Reviewerly/exHarmony huggingface dataset:
A new method to solve rotated template matching using metaheuristic algorithms and the structural similarity index

Template matching (TM) is a technique used in computer vision that helps us to identify small pieces of a predefined sub image known as template and a corresponding area inside a large image. There are several approaches in the literature that solve this problem. In spite of their acceptable results, the existing methods fail to detect templates when they are rotated in a large image. On the other hand, the structural similarity (SSIM) index is a resemblance model that allows us to evaluate the affinity between two images. Different from other similarity indexes, SSIM considers important structural information while also incorporating essential elements such as luminance and contrast terms. This paper presents a new approach for template matching when an image is rotated. Our method considers the complete process as an optimization problem where a metaheuristic algorithm is used to find the best resemblance between the template and its rotated or unrotated image. In order to obtain a robust detection, our approach considers the use of the SSIM index as an objective function. Although metaheuristic approaches produce interesting results, there is not an ideal algorithm that can competitively solve all problems. Under such conditions, our study analyzes the performance of five different metaheuristic techniques for solving the problem of TM. In the comparison, several complicated computer experiments have been conducted considering different performance indexes. The results show that the proposed technique solves the problem of TM competitively for rotated instances in terms of accuracy and robustness.
NAS For efficient mobile eyebrow biometrics

Occlusions, such as those due to wearing surgical masks, pose a significant challenge to the face recognition systems. Among possible remedies, ocular biometric has proven to be a popular choice. However, the upper ocular regions, especially the patterns presented by the eyebrows, have yet to gain the attention they deserve. In this work, we leverage Neural Architecture Search (NAS) to discover better-performing architectures for eyebrow recognition. To reduce the computational complexity, we apply a zero-shot NAS to assess the exploratory architectures’ performance prior to any training. We were able to discover three new architectures that achieved competitive accuracies in eyebrow recognition. In doing so, we explored depthwise separable convolution, hard-swish, and Arcface loss functions to further enhance the discovered models in terms of accuracy and number of parameters. Our best result provided 0.999 AUC, 0.6% EER, and 98.25% GMR at 10−3 FMR over FACES dataset, which is better than the results of state-of-the-art architecture, a 29-layer lightCNN which has 21× more parameters and 8× more FLOPS.
Design and cryptanalysis of a new iterative structure

Abstract This study proposes a new iterative structure called the L‐Feistel structure, which shares similar procedures between encryption and decryption and could unify the Feistel structure and the Lai‐Massey structure. This paper evaluates the security of the L‐Feistel structure from the perspective of provable security and classical analysis, respectively. Firstly, it is proved that the 4‐round Key‐Alternating L‐Feistel cipher with independent round keys and independent round functions is secure against 2 n /2 queries that is, birthday‐bound security. Then by presenting the dual structure of the L‐Feistel structure with SP‐type round functions, it is proven that a differential of the L‐Feistel structure is impossible when and only when it is a zero‐correlation linear hull of its dual structure. Finally, the paper constructs impossible differentials, zero‐correlation linear hulls and integral distinguishers of the L‐Feistel structure with SP‐type round functions which cover six and seven rounds, respectively, under different conditions.
Camouflage Object Segmentation Using an Optimized Deep-Learning Approach

Camouflage objects hide information physically based on the feature matching of the texture or boundary line within the background. Texture matching and similarities between the camouflage objects and surrounding maps make differentiation difficult with generic and salient objects, thus making camouflage object detection (COD) more challenging. The existing techniques perform well. However, the challenging nature of camouflage objects demands more accuracy in detection and segmentation. To overcome this challenge, an optimized modular framework for COD tasks, named Optimize Global Refinement (OGR), is presented. This framework comprises a parallelism approach in feature extraction for the enhancement of learned parameters and globally refined feature maps for the abstraction of all intuitive feature sets at each extraction block’s outcome. Additionally, an optimized local best feature node-based rule is proposed to reduce the complexity of the proposed model. In light of the baseline experiments, OGR was applied and evaluated on a benchmark. The publicly available datasets were outperformed by achieving state-of-the-art structural similarity of 94%, 93%, and 96% for the Kvasir-SEG, COD10K, and Camouflaged Object (CAMO) datasets, respectively. The OGR is generalized and can be integrated into real-time applications for future development.
State-space computation of quadratic-exponential functional rates for linear quantum stochastic systems

