auditing scheme without bilinear pairings in smart cities

Smart Cities have become a global strategy. However, massive data generated by various smart devices need to be uploaded and stored to the cloud servers. It is critical to ensure the integrity and privacy of the stored data. Quite a few public cloud auditing schemes have been proposed recently. However, most of them use bilinear pairing operations in the audit phase, requiring a significant time cost. Meanwhile, users (may be resource-constrained mobile devices or sensor nodes) still need to perform significant computations, like computing meta data for each data block, which bring a huge burden of calculation for these users. Moreover, those schemes cannot effectively protect users’ data privacy. Thus, we propose a lightweight and privacy-preserving public cloud auditing scheme for smart cities that does not require bilinear pairings. First, the proposed scheme is pairing-free, and allowing a third party auditor to generate authentication meta set on behalf of users. Furthermore, it also protects data privacy against the third party auditor and the cloud service providers. In addition, this new scheme can be easily and naturally extended to batch auditing in a multi-user scenario. Detailed security and performance analyses show that the proposed scheme is more secure and efficient compared to the existing public cloud auditing schemes.
Modeling cyber-physical attacks based on stochastic game and Markov processes

Security of cyber-physical systems (CPS) has been a prime concern due to the ever-increasing integration of communication networks with physical devices. In this paper, we propose a zero − sum stochastic game model to capture the system dynamics resulting from the attacker − defender interactions in an attacked CPS. In addition to predicting the attacker behavior in terms of optimal strategies, the best strategies for counteracting the malicious activities are also determined. The lifetime of the attacked CPS is characterized based on a continuous time Markov chain (CTMC) and consequently, mean time to failure of the system is derived. Further, we address dependability issues of the system by formulating a semi-Markov model and obtaining different dependability measures such as reliability, availability and confidentiality. The studies identify critical states of the system with respect to its dependability. Theoretical results are also validated via simulation. The results of the studies are expected to be helpful in developing efficient and robust defense mechanisms.
On $Z_p Z_{p^k}$ -Additive Codes and Their Duality

In this paper, two different Gray-like maps from $Z_p^α\times Z_{p^k}^β$, where $p$ is prime, to $Z_p^n$, $n={α+βp^{k-1}}$, denoted by $ϕ$ and $Φ$, respectively, are presented. We have determined the connection between the weight enumerators among the image codes under these two mappings. We show that if $C$ is a $Z_p Z_{p^k}$-additive code, and $C^\bot$ is its dual, then the weight enumerators of the image $p$-ary codes $ϕ(C)$ and $Φ(C^\bot)$ are formally dual. This is a partial generalization of [On $Z_{2^k}$-dual binary codes, arXiv:math/0509325], and the result is generalized to odd characteristic $p$ and mixed alphabet. Additionally, a construction of $1$-perfect additive codes in the mixed $Z_p Z_{p^2} ... Z_{p^k}$ alphabet is given.
Efficient discrete latent semantic hashing for scalable cross-modal retrieval

Hashing has been widely exploited in the cross-modal retrieval applications in recent years for its low storage cost and high retrieval efficiency. However, most existing cross-modal hashing methods either fail to capture the discriminative semantics of multi-modal data or suffer from the relatively high training cost. To address these limitations, we propose an efficient Discrete Latent Semantic Hashing (DLSH) method. DLSH first learns the latent semantic representations of different modalities, and then projects them into a shared Hamming space to support the scalable cross-modal retrieval. Because DLSH directly correlates the explicit semantic labels with binary codes, it can enhance the discriminative capability of the learned hashing codes. Furthermore, to obtain binary codes, traditional methods often relax the discrete constraint, resulting in relatively high computation cost as well as quantization loss. In contrast, DLSH directly learns the binary codes with an efficient discrete hash optimization, and thus increases efficiency and reduces the quantization loss in hash optimization. Extensive experiments on several public datasets show that, DLSH outperforms several state-of-the-art cross-modal hashing methods.
All-optical implementation of collision-based evolutions of open quantum systems

We present a new optical scheme enabling the implementation of highly stable and configurable non-Markovian dynamics. Here one photon qubit can circulate in a multipass bulk geometry consisting of two concatenated Sagnac interferometers to simulate the so called collisional model, where the system interacts at discrete times with a vacuum environment. We show the optical features of our apparatus and three different implementations of it, replicating a pure Markovian scenario and two non-Markovian ones, where we quantify the information backflow by tracking the evolution of the initial entanglement between the system photon and an ancillary one.
Predicting the Quality of Images Compressed After Distortion in Two Steps

