Reviewerly/exHarmony huggingface dataset:
How Different Text-Preprocessing Techniques using the Bert Model Affect the Gender Profiling of Authors

Forensic author profiling plays an important role in indicating possible profiles for suspects. Among the many automated solutions recently proposed for author profiling, transfer learning outperforms many other state-of-the-art techniques in natural language processing. Nevertheless, the sophisticated technique has yet to be fully exploited for author profiling. At the same time, whereas current methods of author profiling, all largely based on features engineering, have spawned significant variation in each model used, transfer learning usually requires a preprocessed text to be fed into the model. We reviewed multiple references in the literature and determined the most common preprocessing techniques associated with authors' genders profiling. Considering the variations in potential preprocessing techniques, we conducted an experimental study that involved applying five such techniques to measure each technique’s effect while using the BERT model, chosen for being one of the most-used stock pretrained models. We used the Hugging face transformer library to implement the code for each preprocessing case. In our five experiments, we found that BERT achieves the best accuracy in predicting the gender of the author when no preprocessing technique is applied. Our best case achieved 86.67% accuracy in predicting the gender of authors.
Automating a Dehazing System by Self-Calibrating on Haze Conditions

Existing image dehazing algorithms typically rely on a two-stage procedure. The medium transmittance and lightness are estimated in the first stage, and the scene radiance is recovered in the second by applying the simplified Koschmieder model. However, this type of unconstrained dehazing is only applicable to hazy images, and leads to untoward artifacts in haze-free images. Moreover, no algorithm that can automatically detect the haze density and perform dehazing on an arbitrary image has been reported in the literature to date. Therefore, this paper presents an automated dehazing system capable of producing satisfactory results regardless of the presence of haze. In the proposed system, the input image simultaneously undergoes multiscale fusion-based dehazing and haze-density-estimating processes. A subsequent image blending step then judiciously combines the dehazed result with the original input based on the estimated haze density. Finally, tone remapping post-processes the blended result to satisfactorily restore the scene radiance quality. The self-calibration capability on haze conditions lies in using haze density estimate to jointly guide image blending and tone remapping processes. We performed extensive experiments to demonstrate the superiority of the proposed system over state-of-the-art benchmark methods.
Non-Markovianity criteria for mixtures of noninvertible Pauli dynamical maps

Abstract We analyze the connections between the non-Markovianity degree of the most general phase-damping qubit maps and their legitimate mixtures. Using the results for image non-increasing dynamical maps, we formulate the necessary and sufficient conditions for the Pauli maps to satisfy specific divisibility criteria. Next, we examine how the non-Markovianity properties for (in general noninvertible) Pauli dynamical maps influence the properties of their convex combinations. Our results are illustrated with instructive examples. For P-divisible maps, we propose a legitimate time-local generator whose all decoherence rates are temporarily infinite.
Quantum annealing with trigger Hamiltonians: Application to 2-satisfiability and nonstoquastic problems

We study the performance of quantum annealing for two sets of problems, namely, 2-satisfiability (2-SAT) problems represented by Ising-type Hamiltonians and nonstoquastic problems which are obtained by adding extra couplings to the 2-SAT problem Hamiltonians. In addition, we add to the transverse Ising-type Hamiltonian used for quantum annealing a third term, the trigger Hamiltonian with ferromagnetic or antiferromagnetic couplings, which vanishes at the beginning and end of the annealing process. We also analyze some problem instances using the energy spectrum, average energy or overlap of the state during the evolution with the instantaneous low-lying eigenstates of the Hamiltonian, and identify some nonadiabatic mechanisms which can enhance the performance of quantum annealing.
Design of quantum optical experiments with logic artificial intelligence

Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking their satisfiability (SAT). In contrast to machine learning approaches where the results can be approximations or local minima, Logic AI delivers formal and mathematically exact solutions to those problems. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI significantly improves the resolution of this problem, paving the path to developing more formal-based approaches in the context of quantum physics experiments.
Evaluation of Instance-Based Learning and Q-Learning Algorithms in Dynamic Environments