This paper is concerned with infinite-horizon growth rates of quadratic-exponential functionals (QEFs) for linear quantum stochastic systems driven by multichannel bosonic fields. Such risk-sensitive performance criteria impose an exponential penalty on the integral of a quadratic function of the system variables, and their minimization improves robustness properties of the system with respect to quantum statistical uncertainties and makes its behavior more conservative in terms of tail distributions. We use a frequency-domain representation of the QEF growth rate for the invariant Gaussian quantum state of the system with vacuum input fields in order to compute it in state space. The QEF rate is related to a similar functional for a classical stationary Gaussian random process generated by an infinite cascade of linear systems. A truncation of this shaping filter allows the QEF rate to be computed with any accuracy by solving a recurrent sequence of algebraic Lyapunov equations together with an algebraic Riccati equation. The state-space computation of the QEF rate and its comparison with the frequency-domain results are demonstrated by a numerical example for an open quantum harmonic oscillator.
A domain-specific language for structure manipulation in constraint system-based GUIs

A common frustration with programming Graphical User Interfaces (GUIs) is that features for manipulating structures, such as lists and trees, are limited, inconsistent, buggy, or even missing. Implementing complete and convenient sets of operations for inserting, removing, and reordering elements in such structures can be tedious and difficult: a structure that appears as one collection to the user can be implemented as several different data structures and a web of dependencies between them. Structural modifications require changes both to the GUI’s model and view, and possibly extraneous bookkeeping operations, such as adding and removing event handlers. This paper introduces a DSL that helps programmers to implement a complete set of operations to structures displayed in GUIs. The programmer specifies structures and relations between elements in the structure. Concretely, the latter are definitions of methods for establishing and unestablishing relations. Operations that manipulate structures are specified as rules that control which relations should hold before and after a rule is applied. From these specifications, our tools generate an easy-to-use API for structure manipulation. We target constraint system-based Web GUIs: the DSL generates JavaScript and relies on dataflow constraint systems for expressing dependencies between elements in GUI structures. Our DSL gives tangible representations with well-defined operations for ad-hoc and incidental GUI structures.
Feature selection with scalable variational gaussian process via sensitivity analysis based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si79.svg"><mml:mrow><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> divergence

Feature selection is one of the most important issues in supervised learning and there are a lot of different feature selection approaches in the literature. Among them one recent approach is to use Gaussian process (GP) because it can capture well the hidden relevance between the features of the input and the output. However, the existing feature selection approaches with GP suffer from the scalability problem due to high computational cost of inference with GP. Moreover, they use the Kullback–Leibler (KL) divergence in the sensitivity analysis for feature selection, but we show in this paper that the KL divergence underestimates the relevance of important features in some cases of classification. To remedy such drawbacks of the existing GP based approaches, we propose a new feature selection method with scalable variational Gaussian process (SVGP) and L2 divergence. With the help of SVGP the proposed method exploits given large data sets well for feature selection through so-called inducing points while avoiding the scalability problem. Moreover, we provide theoretical analysis to motivate the choice of L2 divergence for feature selection in both classification and regression. To validate the performance of the proposed method, we compare it with other existing methods through experiments with synthetic and real data sets.
An Empirical Analysis of Machine Learning Algorithms for Solar Power Forecasting in a High Dimensional Uncertain Environment

In the fabric of energy generation, solar power is the most promising clean energy solution as an alternative to non-renewable energy sources. However, solar power's dependency on environmental factors adds uncertainty to energy production. In such a scenario, solar power forecasting provides an edge to mitigate this uncertainty and improves overall system stability. Recently, machine learning (ML) models have been extensively deployed for designing and forecasting solar power. However, data pre-processing, forecast horizon, and performance evaluation of ML algorithms have to be carefully evaluated to find an accurate model. This paper provides an empirical comparison of different generation ML models for solar power forecasting, which can help understand future research on which method to adopt, depending on the ML model's strengths and weaknesses. Therefore, an effective forecasting method is designated in aspects such as performance errors, convergence time, and computational complexity. So, this work rates different ML models on error performance metrics and convergence time. Moreover, cross-fold validations and hyperparameter are also examined for the top five performing models for a comprehensive evaluation and to give more intuitive and calibrated insight into various stakeholders working in the solar power plant modeling field.
Intelligent classification trash bin based on deep learning

