Reviewerly/exHarmony huggingface dataset:
Unconstrained Handwritten Arabic Text-lines Segmentation based on AR2U-Net

Text-lines are hard to segment in the context of Arabic manuscripts, because of the narrowly spaced text-lines with touching or overlapping components, the varying spaces between words, the ascendant or descendant letters, special marks, and dots, calligraphy, etc. In this work, we proposed a system to automatically extract text-lines from images of unconstrained handwritten Arabic texts. Each text-line is detected by its baseline. The proposed system is based on text-line masks which are predicted by a deep neural network called AR2U-Net: a Recurrent Residual convolutional neural network based on the U-Net model with an Attention mechanism. We adjusted the AR2U-Net model to allow a pixel-wise classification and therefore to separate text-lines pixels from the background one. We tested it on BADAM: a Public Dataset for Baseline Detection in Arabic script Manuscripts that involves complex layouts as well as curved and arbitrarily oriented text-lines and overlaps between adjacent text-lines, words, or sub-words. Our model achieves the best performances with a Precision of 0.932% which competes with current state-of-the-art approaches.
Unsupervised video object segmentation with distractor-aware online adaptation

Abstract Unsupervised video object segmentation is a crucial application in video analysis when there is no prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a video clip. In this paper, a novel unsupervised video object segmentation approach via distractor-aware online adaptation (DOA) is proposed. DOA models spatiotemporal consistency in video sequences by capturing background dependencies from adjacent frames. Instance proposals are generated by the instance segmentation network for each frame and they are grouped by motion information as positives or hard negatives. To adopt high-quality hard negatives, the block matching algorithm is then applied to preceding frames to track the associated hard negatives. General negatives are also introduced when there are no hard negatives in the sequence. The experimental results demonstrate these two kinds of negatives are complementary. Finally, we conduct DOA using positive, negative, and hard negative masks to update the foreground and background segmentation. The proposed approach achieves state-of-the-art results on two benchmark datasets, the DAVIS 2016 and the Freiburg-Berkeley motion segmentation (FBMS)-59.
Hypergraph-regularized sparse representation for single color image super resolution

Sparsity-based single image super resolution method generates the High-Resolution (HR) output via a corresponding dictionary from the Low-Resolution (LR) input. However, most of these existing methods ignore the complementary information from color channels, which causes the loss of a valid prior and the limitation of HR image quality improvement. In this paper, hypergraph regularization is first incorporated with Joint Color Dictionary Training (JCDT) model and HR image reconstruction (HRIR) model. A novel Hypergraph-regularized Sparse coding-based Super Resolution (HG-ScSR) is proposed. This regularization can not only focus on the illuminance information, but also exploit the self-channel and cross-channel information of three color RGB channels from high-resolution image patches. Especially, the complex relationship is explored among every color image patch pixel and the consistency of the similar pixels is enforced. Both simulated and real data experiments verify the higher performance of the proposed HG-ScSR.
Empirical Analysis and Modeling of Compute Times of CNN Operations on AWS Cloud

Given the widespread use of Convolutional Neural Networks (CNNs) in image classification applications, cloud providers now routinely offer several GPU-equipped instances with varying price points and hardware specifications. From a practitioner's perspective, given an arbitrary CNN, it is not obvious which GPU instance should be employed to minimize the model training time and/or rental cost. This paper presents Ceer, a model-driven approach to determine the optimal GPU instance(s) for any given CNN. Based on an operation-level empirical analysis of various CNNs, we develop regression models for heavy GPU operations (where input size is a key feature) and employ the sample median estimator for light GPU and CPU operations. To estimate the communication overhead between CPU and GPU(s), especially in the case of multi-GPU training, we develop a model that relates this communication overhead to the number of model parameters in the CNN. Evaluation results on AWS Cloud show that Ceer can accurately predict training time and cost (less than 5% average prediction error) across CNNs, enabling 36% -44% cost savings over simpler strategies that employ the cheapest or the latest generation GPU instances.
Resolution as intersection subtyping via Modus Ponens

Resolution and subtyping are two common mechanisms in programming languages. Resolution is used by features such as type classes or Scala-style implicits to synthesize values automatically from contextual type information. Subtyping is commonly used to automatically convert the type of a value into another compatible type. So far the two mechanisms have been considered independently of each other. This paper shows that, with a small extension, subtyping with intersection types can subsume resolution. This has three main consequences. Firstly, resolution does not need to be implemented as a separate mechanism. Secondly, the interaction between resolution and subtyping becomes apparent. Finally, the integration of resolution into subtyping enables first-class (implicit) environments. The extension that recovers the power of resolution via subtyping is the modus ponens rule of propositional logic. While it is easily added to declarative subtyping, significant care needs to be taken to retain desirable properties, such as transitivity and decidability of algorithmic subtyping, and coherence. To materialize these ideas we develop λ i MP , a calculus that extends a iprevious calculus with disjoint intersection types, and develop its metatheory in the Coq theorem prover.
A modulus-based multigrid method for image retinex

