Reviewerly/exHarmony huggingface dataset:
A Quantum Tanimoto Coefficient Fidelity for Entanglement Measurement

Fidelity plays an important role in quantum information processing, which provides a basic scale for comparing two quantum states. At present, one of the most commonly used fidelities is Uhlmann-Jozsa (U-J) fidelity. However, U-J fidelity needs to calculate the square root of the matrix, which is not trivial in the case of large or infinite density matrices. Moreover, U-J fidelity is a measure of overlap, which has limitations in some cases and cannot reflect the similarity between quantum states well. Therefore, a novel quantum fidelity measure called quantum Tanimoto coefficient (QTC) fidelity is proposed in this paper. Unlike other existing fidelities, QTC fidelity not only considers the overlap between quantum states, but also takes into account the separation between quantum states for the first time, which leads to a better performance of measure. Specifically, we discuss the properties of the proposed QTC fidelity. QTC fidelity is compared with some existing fidelities through specific examples, which reflects the effectiveness and advantages of QTC fidelity. In addition, based on the QTC fidelity, three discrimination coefficients d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> , d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> , and d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> are defined to measure the difference between quantum states. It is proved that the discrimination coefficient d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> is a true metric. Finally, we apply the proposed QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states to show their practicability.
Multi-Agent Assisted Shortest Path Planning using Monte Carlo Tree Search

In this article, we propose a Monte Carlo Tree Search (MCTS) based anytime algorithm for the multi-agent Assisted Shortest Path Planning (ASPP) problem. The computation time required by the existing, exact algorithm for the ASPP increases exponentially with the complexity of the instances. The proposed MCTS algorithm can provide a feasible solution within a close range of the optimal cost for a fraction of computation time and can reach the optimal cost given sufficient time. Detailed computational analysis is presented to illustrate the effectiveness of the MCTS algorithm.
Against Imprinting: The Photographic Image as a Source of Evidence

A photographic image is said to provide evidence of a photographed scene because it is a causal imprint of reflected light, an indexical trace of real objects and events. Though widely established in the history, theory, and philosophy of photography, this traditional imprinting model must be rejected because it relies on a “single-stage” misconception of the photographic process: the idea that a photographic image comes into existence at the time of exposure. In its place, a “multistage” account properly articulates different production stages, such as registering and rendering, that are relevant to understanding the relation between a photographic image and the photographed scene. By denying that any photographic image is a causal imprint, the multistage approach proposes a more demanding evaluation of photographic evidence. This has implications for documentary film and photojournalism, along with specialized applications such as forensics, surveillance, and face-recognition technology.
Application of the Model of Spots for Inverse Problems

This article proposes the application of a new mathematical model of spots for solving inverse problems using a learning method, which is similar to using deep learning. In general, the spots represent vague figures in abstract “information spaces” or crisp figures with a lack of information about their shapes. However, crisp figures are regarded as a special and limiting case of spots. A basic mathematical apparatus, based on L4 numbers, has been developed for the representation and processing of qualitative information of elementary spatial relations between spots. Moreover, we defined L4 vectors, L4 matrices, and mathematical operations on them. The developed apparatus can be used in Artificial Intelligence, in particular, for knowledge representation and for modeling qualitative reasoning and learning. Another application area is the solution of inverse problems by learning. For example, this can be applied to image reconstruction using ultrasound, X-ray, magnetic resonance, or radar scan data. The introduced apparatus was verified by solving problems of reconstruction of images, utilizing only qualitative data of its elementary relations with some scanning figures. This article also demonstrates the application of a spot-based inverse Radon algorithm for binary image reconstruction. In both cases, the spot-based algorithms have demonstrated an effective denoising property.
Animal Pose Estimation Based on 3D Priors