In a typical communication pipeline, images undergo a series of processing steps that can cause visual distortions before being viewed. Given a high quality reference image, a reference (R) image quality assessment (IQA) algorithm can be applied after compression or transmission. However, the assumption of a high quality reference image is often not fulfilled in practice, thus contributing to less accurate quality predictions when using stand-alone R IQA models. This is particularly common on social media, where hundreds of billions of usergenerated photos and videos containing diverse, mixed distortions are uploaded, compressed, and shared annually on sites like Facebook, YouTube, and Snapchat. The qualities of the pictures that are uploaded to these sites vary over a very wide range. While this is an extremely common situation, the problem of assessing the qualities of compressed images against their precompressed, but often severely distorted (reference) pictures has been little studied. Towards ameliorating this problem, we propose a novel two-step image quality prediction concept that combines NR with R quality measurements. Applying a first stage of NR IQA to determine the possibly degraded quality of the source image yields information that can be used to quality-modulate the R prediction to improve its accuracy. We devise a simple and efficient weighted product model of R and NR stages, which combines a pre-compression NR measurement with a post-compression R measurement. This first-of-a-kind two-step approach produces more reliable objective prediction scores. We also constructed a new, first-of-a-kind dedicated database specialized for the design and testing of two-step IQA models. Using this new resource, we show that twostep approaches yield outstanding performance when applied to compressed images whose original, pre-compression quality covers a wide range of realistic distortion types and severities. The two-step concept is versatile as it can use any desired R and NR components. We are making the source code of a particularly efficient model that we call 2stepQA publicly available at https://github.com/xiangxuyu/2stepQA. We are also providing the dedicated new two-step database free of charge at http://live.ece.utexas.edu/research/twostep/index.html.
Visual Permutation Learning

We present a principled approach to uncover the structure of visual data by solving a deep learning task coined visual permutation learning. The goal of this task is to find the permutation that recovers the structure of data from shuffled versions of it. In the case of natural images, this task boils down to recovering the original image from patches shuffled by an unknown permutation matrix. Permutation matrices are discrete, thereby posing difficulties for gradient-based optimization methods. To this end, we resort to a continuous approximation using doubly-stochastic matrices and formulate a novel bi-level optimization problem on such matrices that learns to recover the permutation. Unfortunately, such a scheme leads to expensive gradient computations. We circumvent this issue by further proposing a computationally cheap scheme for generating doubly stochastic matrices based on Sinkhorn iterations. To implement our approach we propose DeepPermNet, an end-to-end CNN model for this task. The utility of DeepPermNet is demonstrated on three challenging computer vision problems, namely, relative attributes learning, supervised learning-to-rank, and self-supervised representation learning. Our results show state-of-the-art performance on the Public Figures and OSR benchmarks for relative attributes learning, chronological and interestingness image ranking for supervised learning-to-rank, and competitive results in the classification and segmentation tasks of the PASCAL VOC dataset for self-supervised representation learning.
Improved learning algorithm for two-layer neural networks for identification of nonlinear systems

This study is concerned with the asymptotic identification of nonlinear systems based on Lyapunov theory and two-layer neural networks. An improved identification model enhanced with a feedback term and a novel adaptation law for the threshold offset, associated with the output weight matrix, is introduced to assure the convergence of the online prediction error, even in the presence of approximation error and bounded disturbances and when upper bounds for these perturbations are not known in advance. The effectiveness of the proposed method and its application to the identification of a hyperchaotic system and control of a welding system is investigated.
Computation Scheduling for Distributed Machine Learning with Straggling Workers

We study scheduling of computation tasks across n workers in a large scale distributed learning problem. Computation speeds of the workers are assumed to be heterogeneous and unknown to the master, and redundant computations are assigned to the workers in order to tolerate straggling workers. We consider sequential computation and instantaneous communication from each worker to the master, and each computation round, which can model a single iteration of the stochastic gradient descent (SGD) algorithm, is completed once the master receives k ≤ n distinct computations, referred to as the computation target. Our goal is to characterize the average completion time as a function of the computation load, which denotes the portion of the dataset available at each worker, and the computation target. We propose two computation scheduling schemes that specify the computation tasks assigned to each worker, as well as their order of execution. We also establish a lower bound on the minimum average completion time. Numerical results show a significant reduction in the average computation time over the existing coded and uncoded computing schemes.
Bounded Z-type neurodynamics with limited-time convergence and noise tolerance for calculating time-dependent Lyapunov equation