Retinex theory shows that the color of a scene perceived by human visual system is only dependent on the intrinsic reflectance and unrelated to the illumination. For images recorded by digital cameras, the intensities are the product of the scene reflectance and illumination. Image intensities are different when taken under different illuminations. The aim of image retinex problem is to decompose the reflectance from the image intensities. In this paper, we propose a variational model for image retinex problem. The model can be reduced to a linear complementarity problem and a modulus-based multigrid method is applied to solve it. A pointwise coupled modulus-based Gauss-Seidel iteration scheme is used as a smoother of the multigrid method. The smoothing factor of the smoother is analyzed by using the local Fourier analysis. Both theoretical analyses and numerical results show the efficiency of the proposed method for image retinex problem.
Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

Smart city environments, when applied to healthcare, improve the quality of people's lives, enabling, for instance, disease prediction and treatment monitoring.In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians' daily work.Recommender systems are an alternative to automatically integrate the data generated in such environments with predictive models and recommend actions, content, or services.The data produced by smart devices are accurate and reliable for predictive and decision-making contexts.This study main purpose is to assist patients and doctors in the early detection of disease or prediction of postoperative worsening through constant monitoring.To achieve this objective, this study proposes an architecture for recommender systems applied to healthcare, which can prioritize emergency cases.The architecture brings an ensemble approach for prediction, which adopts multiple Machine Learning algorithms.The methodology used to carry out the study followed three steps.First, a systematic literature mapping, second, the construction and development of the architecture, and third, the evaluation through two case studies.The results demonstrated the feasibility of the proposal.The predictions are promising and adherent to the application context for accurate datasets with a low amount of noises or missing values.
Coordinated Behavior for Sequential Cooperative Task Using Two-Stage Reward Assignment with Decay

Recently, multi-agent deep reinforcement learning (MADRL) has been studied to learn actions to achieve complicated tasks and generate their coordination structure. The reward assignment in MADRL is a crucial factor to guide and produce both their behaviors for their own tasks and coordinated behaviors by agents' individual learning. However, it has not been sufficiently clarified the reward assignment in MADRL's effect on learned coordinated behavior. To address this issue, using the sequential tasks, coordinated delivery and execution problem with expiration time, we analyze the effect of various ratios of the reward given for the task that agent is responsible for to the reward given for the whole task. Then, we propose a two-stage reward assignment with decay to learn the actions for tasks that the agent is responsible for and coordinated actions for facilitating other agents' tasks. We experimentally showed that the proposed method enabled agents to learn both actions in a balanced manner, so they could realize effective coordination, by reducing the number of tasks that were ignored by other agents. We also analyzed the mechanism behind the emergence of different coordinated behaviors.
Hybridization of linguistic and statistical methods of text analysis for describing the subject area in the form of a fuzzy ontology

Abstract The algorithm for selection of n-words and formation of a description of the subject area in the form of a fuzzy ontology based on the hybridization of linguistic and statistical methods of text analysis has been developed. The input data of the algorithm are text sequences in machine language, the result of the algorithm operation is the description of the subject area in the form of a fuzzy ontology. The definition of classes of fuzzy ontologies in accordance with the source code is carried out using several machine learning algorithms that combine methods of data preparation and prediction. Methods “Bootstrap”, “Bagging” and “Random forest” were used to classify text sequences. A feature of the developed algorithm is the need to represent ontology objects mainly in the form of one-words with maximization of the number of relations between objects.
Fractographic characterization of isotactic polypropylene fibers

Abstract In this article we present an opto‐thermo‐mechanical characterization of isotactic polypropylene monofilament during failure by fracture. The digital photoelasticity, the two‐beam Pluta polarising interference microscope and the computed tomography are used to provide quantitative data and visual models of the fracture regions of isotactic polypropylene fibers suffering failure by fracture at different thermal and mechanical conditions. Birefringence values and the intrinsic stress‐anisotropy are measured at the fracture surface. 3D models of the fracture regions are reconstructed. Photoelastic patterns and two‐beam interferograms are included for illustration.
Round Efficient Secure Multiparty Quantum Computation with Identifiable Abort