Reinforcement learning is an unsupervised learning algorithm, where learning is based upon feedback from the environment.Prior research has proposed cognitive (e.g., Instance-based Learning or IBL) and statistical (Q-learning) reinforcement learning algorithms.However, an evaluation of these algorithms in a single dynamic environment has not been explored.In this paper, a comparison between the statistical Qlearning algorithm and the cognitive IBL algorithm is presented.A well-known environment, "Frozen Lake," is used to train, generalize, and scale Q-learning and IBL algorithms.For generalizing, the Q-learning and IBL agents were trained on one version of the Frozen Lake and tested on a permuted version of the same environment.For scaling, the two algorithms were tested on a larger version of the Frozen Lake environment.Results revealed that the IBL algorithm used less training time and generalized better to different environment variants.The IBL algorithm was also able to show scalability by retaining its superior performance in the larger environment.These results indicate that the IBL algorithm could be proposed as an alternative to the standard reinforcement learning algorithms based on dynamic programming such as Q-learning.The inclusion of human factors (such as memory) in the IBL algorithm makes it suitable for robust learning in complex and dynamic environments.
Optimal design and performance evaluation of free-space quantum key distribution systems

Abstract Free-space ground-to-ground links will be an integral part of future quantum communication networks. The implementation of free-space and fiber links in daylight inter-modal configurations is however still hard to achieve, due to the impact of atmospheric turbulence, which strongly decreases the coupling efficiency into the fiber. In this work, we present a comprehensive model of the performance of a free-space ground-to-ground quantum key distribution (QKD) system based on the efficient-BB84 protocol with active decoy states. Our model takes into account the atmospheric channel contribution, the transmitter and receiver telescope design constraints, the parameters of the quantum source and detectors, and the finite-key analysis to produce a set of requirements and optimal design choices for a QKD system operating under specific channel conditions. The channel attenuation is calculated considering all effects deriving from the atmospheric propagation (absorption, beam broadening, beam wandering, scintillation, and wavefront distortions), as well as the effect of fiber-coupling in the presence of a partial adaptive optics correction with finite control bandwidth. We find that the channel fluctuation statistics must be considered to correctly estimate the effect of the saturation rate of the single-photon detectors, which may otherwise lead to an overestimation of the secret key rate. We further present strategies to minimize the impact of diffuse atmospheric background in daylight operation by means of spectral and temporal filtering.
Co-clustering of Time-Dependent Data via the Shape Invariant Model

Multivariate time-dependent data, where multiple features are observed over time for a set of individuals, are increasingly widespread in many application domains. To model these data, we need to account for relations among both time instants and variables and, at the same time, for subject heterogeneity. We propose a new co-clustering methodology for grouping individuals and variables simultaneously, designed to handle both functional and longitudinal data. Our approach borrows some concepts from the curve registration framework by embedding the shape invariant model in the latent block model, estimated via a suitable modification of the SEM-Gibbs algorithm. The resulting procedure allows for several user-defined specifications of the notion of cluster that can be chosen on substantive grounds and provides parsimonious summaries of complex time-dependent data by partitioning data matrices into homogeneous blocks. Along with the explicit modelling of time evolution, these aspects allow for an easy interpretation of the clusters, from which also low-dimensional settings may benefit.
Field (In)dependence Flexibility Following a Virtual Immersion Is Associated With Cybersickness and Sense of Presence