Animal pose estimation is very useful in analyzing animal behavior, monitoring animal health and moving trajectories, etc. However, occlusions, complex backgrounds, and unconstrained illumination conditions in wild-animal images often lead to large errors in pose estimation, i.e., the detected key points have large deviations from their true positions in 2D images. In this paper, we propose a method to improve animal pose estimation accuracy by exploiting 3D prior constraints. Firstly, we learn the 3D animal pose dictionary, in which each atom provides prior knowledge about 3D animal poses. Secondly, given the initially estimated 2D animal pose in the image, we represent its latent 3D pose with the learned dictionary. Finally, the representation coefficients are optimized to minimize the difference between the initially estimated 2D pose and the 2D projection of the latent 3D pose. Furthermore, we construct 2D and 3D animal pose datasets, which are used to evaluate the algorithm’s performance and learn the 3D pose dictionary, respectively. Our experimental results demonstrate that the proposed method makes good use of the 3D pose knowledge and can effectively improve 2D animal pose estimation.
General framework of a two-dimensional complex wavelet for fringe projection profilometry

Single-shot high-accuracy reconstruction of three-dimensional (3D) objects plays an essential role in many fields. Wavelet-based fringe projection profilometry (WFPP) is a popular single-shot imaging method due to its multiple-resolution analysis and non-contact property. However, the construction of mother wavelets for high-accuracy WFPP is still challenging. In this paper, a general framework of two-dimensional (2D) compact support complex wavelet employing the dual-tree concept combined with the one-dimensional (1D) asymmetric wavelet function is proposed. The constructed wavelet has an asymmetric frequency envelope in the radial direction and concentrated energy in a single quadrant of the frequency domain. A method that the constructed wavelet applied in dual-angle rotation-based 2D WFPP is presented for higher accuracy. The theoretical analysis of the proposed method is presented, and a comparison of frequency characteristics between the constructed wavelet and other popular wavelets is conducted. Computer simulations and experiments have demonstrated that the proposed method provides a better reconstruction of 3D shapes in single-shot WFPP.
Security Assessment of NTRU Against Non-Profiled SCA

NTRU was first introduced by J. Hoffstein, J. Pipher and J.H Silverman in 1998. Its security, efficiency and compactness properties have been carefully studied for more than two decades. A key encapsulation mechanism (KEM) version was even submitted to the NIST standardization competition and made it to the final round. Even though it has not been chosen to be a new standard, NTRU remains a relevant, practical and trustful post-quantum cryptographic primitive. In this paper, we investigate the side-channel resistance of the NTRU Decrypt procedure. In contrast with previous works about side-channel analysis on NTRU, we consider a weak attacker model and we focus on an implementation that incorporates some side-channel countermeasures. The attacker is assumed to be unable to mount powerful attacks by using templates or by forging malicious ciphertexts for instance. In this context, we show how a non-profiled side-channel analysis can be done against a core operation of NTRU decryption. Despite the considered countermeasures and the weak attacker model, our experiments show that the secret key can be fully retrieved with a few tens of traces.
Biometric face recognition method using graphics processing unit system

The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.
Preliminary observations on computerized tomography-powered fractal dimension-based technique to differentiate between coprolites and body fossils

This study presents a fossil signature based on fractal dimension (FD) derived from computerized tomography (CT) images to differentiate between coprolites and body fossils.Coprolites are generally studied using destructive techniques like thin section study and geochemistry.Coprolites and body fossils collected from the Triassic terrestrial Gondwana deposits of India were chosen for the study.The presented CT-powered FD-based digital signature can properly distinguish coprolites from other fossils, without losing the structural features of the samples.The present study will further enhance the digitalized fossil research.
Multi-lingual agents through multi-headed neural networks

This paper considers cooperative Multi-Agent Reinforcement Learning, focusing on emergent communication in settings where multiple pairs of independent learners interact at varying frequencies. In this context, multiple distinct and incompatible languages can emerge. When an agent encounters a speaker of an alternative language, there is a requirement for a period of adaptation before they can efficiently converse. This adaptation results in the emergence of a new language and the forgetting of the previous language. In principle, this is an example of the Catastrophic Forgetting problem which can be mitigated by enabling the agents to learn and maintain multiple languages. We take inspiration from the Continual Learning literature and equip our agents with multi-headed neural networks which enable our agents to be multi-lingual. Our method is empirically validated within a referential MNIST-based communication game and is shown to be able to maintain multiple languages where existing approaches cannot.
Analyzing and Creating Malicious URLs: A Comparative Study on Anti-Phishing Learning Games