A recent result by Dulek et al. (EUROCRYPT 2020) showed a secure protocol for computing any quantum circuit even without the presence of an honest majority. Their protocol, however, is susceptible to a "denial of service" attack and allows even a single corrupted party to force an abort. We propose the first quantum protocol that admits security-with-identifiable-abort, which allows the honest parties to agree on the identity of a corrupted party in case of an abort. Additionally, our protocol is the first to have the property that the number of rounds where quantum communication is required is independent of the circuit complexity. Furthermore, if there exists a post-quantum secure classical protocol whose round complexity is independent of the circuit complexity, then our protocol has this property as well. Our protocol is secure under the assumption that classical quantum-resistant fully homomorphic encryption schemes with decryption circuit of logarithmic depth exist. Interestingly, our construction also admits a reduction from quantum fair secure computation to classical fair secure computation.
CHOOSE YOUR WORDS CAREFULLY: AN EXERCISE TO INTRODUCE ARTIFICIAL INTELLIGENCE TO THE MARKETING CLASSROOM USING TONE ANALYSIS

As technology changes rapidly, marketing educators are challenged with maintaining a curriculum that is ever-evolving in order for their graduates to be real-world ready. This paper suggests a highly adaptable exercise that can be used to teach tone analysis while simultaneously familiarizing students with an application of artificial intelligence (AI). By incorporating IBM’s Watson Tone Analyzer into the activity, students are able to discover the power of AI and interact with it both directly and in real-time. Empirical evidence shows that students find this exercise to be engaging, effective, and highly influential. From a faculty perspective, this is an ideal assignment to introduce one application of AI in a simple way that creates a springboard for additional discussion and more in-depth exploration of modern technologies. The prescribed assignment has been tested in four different marketing courses, and it can easily be adapted to fit others. Additionally, this exercise can be utilized in a traditional, hybrid, or online learning environment.
Detecting systematic anomalies affecting systems when inputs are stationary time series

Abstract We develop an anomaly detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly free inputs (i.e., those before contamination) to originate from a wide class of random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally nonstationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly free inputs following an ARMA time series model under various contamination scenarios.
A Robust Methodology for Assessing the Effectiveness of Site Adaptation Techniques for Calibration of Solar Radiation Data

Abstract The abundant spatial and temporal availability of solar energy has been fueling many researches and have been the reason for the proliferation of solar energy applications in the past decades. Many of these applications involve heavy investments and thus require highly accurate and reliable long-term average solar data for efficient deployment of solar energy technologies. Since ground stations are costly, site-specific, scarce and cannot provide long-term solar data, satellite-derived data is the next best alternative. However, satellite models are often unable to capture the complex local climatological variations of a given site. As such, short-term high precision solar ground measurements are used to train the satellite model so as to improve the accuracy of long-term solar estimates. There exist several site adaptation techniques to perform this task. However, to the knowledge of the researchers, no comparative study has been conducted to establish which site adaptation technique is the most effective. In this study, a robust methodology has been proposed to compare the effectiveness of four site adaptation techniques for monthly and yearly data sets using novel key performance indicators. Ground measurements from 12 stations in the tropical islands of Mauritius, Rodrigues, and Agalega were used to adapt satellite data obtained from HelioClim-3 database using different techniques. Three new nonlinear site adaptation techniques have been proposed: adjustment technique (Technique 2), compensation technique (Technique 3), and relationship technique (Technique 4). The first part of the study showed that 67–100% of the data sets were best approximated with sixth-order polynomials for the three nonlinear techniques. The second part revealed that Technique 1 (linear method) and Technique 2 were most appropriate for maximum and average data sets, respectively. The results were such that Technique 2 and Technique 1 provided best approximations for77.9–83.3% and 40.7–58.3% of average and maximum data sets, respectively. In the third part of the study, only Technique 2 provided remarkable improvements for all statistical metrics with respect to the original monthly data sets (113–118 data sets). The analysis reported 57.6–89.9%, 49.8–68.0%, 67.4–87.3%, 53.8–63.1%, 45.0–64.0%, 7.7–9.6% and 2.7–4.7% mean improvements for mean bias error (MBE), mean absolute bias error (MABE), mean percentage error (MPE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), Nash–Sutcliffe (NSE), and coefficient of determination (COD), respectively, for Technique 2. Similar results were observed for yearly average data sets while the appreciation was shared among all four techniques for yearly maximum data sets, with Technique 1 having a slight advantage.
General multilevel adaptations for stochastic approximation algorithms II: CLTs

In this article we establish central limit theorems for multilevel Polyak–Ruppert averaged stochastic approximation schemes. We work under very mild technical assumptions and consider the slow regime in which typical errors decay like N−δ with δ∈(0,12) and the critical regime in which errors decay of order N−1∕2logN in the runtime N of the algorithm.
Human Action Recognition based on Hybrid Deep Learning Model and Shearlet Transform