Field dependence–independence (FDI) is a psychological construct determining an individual’s approach of the perception–cognition coupling. In virtual reality (VR) context, several studies suggest that an individual’s perceptive style is susceptible to shift toward a more FI mode through down-weighting of conflicting visual cues. The present study proposes to investigate the potential flexible nature of FDI following a virtual immersion and to assess if this flexibility might be associated with the subjective experience of VR. 86 participants explored a real-world–like virtual environment for approximately 10 min. FDI levels were measured before and after the VR exposure using the rod-and-frame test. Their subjective experience of VR was measured a posteriori (cybersickness and sense of presence) and used in order to build two experimental groups via a cluster analysis. The results showed that only participants with a poor subjective experience of VR (i.e., a low level of sense of presence associated with a high level of cybersickness) significantly shifted to a more FI mode, which is discussed as a sensory re-weighting mechanism. Pragmatical applications are discussed, and future studies are outlined, based on the conclusion that FDI might be more flexible than we thought, which could shed light on the psychophysiology of VR.
Prime factorization using quantum variational imaginary time evolution

The road to computing on quantum devices has been accelerated by the promises that come from using Shor's algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as [Formula: see text], where n is the bit-length of the number to be factorized and d is the depth of the circuit. We use a single layer of entangling gates to factorize 36 numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method's performance by implementing it on the IBMQ Lima hardware to factorize 55, 65, 77 and 91 which are greater than the largest number (21) to have been factorized on IBMQ hardware.
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy

Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNNs has a significant impact on model utility in the context of private deep learning, whereas its effect is largely unexplored in previous studies. In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS. To integrate private learning with architecture search, a DP-aware approach is introduced for training candidate models composed on a delicately defined novel search space. We empirically certify the effectiveness of the proposed framework. The searched model DPNASNet achieves state-of-the-art privacy/utility trade-offs, e.g., for the privacy budget of (epsilon, delta)=(3, 1e-5), our model obtains test accuracy of 98.57% on MNIST, 88.09% on FashionMNIST, and 68.33% on CIFAR-10. Furthermore, by studying the generated architectures, we provide several intriguing findings of designing private-learning-friendly DNNs, which can shed new light on model design for deep learning with differential privacy.
Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.
A Novel Artificial Jellyfish Search Algorithm Improved with Detailed Local Search Strategy

Metaheuristic algorithms are frequently preferred algorithms for optimization problems in many different fields. The number of these algorithms inspired by natural phenomena is increasing day by day. Artificial Jellyfish Search Algorithm, inspired by the behavior of jellyfish in the ocean, is one of the new metaheuristic algorithms that have been proposed recently. In this study, two different search strategies were used together with a modification made in the active motion of the Artificial Jellyfish Search Algorithm, in the local search section, and the local search capability of the algorithm was thus developed. With this modification, it is aimed to preserve population diversity for a longer time. The proposed algorithm has been tested for 10, 30, and 50 dimensions on single-objective CEC2017 benchmark functions. The results obtained were compared with the standard algorithm and algorithms selected from the literature and interpreted with the help of statistical tests. It has been determined that the proposed algorithm outperforms the standard algorithm and becomes competitive with other algorithms in the literature thanks to the modification made.
Pengembangan Media E-Booklet Pembelajaran Berbasis Flipbook Maker pada Materi Identifikasi Karir Siswa

The purpose of this study was to develop an e-booklet media based on flipbook maker on career identification material for class XI students at SMKS YP 17 Cilegon. The method used is the R&D method. This development research uses the steps developed by Borg & Gall. There are three subjects in this study, namely material experts, media experts, and 30 students of class XI Marketing Department of Marketing. The instrument used is a validation sheet that is tested on media experts, material experts, and student responses. Based on the results of research that has been carried out at SMKS YP 17 Cilegon, it can be concluded that the development of flipbook maker-based e-booklet media has an average value of 89.28% which is categorized as very feasible with details of the percentage of media experts of 90.6%, material experts of 93.7%, and student responses of 83.56% so that the media is included in the appropriate category.
A Unifying Framework for $n$-Dimensional Quasi-Conformal Mappings