In order to solve the problems of low accuracy and low labor efficiency in garbage classification, an intelligent classification trash bin equipped with robot arm and 110-degree wide-angle camera is designed. A fast identification and positioning algorithm based on deep learning is proposed. The PASCAL VOC 2007 dataset is applied, which consists of 600 images containing 4000 different types of garbage in different situations. 20% of the images is randomly selected as the validation set, and the remaining 80% as the training set. The dataset is trained using the YOLOv5 model. After training, the model is tested on the garbage can system, automatically identification and classification the type of garbage is achieved and the robotic arm realizes the automatic positioning, grabbing and sorting. The experimental test results show that the error of the positioning algorithm is less than 0.8 mm, the fastest recognition speed is 2.6ms/piece, and the recognition accuracy can be maintained above 90% in the experimental environment, indicating that the algorithm has good accuracy and stability.
IM2City

This study investigated multi-modal learning as a stand-alone solution to image geo-localization problems. Based on the successful trials on the contrastive language-image pre-training (CLIP) model, we developed GEo-localization Multi-modal (GEM) models, which not only learn the visual features from input images, but also integrate the labels with corresponding geo-location context to generate textual features, which in turn are fused with the visual features for image geo-localization. We demonstrated that simply utilizing the image itself and appropriate contextualized prompts (i.e., mechanisms to integrate labels with geo-location context as textural features) is effective for global image geo-localization, which traditionally requires large amounts of geo-tagged images for image matching. Moreover, due to the integration of natural language, our GEM models are able to learn spatial proximity of geo-contextualized labels (i.e., their spatial closeness), which is often neglected by classification-based geo-localization methods. In particular, the proposed Zero-shot GEM model (i.e., geo-contextualized prompt tuning on CLIP) outperforms the state-of-the-art model - Individual Scene Networks (ISN), obtaining 4.1% and 49.5% accuracy improvements on the two benchmark datasets, IM2GPS3k and Place Plus 2.0 (i.e., 22k street view images across 56 cities worldwide), respectively. In addition, our proposed Linear-probing GEM model (i.e., CLIP's image encoder linearly trained on street view images) outperforms ISN even more significantly, obtaining 16.8% and 71.0% accuracy improvements, respectively. By exploring optimal geographic scales (e.g., city-level vs. country-level), training datasets (street view images vs. random online images), and pre-trained models (e.g., ResNet vs. CLIP for linearly probing), this research sheds light on integrating textural features with visual features for image geo-localization and beyond.
Predicting Touch Accuracy for Rectangular Targets by Using One-Dimensional Task Results

We propose a method that predicts the success rate in pointing to 2D rectangular targets by using 1D vertical-bar and horizontal-bar task results. The method can predict the success rates for more practical situations under fewer experimental conditions. This shortens the duration of experiments, thus saving costs for researchers and practitioners. We verified the method through two experiments: laboratory-based and crowdsourced ones. In the laboratory-based experiment, we found that using 1D task results to predict the success rate for 2D targets slightly decreases the prediction accuracy. In the crowdsourced experiment, this method scored better than using 2D task results. Thus, we recommend that researchers use the method properly depending on the situation.
TetraForce: A Magnetic-Based Interface Enabling Pressure Force and Shear Force Input Applied to Front and Back of a Smartphone

We propose a novel phone-case-shaped interface named TetraForce, which enables four types of force input consisting of two force directions (i.e., pressure force and shear force) and two force-applied surfaces (i.e., touch surface and back surface) in a single device. Force detection is achieved using the smartphone's built-in magnetometer (and supplementary accelerometer and gyro sensor) by estimating the displacement of a magnet attached to a 3-DoF passively movable panel at the back. We conducted a user study (N=12) to investigate the fundamental user performance by our interface and demonstrated that the input was detected as intended with a success rate of 97.4% on average for all four input types. We further conducted an ideation workshop with people who were involved in human-computer interaction (N=12) to explore possible applications of this interface, and we obtained 137 ideas for applications using individual input types and 51 possible scenarios using them in combination. Organizing these ideas reveals the advantages of each input type and suggests that our interface is useful for applications that require complex operations and that it can improve intuitiveness through elastic feedback.
LightMeUp: Back-print Illumination Paper Display with Multi-stable Visuals