Anti-phishing learning games are a promising approach to educate the general population about phishing, as they offer a scalable, motivational, and engaging environment for active learning.Existing games have been criticized for their limited game mechanics, which mostly require binary decisions to advance in the games, and for failing to consider the users' familiarity with online services presented in the game.In this paper, we present the evaluation of two novel game prototypes that incorporate more complex game mechanics.The first game requires the classification of URLs into several different categories, thus giving additional insights into the player's decision, while the second game addresses a different cognitive process by requiring the creation of new URLs.We compare the games with each other and with a baseline game which uses binary decisions similar to existing games.A user study with 133 participants shows, that while all three games lead to performance increases, none of the proposed game mechanics offer significant improvements over the baseline.However, we show that the analysis of the new games offers valuable insights into the players' behavior and problems while playing the games, in particular with regards to different categories of phishing URLs.Furthermore, the user study shows that the participants were significantly better in classifying URLs of services they know than those they do not know.These results indicate, that the distinction between known and unknown services in phishing tests is important to gain a better understanding of the test results, and should be considered when designing and reproducing studies.
SISTEM DETEKSI PEMAKAIAN MASKER PADA WAJAH SECARA REAL TIME MENGGUNAKAN FRAMEWORK TENSORFLOW DAN LIBRARY OPENCV

At the end of December 2019, there was a mysterious virus that attacked the city of Wuhan in China. Which at the beginning of 2020 the new virus was a new type, namely (SARS-CoV-2) and the disease was called Coronavirus disease 19 (COVID-19). The World Health Organization (WHO) provides instructions to maintain a minimum distance of 1 meter, always wash hands with soap, and always use a mask when leaving the house. The purpose of this writing is to create a program to detect wearing masks on the face, which makes it easier for users to detect who is wearing a mask or not. The method used in this writing uses the SDLC model or System Data Life Cycle by carrying out five stages namely, planning, analysis, design, implementation, and testing. The design uses flowcharts and navigation structures as well as two UML diagrams, namely use case diagrams and activity diagrams. Then for making the program using Python as a programming language, Tensorflow as a framework, Keras and Opencv as libraries, and Visual Studio Code as a text editor. From the results of trials that have been carried out using three different devices and with different specifications, this system can detect mask objects very well and this detection system can also detect more than one person wearing a mask, or not wearing a mask.
Contrastive learning for unsupervised medical image clustering and reconstruction

The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challenges in exploiting medical imaging information in a precision medicine paradigm, where in principle dense patient-specific data can be employed to formulate individual predictions and/or stratify patients into finer-grained groups which may follow more homogeneous trajectories and therefore empower clinical trials. In order to efficiently explore the effective degrees of freedom underlying variability in medical images in an unsupervised manner, in this work we propose an unsupervised autoencoder framework which is augmented with a contrastive loss to encourage high separability in the latent space. The model is validated on (medical) benchmark datasets. As cluster labels are assigned to each example according to cluster assignments, we compare performance with a supervised transfer learning baseline. Our methods achieves similar performance to the supervised architecture, indicating that separation in the latent space reproduces expert medical observer-assigned labels. The proposed method could be beneficial for patient stratification, exploring new subdivision of larger classes or pathological continua or, due to its sampling abilities in a variation setting, data augmentation in medical image processing.
Standard model and predictions for the Higgs boson