With the advancement of computer technology, there is a surge of interest in effective mapping methods for objects in higher-dimensional spaces.To establish a one-to-one correspondence between objects, higher-dimensional quasi-conformal theory can be utilized for ensuring the bijectivity of the mappings.In addition, it is often desirable for the mappings to satisfy certain prescribed geometric constraints and possess low distortion in conformality or volume.In this work, we develop a unifying framework for computing n-dimensional quasi-conformal mappings.More specifically, we propose a variational model that integrates quasi-conformal distortion, volumetric distortion, landmark correspondence, intensity mismatch and volume prior information to handle a large variety of deformation problems.We further prove the existence of a minimizer for the proposed model and devise efficient numerical methods to solve the optimization problem.We demonstrate the effectiveness of the proposed framework using various experiments in two-and three-dimensions, with applications to medical image registration, adaptive remeshing and shape modeling.
Neuro-Symbolic Reinforcement Learning with First-Order Logic

Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neurosymbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network.The method is first to extract first-order logical facts from text observation and external word meaning network (ConceptNet), then train a policy in the network with directly interpretable logical operators.Our experimental results show RL training with the proposed method converges significantly faster than other state-of-the-art neuro-symbolic methods in a TextWorld benchmark.
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization

While much research has been done in textto-image synthesis, little work has been done to explore the usage of linguistic structure of the input text.Such information is even more important for story visualization since its inputs have an explicit narrative structure that needs to be translated into an image sequence (or visual story).Prior work in this domain has shown that there is ample room for improvement in the generated image sequence in terms of visual quality, consistency and relevance.In this paper, we first explore the use of constituency parse trees using a Transformer-based recurrent architecture for encoding structured input.Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story.Third, we also incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images within a dual learning setup.We show that off-theshelf dense-captioning models trained on Visual Genome can improve the spatial structure of images from a different target domain without needing fine-tuning.We train the model end-to-end using intra-story contrastive loss (between words and image sub-regions) and show significant improvements in visual quality.Finally, we provide an analysis of the linguistic and visuo-spatial information. 11 Code and data: https://github.com/adymaharana/VLCStoryGan. Ground Truth DuCo Captions VLC Petty asks whether it is because of cookies.Eddy denies with his hands.Petty hands her cookies to Eddy.Petty gives her cookies to Loopy and Crong.Crong sighs.
The Coming of Age of Interpretable and Explainable Machine Learning Models

Machine learning-based systems are now part of a wide array of real-world applications seamlessly embedded in the social realm.In the wake of this realisation, strict legal regulations for these systems are currently being developed, addressing some of the risks they may pose.This is the coming of age of the interpretability and explainability problems in machine learning-based data analysis, which can no longer be seen just as an academic research problem.In this tutorial, associated to ESANN 2021 special session on "Interpretable Models in Machine Learning and Explainable Artificial Intelligence", we discuss explainable and interpretable machine learning as post-hoc and ante-hoc strategies to address these problems and highlight several aspects related to them, including their assessment.The contributions accepted for the session are then presented in this context.
LoopBlocks: Design and Preliminary Evaluation of an Accessible Tangible Musical Step Sequencer

This paper presents the design and preliminary evaluation of an Accessible Digital Musical Instrument (ADMI) in the form of a tangible wooden step sequencer that uses photoresistors and wooden blocks to trigger musical events.Furthermore, the paper presents a short overview of design criteria for ADMIs based on literature and first insights of an ongoing qualitative interview study with German Special Educational Needs (SEN) teachers conducted by the first author.The preliminary evaluation is realized by a reflection on the mentioned criteria.The instrument was designed as a starting point for a participatory design process in music education settings.The software is programmed in Pure Data and running on a Raspberry Pi computer that fits inside the body of the instrument.While most similar developments focus on professional performance and complex interactions, LoopBlocks focuses on accessibility and Special Educational Needs settings.The main goal is to reduce the cognitive load needed to play music by providing a clear and constrained interaction, thus reducing intellectual and technical barriers to active music making.