The hybrid deep learning model has become common in all recent studies dealing with machine vision and human action recognition. Most of the accuracy in revealing knowledge of machine vision is in extracting important features, including segmentation of the image. This paper proposes a new model for recognizing human actions from video sequences by integrating repetitive, gated recurrent neural networks across multiple scales with shearlet-based image segmentation extraction. Segmentations are the most critical information to distinguish human action. The feature extraction can impact the complexity of the calculation and the performance of the algorithm. The idea is to increase training robustness and improve segmentation through the use of the shearlet transform. Hence, the video classification based on a recurrent neural network and shearlet transform will work optimally. The proposed approach is evaluated on human activity videos using KTH, UCF-101, and UCF Sports Action datasets. The experimental results showed state-of-the-art performance in comparison to current methods. The average resulting classification accuracy is 95.1% for the KTH datasets. That was the optimal case in our proposed model reached.
Semi-Adaptively Secure Offline Witness Encryption from Puncturable Witness PRF

In this work, we introduce the notion of puncturable witness pseudorandom function (pWPRF) which is a stronger variant of WPRF proposed by Zhandry, TCC 2016. The punctured technique is similar to what we have seen for puncturable PRFs and is capable of extending the applications of WPRF. Specifically, we construct a semi-adaptively secure offline witness encryption (OWE) scheme using a pWPRF, an indistinguishability obfuscation ( $$i\mathcal {O}$$ ) and a symmetric-key encryption (SKE), which enables us to encrypt messages along with NP statements. We show that replacing $$i\mathcal {O}$$ with extractability obfuscation, the OWE turns out to be an extractable offline witness encryption scheme. To gain finer control over data, we further demonstrate how to convert our OWEs into offline functional witness encryption (OFWE) and extractable OFWE. All of our OWEs and OFWEs produce an optimal size ciphertext, in particular, encryption of a message is as small as the size of the message plus the security parameter multiplied with a constant, which is optimal for any public-key encryption scheme. On the other hand, in any previous OWE, the size of a ciphertext increases polynomially with the size of messages. Finally, we show that the WPRF of Pal et al. (ACISP 2019) can be extended to a pWPRF and an extractable pWPRF.
Multi-Modal Deep Learning-Based Violin Bowing Action Recognition

In this paper, a deep learning-based violin action recognition is proposed. By fusing the sensing signals from depth camera modality and inertial sensor modalities, violin bowing actions can be recognized by the proposed deep learning scheme. The actions performed by a violinist are captured by a depth camera, and recorded by wearable sensors on the forearm of a violinist. In the proposed system, 3D convolution neural network (3D-CNN) and long short-term memory (LSTM) deep learning algorithms are adopted to generate the action models from depth camera modality and inertial sensor modalities. The features and models obtained from multi-modalities are used to classify different violin bowing actions. A fusion process from different modalities can achieve satisfactory recognition accuracy. In this paper, we generate a violin bowing actions dataset for the preliminary study and the system performance evaluation.
Balloon Interface for Midair Haptic Interaction

Midair physical prop is a promising tool to facilitate intuitive human-computer interaction in a three-dimensional (3D) space. In such systems, it is challenging to guarantee safety and comfort during interaction with real objects in high-speed 3D motion. In this paper, we propose a balloon interface, a midair physical prop that affords direct single-handed manipulation in a safe manner. The system uses a spherical helium-filled balloon controlled by surrounding ultrasound phased array transducers as a physical prop. It is safe to collide even when it is moving fast because it has an elastic body and does not have any mechanical parts. Fast switching of driving transducer units and closed-loop control based on a high-speed measurement system result in the 3D control of an object of a size that affords a spherical grasp, e.g., a spherical balloon of 10-cm diameter.
Implementing Lightweight Image/Video Encryption Cores on Xilinx Zynq

Plethora of the optimized video/image cryptography algorithms with diverse structures such as chaos-based, transformed-based is proposed for disparate systems, especially embedded ones. Among them, the fully-layered light-weights have not been considered sufficiently. Our goal is to evaluate and compare different type of state-of-the-art fully-layered light-weights algorithms performances in order to figure out what do they meet all the mandatories of resource-constrained devices and security criteria for multimedia data type or not? In this regard, we select three light-weight algorithms -GIFT128, ASCON128a, and ZUC256- with disparate structures and salient suitable features. We initially assess the security quality of their outputs by C++ programs, then each of cores which has concomitant AXI interface is implemented by Verilog language in Xilinx Vivado 2016.2 on Zynq7020 FPGA. The achieved results from implementations and simulation of security analysis show that these cryptography cores can meet all the requirements of an embedded encryption system, in which the obvious ones are high-security, real-time processing, and low-power consumption.