Despite the drive to digitise learning, paper still holds a prominent role within educational settings. While computational devices have several advantages over paper (e.g. changing and showing content based on user interaction and needs) their prolonged or incorrect usage can hinder educational achievements. In this paper, we combine the interactivity of computational devices with paper whilst reducing the usage of technology to the minimum. To this end, we developed and evaluated a novel back-print illumination paper display called LightMeUp where different information printed on the back side of the paper becomes visible when paper is placed on an interactive display and back-illuminated with a particular colour. To develop this novel display, we first built a display simulator that enables the simulation of various spectral characteristics of the elements used in the system (i.e. light sources such as tablet computers, paper types and printing inks). By using our simulator, we designed various use-case prototypes that demonstrate the capabilities and feasibility of the proposed system. With our simulator and use-cases presented, educators and educational content designers can easily design multi-stable interactive visuals by using readily available paper, printers and touch displays.
Bidirectional remote hyperstate preparation under common quantum control using hyperentanglement

In this paper, we propose a new, to the best of our knowledge, protocol that enables two distant parties to prepare a photon hyperstate for each other encoded at the same time in both polarization and spatial-mode degrees of freedom. The bidirectional remote hyperstate preparation is demanded so that it is remotely controllable by a common supervisor. Such a task appears possible using a shared quantum channel made of five photons entangled simultaneously in the two corresponding degrees of freedom, the so-called hyperentanglement. We first design a near-deterministic scheme to produce a relevant five-photon hyperentanagled state to be served as the working nonlocal channel and then present our protocol for controlled bidirectional remote hyperstate preparation, which always is successful.
Modeling the Cognitive Activity of an Individual Based on the Mathematical Apparatus of Self-Oscillatory Quantum Mechanics

The goal of this research is to design a model of cognitive activity in the human brain. The fundamental component of such a model is the mathematical apparatus of self-oscillating quantum mechanics considered through the theory of information images/representations. Methods. This article provides a brief description of the proposed theory and highlights remarkable similarities between information images/representations and certain elementary particles, in particular—virtual Feynman particles. Following this principle, the human mind is considered as a one-dimensional potential hole with finite walls of different sizes. The internal potential barrier in this model represents the border between consciousness and subconsciousness. The authors carried out parametrization, taking into account the proposed theory. This allowed authors to lay down the foundations of the mathematical apparatus, viewing the proposed model both from the standpoint of classical quantum mechanics and through the mathematical apparatus of self-oscillatory quantum mechanics. The findings could open a way to the prediction of certain cognitive functions of the human brain. Additionally, the authors formulated the equation, which describes the state function of the information image during the cognitive activity of an individual. Conclusions. The key outcome of this research are the primary calculations of the state functions of information images/representations on the computer model, as well as the patterns of movement of the information image into and out of the human consciousness.
Research on Retinex Algorithm Combining with Attention Mechanism for Image Enhancement

Considering the high noise and chromatic aberration in the Retinex-Net image enhancement results, this paper put forward a modified Retinex-Net algorithm for weak illumination image enhancement based on the Decom-Net and Enhance-Net structures of Retinex-Net. The improved structure proposed in this paper adds the attention mechanism ECA-Net into the Decom-Net and Enhance-Net convolution layer of the original Retinex-Net structure, which can effectively reduce the problem of irrelevant background and local brightness imbalance, activate sensitive features, and improve the image’s details and brightness processing ability. Additionally, deep connected attention networks are embedded between the introduced attention modules, so that all of the attention modules can be trained jointly to improve the learning ability. Furthermore, the improved method also introduces a noise reduction loss function and a color loss function to suppress noise and to reduce image color distortion. The test results of the proposed method indicate that the image’s overall brightness can be balanced, the local areas cannot be overexposed, and more image details and color information can be retained than with other enhancement algorithms.
Stance Classification of Social Media Texts for Under-Resourced Scenarios in Social Sciences

In this work, we explore the performance of supervised stance classification methods for social media texts in under-resourced languages and using limited amounts of labeled data. In particular, we focus specifically on the possibilities and limitations of the application of classic machine learning versus deep learning in social sciences. To achieve this goal, we use a training dataset of 5.7K messages posted on Flashback Forum, a Swedish discussion platform, further supplemented with the previously published ABSAbank-Imm annotated dataset, and evaluate the performance of various model parameters and configurations to achieve the best training results given the character of the data. Our experiments indicate that classic machine learning models achieve results that are on par or even outperform those of neural networks and, thus, could be given priority when considering machine learning approaches for similar knowledge domains, tasks, and data. At the same time, the modern pre-trained language models provide useful and convenient pipelines for obtaining vectorized data representations that can be combined with classic machine learning algorithms. We discuss the implications of their use in such scenarios and outline the directions for further research.