Z-type neurodynamics (ZND) is an effective tool for calculating time-dependent problems and has been extensively used. However, the convergence rate and the noise tolerance of ZND models have frequently been addressed separately. In this work, a unified design formula for the ZND is proposed by combining the nonlinear activation function and the integral term. On the basis of a formula, a bounded ZND (BZND) model is proposed and used to compute a real-time-dependent Lyapunov equation in noisy environments. Notably, the proposed BZND model, which adopts the Li activation function, not only converges in a limited time but also has inherently noise-tolerant characteristics. Theoretical analyses of the convergence and robustness of the BZND model are further presented. In addition, the upper bound of convergence time is also derived theoretically. Finally, illustrative examples are conducted. Results confirm the superior performance of the proposed BZND model for calculating the real-time-dependent Lyapunov equation with various types of noise to the existing ZND models.
Component-based face recognition under transfer learning for forensic applications

Abstract In numerous scenarios of forensic applications , only a partial face image is available for recognition purposes. Hence, the systems working on components of the face and partial face images have gained great importance. This paper proposes a novel approach for component-based face recognition and association under transfer learning and demonstrates that the knowledge gained from complete face images is transferred to classify components of the face. Three important components of a face, viz. ears, lips, and nose, are used for association and recognition. These components are unique, stable, and unaltered by the change in poses and expressions. Association is found between the face and nose, face and lips, as well as face and ears. Although, these face components and the face itself are from different domains, they share common information which is utilized to transfer the knowledge gained from one domain to another. Similarly, different types of half-faces are considered: left, right, upper, and lower half and left, right, upper, and lower diagonal for the association between complete and partial face images. Because of the association between a face and its different components, the proposed method can be applied to holistic face recognition, component-based face recognition and partial face recognition.
Traffic light recognition using high-definition map features

Accurate recognition of traffic lights in public roads is a critical step to deploy automated driving systems. Camera sensors are widely used for the object detection task. It might seem natural to employ them to traffic signal detection. However, images as captured by cameras contain a broad number of unrelated objects, causing a significant reduction in the detection accuracy. This paper presents an innovative, yet reliable method to recognize the state of traffic lights in images. With the help of accurate 3D maps and a self-localization technique in it, elements already being used in autonomous driving systems, we propose a method to improve the traffic light detection accuracy. Using the current location and looking for the traffic signals in the road, we extract the region related only to the traffic light (ROI, region of interest) in images captured by a vehicle-mounted camera, then we feed the ROIs to custom classifiers to recognize the state. Evaluation of our method was carried out in two datasets recorded during our urban public driving experiments, one taken during day light and the other obtained during sunset. The quantitative evaluations indicate that our method achieved over 97% average precision for each state and approximately 90% recall as far as 90 meters under preferable condition.
Cardinality-Consensus-Based PHD Filtering for Distributed Multitarget Tracking

We present a distributed probability hypothesis density (PHD) filter for multitarget tracking in decentralized sensor networks with severely constrained communication. The proposed “cardinality consensus” (CC) scheme uses communication only to estimate the number of targets (or, the cardinality of the target set) in a distributed way. The CC scheme allows for different implementations-e.g., using Gaussian mixtures or particles-of the local PHD filters. Although the CC scheme requires only a small amount of communication and of fusion computation, our simulation results demonstrate large performance gains compared with noncooperative local PHD filters.
Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations

A surface solar radiation forecast algorithm is developed using cloud physical properties from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of the Meteosat Second Generation (MSG) geostationary satellite. The novelty of the algorithm is the derivation of cloud motion vectors using cloud physical properties. The solar radiation forecast products include global horizontal irradiance (GHI) and direct normal irradiance (DNI). The forecast horizon is 0–4 h at a 15 min temporal resolution. The forecast is currently tested for the Netherlands at a spatial resolution of about 4 km × 6 km. Compared to measurements of the Baseline Surface Radiation Network (BSRN) site of Cabauw, the root mean square error (RMSE) is about 31–44% for GHI and 59–100% for DNI at a forecast horizon of 2 h. For a forecast horizon of 15 min, the RMSE is 22–24% for GHI and 43–61% for DNI. The correlation coefficients between the forecasts and BSRN measurements are similar for GHI and DNI, and decrease from about 0.8–0.9 at 15 min to 0.45–0.75 at 2 h. The SEVIRI forecast outperforms the HARMONIE numerical weather prediction model forecast in the first 2–3 h. The quality of the forecast depends on the sky conditions: for clear-sky indices larger than 0.7, the SEVIRI GHI forecast is better than both smart persistence and the HARMONIE forecast in the first 4 h.