This article describes the electroweak interactions of the Standard Model (SM) with Brout-Englert-Higgs (BEH) mechanism, the appearance and properties of the Higgs boson. Braut-Engler-Higgs mechanism plays an important role in the Standard Model, which introduce a scalar Higgs field with a non-zero vacuum expectation results from spontaneous symmetry breaking. So, due to the interaction with this field, elementary particles become massive. Here arise electrically neutral quanta associated with the Higgs field, so called Higgs boson, in the same way that there is a quanta associated with the electromagnetic field, i.e. photons. The strong part of the Standard Model, Quantum Chromodynamics, quark mixing Cabibbo–Kobayashi–Maskawa matrix, physics of the neutrino and many other issues were not mentioned in the article. Brout-Englert-Higgs mechanism and associated Higgs boson are important parts of the Standard Model of elementary particles. In this paper we present the full Lagrangian of the Standard Model and a set of theoretical predictions about the Higgs boson before its detection, as well as the current state of the Higgs boson problem. Keywords: Standard Model, High Energy, Large Hadron Collider, Higgs boson, Higgs mechanism, Spontaneous symmetry breaking.
Phishing awareness and education – When to best remind?

The use of security awareness and education programmes is very common in organisations.But how effective are they over time?Some initial research on this question is, among others, the extensive study of Reinheimer et al. [74] that measured effectiveness at several time intervals.Their research found still significantly better results than before the awareness program after four months, but no longer after six months.This left open a two months interval for the reminder.The contribution of our paper is to study whether the reminder should be closer to four or six months.Thus, we measured effectiveness after five months.With still significant better results than before the programme after five months, we conclude that it is recommended to remind users more towards six months rather than already after five.However, we kindly invite the community to conduct more long-term studies, in different contexts, to confirm these findings.
Ultrabright polarization-entangled photon pair source for frequency-multiplexed quantum communication in free-space

The distribution of entanglement via satellite links will drastically extend the reach of quantum networks. Highly efficient entangled photon sources are an essential requirement towards overcoming high channel loss and achieving practical transmission rates in long-distance satellite downlinks. Here we report on an ultrabright entangled photon source that is optimized for long-distance free-space transmission. It operates in a wavelength range that is efficiently detected with space-ready single photon avalanche diodes (Si-SPADs), and readily provides pair emission rates that exceed the detector bandwidth (i.e., the temporal resolution). To overcome this limitation, we demultiplex the photon flux into wavelength channels that can be handled by current single photon detector technology. This is achieved efficiently by using the spectral correlations due to hyper-entanglement in polarization and frequency as an auxiliary resource. Combined with recent demonstrations of space-proof source prototypes, these results pave the way to a broadband long-distance entanglement distribution network based on satellites.
Socio-cognitive Optimization of Time-delay Control Problems using Agent-based Metaheuristics

Following the introduction of the socio-cognitive caste-based algorithms into the classic evolutionary metaheuristics, in this paper we focus on similar task regarding agent-based universal optimization methods. We tackle EMAS and DE algorithms and enrich them also with TOPSIS-inspired mechanism. Besides giving the details of the methods and the background, we present preliminary results after applying those techniques to solving the problem of optimization of time-delay system model.
Some Trends in Vocabulary Research: A Discussion of Three Papers Presented at the JALT Vocabulary SIG

The Japanese context is a particularly rich area for a wide range of vocabulary research. I was therefore quite honored (and humbled) to be asked to serve as one of the discussants for the 2022 JALT Vocabulary SIG. The morning session of the Fall 2022 Vocabulary SIG included three papers that represent a range of topics that are common in vocabulary research. The first study (Eguchi, 2022) represents recent research on multivariate approaches to lexical sophistication, and highlights the importance of index selection, statistical choices, and the consideration of register when interpreting results. The second study (Al-Hoorie et al., 2022) highlights the wealth of vocabulary learning research that has been conducted in the last 30 years through a scoping review of research syntheses. The third study (Canning et al., 2022) represents an evaluation of one aspect of a substantive validity argument for a well-known receptive vocabulary knowledge test. The study highlights the idea that validity arguments are multi-faceted and that the evaluation of the validity of an assessment is an iterative, context-specific process, and is not represented by a one-time, one-size-fits-all study. Below, a brief summary of each study is provided, highlighting the particularly admirable qualities of each followed by suggestions for future research.